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of false alarm</title> <meta name="description" content="Search results for: probability of false alarm"> <meta name="keywords" content="probability of false alarm"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="probability of false alarm"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1681</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: probability of false alarm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1681</span> A Method for False Alarm Recognition Based on Multi-Classification Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weiwei%20Cui">Weiwei Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Dejian%20Lin"> Dejian Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Leigang%20Zhang"> Leigang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yao%20Wang"> Yao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zheng%20Sun"> Zheng Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Lianfeng%20Li"> Lianfeng Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=false%20alarm" title="false alarm">false alarm</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=BIT" title=" BIT"> BIT</a> </p> <a href="https://publications.waset.org/abstracts/87531/a-method-for-false-alarm-recognition-based-on-multi-classification-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87531.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">155</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1680</span> A Study on the False Alarm Rates of MEWMA and MCUSUM Control Charts When the Parameters Are Estimated</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Farouk%20Abbas">Umar Farouk Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Danjuma%20Mustapha"> Danjuma Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamisu%20Idi"> Hamisu Idi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is now a known fact that quality is an important issue in manufacturing industries. A control chart is an integrated and powerful tool in statistical process control (SPC). The mean µ and standard deviation σ parameters are estimated. In general, the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) are used in the detection of small shifts in joint monitoring of several correlated variables; the charts used information from past data which makes them sensitive to small shifts. The aim of the paper is to compare the performance of Shewhart xbar, MEWMA, and MCUSUM control charts in terms of their false rates when parameters are estimated with autocorrelation. A simulation was conducted in R software to generate the average run length (ARL) values of each of the charts. After the analysis, the results show that a comparison of the false alarm rates of the charts shows that MEWMA chart has lower false alarm rates than the MCUSUM chart at various levels of parameter estimated to the number of ARL0 (in control) values. Also noticed was that the sample size has an advert effect on the false alarm of the control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=MCUSUM%20chart" title=" MCUSUM chart"> MCUSUM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=MEWMA%20chart" title=" MEWMA chart"> MEWMA chart</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm%20rate" title=" false alarm rate"> false alarm rate</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/106504/a-study-on-the-false-alarm-rates-of-mewma-and-mcusum-control-charts-when-the-parameters-are-estimated" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1679</span> An Improved Two-dimensional Ordered Statistical Constant False Alarm Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weihao%20Wang">Weihao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhulin%20Zong"> Zhulin Zong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=two-dimensional" title="two-dimensional">two-dimensional</a>, <a href="https://publications.waset.org/abstracts/search?q=ordered%20statistical" title=" ordered statistical"> ordered statistical</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20false%20alarm" title=" constant false alarm"> constant false alarm</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20target%20signals" title=" weak target signals"> weak target signals</a> </p> <a href="https://publications.waset.org/abstracts/163351/an-improved-two-dimensional-ordered-statistical-constant-false-alarm-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163351.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1678</span> Test of Moisture Sensor Activation Speed</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Parkova">I. Parkova</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Vali%C5%A1evskis"> A. Vališevskis</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Vi%C4%BCumsone"> A. Viļumsone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nocturnal enuresis or bed-wetting is intermittent incontinence during sleep of children after age 5 that may precipitate wide range of behavioural and developmental problems. One of the non-pharmacological treatment methods is the use of a bed-wetting alarm system. In order to improve comfort conditions of nocturnal enuresis alarm system, modular moisture sensor should be replaced by a textile sensor. In this study behaviour and moisture detection speed of woven and sewn sensors were compared by analysing change in electrical resistance after solution (salt water) was dripped on sensor samples. Material of samples has different structure and yarn location, which affects solution detection rate. Sensor system circuit was designed and two sensor tests were performed: system activation test and false alarm test to determine the sensitivity of the system and activation threshold. Sewn sensor had better result in system’s activation test – faster reaction, but woven sensor had better result in system’s false alarm test – it was less sensitive to perspiration simulation. After experiments it was found that the optimum switching threshold is 3V in case of 5V input voltage, which provides protection against false alarms, for example – during intensive sweating. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conductive%20yarns" title="conductive yarns">conductive yarns</a>, <a href="https://publications.waset.org/abstracts/search?q=moisture%20textile%20sensor" title=" moisture textile sensor"> moisture textile sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=industry" title=" industry"> industry</a>, <a href="https://publications.waset.org/abstracts/search?q=material" title=" material"> material</a> </p> <a href="https://publications.waset.org/abstracts/7649/test-of-moisture-sensor-activation-speed" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7649.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">246</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1677</span> Method of False Alarm Rate Control for Cyclic Redundancy Check-Aided List Decoding of Polar Codes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dmitry%20Dikarev">Dmitry Dikarev</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajit%20Nimbalker"> Ajit Nimbalker</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexei%20Davydov"> Alexei Davydov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Polar coding is a novel example of error correcting codes, which can achieve Shannon limit at block length N→∞ with log-linear complexity. Active research is being carried to adopt this theoretical concept for using in practical applications such as 5th generation wireless communication systems. Cyclic redundancy check (CRC) error detection code is broadly used in conjunction with successive cancellation list (SCL) decoding algorithm to improve finite-length polar code performance. However, there are two issues: increase of code block payload overhead by CRC bits and decrease of CRC error-detection capability. This paper proposes a method to control CRC overhead and false alarm rate of polar decoding. As shown in the computer simulations results, the proposed method provides the ability to use any set of CRC polynomials with any list size while maintaining the desired level of false alarm rate. This level of flexibility allows using polar codes in 5G New Radio standard. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=5G%20New%20Radio" title="5G New Radio">5G New Radio</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20coding" title=" channel coding"> channel coding</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20redundancy%20check" title=" cyclic redundancy check"> cyclic redundancy check</a>, <a href="https://publications.waset.org/abstracts/search?q=list%20decoding" title=" list decoding"> list decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=polar%20codes" title=" polar codes"> polar codes</a> </p> <a href="https://publications.waset.org/abstracts/85145/method-of-false-alarm-rate-control-for-cyclic-redundancy-check-aided-list-decoding-of-polar-codes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85145.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">238</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1676</span> Performance of Nakagami Fading Channel over Energy Detection Based Spectrum Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ranjeeth">M. Ranjeeth</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Anuradha"> S. Anuradha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spectrum sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. In this paper we compare the theoretical results of detection probability of different fading environments like Rayleigh, Rician, Nakagami-m fading channels with the simulation results using energy detection based spectrum sensing. The numerical results are plotted as P_f Vs P_d for different SNR values, fading parameters. It is observed that Nakagami fading channel performance is better than other fading channels by using energy detection in spectrum sensing. A MATLAB simulation test bench has been implemented to know the performance of energy detection in different fading channel environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title="spectrum sensing">spectrum sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20detection" title=" energy detection"> energy detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fading%20channels" title=" fading channels"> fading channels</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20detection" title=" probability of detection"> probability of detection</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20false%20alarm" title=" probability of false alarm"> probability of false alarm</a> </p> <a href="https://publications.waset.org/abstracts/15800/performance-of-nakagami-fading-channel-over-energy-detection-based-spectrum-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15800.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">532</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1675</span> Fast Accurate Detection of Frequency Jumps Using Kalman Filter with Non Linear Improvements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20E.%20Mohamed">Mahmoud E. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20F.%20Shalash"> Ahmed F. Shalash</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20A.%20Kamal"> Hanan A. Kamal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, Despite the tradeoff between the noise level and the speed of the detection. In this paper, An improvement is introduced in the Kalman filter, Through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, The effect on the response to false alarms is also presented and false alarm rate show improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title="Kalman filter">Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation" title=" innovation"> innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20detection" title=" false detection"> false detection</a>, <a href="https://publications.waset.org/abstracts/search?q=improvement" title=" improvement "> improvement </a> </p> <a href="https://publications.waset.org/abstracts/7978/fast-accurate-detection-of-frequency-jumps-using-kalman-filter-with-non-linear-improvements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7978.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">602</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1674</span> Filtering Intrusion Detection Alarms Using Ant Clustering Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghodhbani%20Salah">Ghodhbani Salah</a>, <a href="https://publications.waset.org/abstracts/search?q=Jemili%20Farah"> Jemili Farah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title="intrusion detection system">intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=alarm%20filtering" title=" alarm filtering"> alarm filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=ANT%20class" title=" ANT class"> ANT class</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20clustering" title=" ant clustering"> ant clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=intruders%E2%80%99%20behaviors" title=" intruders’ behaviors"> intruders’ behaviors</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarms" title=" false alarms"> false alarms</a> </p> <a href="https://publications.waset.org/abstracts/23716/filtering-intrusion-detection-alarms-using-ant-clustering-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1673</span> Linear Frequency Modulation-Frequency Shift Keying Radar with Compressive Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ho%20Jeong%20Jin">Ho Jeong Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang%20Won%20Seo"> Chang Won Seo</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon%20Sik%20Cho"> Choon Sik Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Bong%20Yong%20Choi"> Bong Yong Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Kyun%20Na"> Kwang Kyun Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20Rok%20Lee"> Sang Rok Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a radar signal processing technique using the LFM-FSK (Linear Frequency Modulation-Frequency Shift Keying) is proposed for reducing the false alarm rate based on the compressive sensing. The LFM-FSK method combines FMCW (Frequency Modulation Continuous Wave) signal with FSK (Frequency Shift Keying). This shows an advantage which can suppress the ghost phenomenon without the complicated CFAR (Constant False Alarm Rate) algorithm. Moreover, the parametric sparse algorithm applying the compressive sensing that restores signals efficiently with respect to the incomplete data samples is also integrated, leading to reducing the burden of ADC in the receiver of radars. 24 GHz FMCW signal is applied and tested in the real environment with FSK modulated data for verifying the proposed algorithm along with the compressive sensing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressive%20sensing" title="compressive sensing">compressive sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=LFM-FSK%20radar" title=" LFM-FSK radar"> LFM-FSK radar</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20signal%20processing" title=" radar signal processing"> radar signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20algorithm" title=" sparse algorithm"> sparse algorithm</a> </p> <a href="https://publications.waset.org/abstracts/51309/linear-frequency-modulation-frequency-shift-keying-radar-with-compressive-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51309.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1672</span> Adaptive CFAR Analysis for Non-Gaussian Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bouchemha%20Amel">Bouchemha Amel</a>, <a href="https://publications.waset.org/abstracts/search?q=Chachoui%20Takieddine"> Chachoui Takieddine</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Maalem"> H. Maalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic detection of targets in a modern communication system RADAR is based primarily on the concept of adaptive CFAR detector. To have an effective detection, we must minimize the influence of disturbances due to the clutter. The detection algorithm adapts the CFAR detection threshold which is proportional to the average power of the clutter, maintaining a constant probability of false alarm. In this article, we analyze the performance of two variants of adaptive algorithms CA-CFAR and OS-CFAR and we compare the thresholds of these detectors in the marine environment (no-Gaussian) with a Weibull distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CFAR" title="CFAR">CFAR</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold" title=" threshold"> threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=clutter" title=" clutter"> clutter</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution" title=" distribution"> distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Weibull" title=" Weibull"> Weibull</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/21359/adaptive-cfar-analysis-for-non-gaussian-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">589</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1671</span> Performance Analysis of the Time-Based and Periodogram-Based Energy Detector for Spectrum Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadaf%20Nawaz">Sadaf Nawaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnan%20Ahmed%20Khan"> Adnan Ahmed Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20Mahmood"> Asad Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Chaudhary%20Farrukh%20Javed"> Chaudhary Farrukh Javed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classically, an energy detector is implemented in time domain (TD). However, frequency domain (FD) based energy detector has demonstrated an improved performance. This paper presents a comparison between the two approaches as to analyze their pros and cons. A detailed performance analysis of the classical TD energy-detector and the periodogram based detector is performed. Exact and approximate mathematical expressions for probability of false alarm (Pf) and probability of detection (Pd) are derived for both approaches. The derived expressions naturally lead to an analytical as well as intuitive reasoning for the improved performance of (Pf) and (Pd) in different scenarios. Our analysis suggests the dependence improvement on buffer sizes. Pf is improved in FD, whereas Pd is enhanced in TD based energy detectors. Finally, Monte Carlo simulations results demonstrate the analysis reached by the derived expressions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20detector" title=" energy detector"> energy detector</a>, <a href="https://publications.waset.org/abstracts/search?q=periodogram" title=" periodogram"> periodogram</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title=" spectrum sensing"> spectrum sensing</a> </p> <a href="https://publications.waset.org/abstracts/63028/performance-analysis-of-the-time-based-and-periodogram-based-energy-detector-for-spectrum-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63028.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1670</span> Ship Detection Requirements Analysis for Different Sea States: Validation on Real SAR Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaime%20Mart%C3%ADn-de-Nicol%C3%A1s">Jaime Martín-de-Nicolás</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Mata-Moya"> David Mata-Moya</a>, <a href="https://publications.waset.org/abstracts/search?q=Nerea%20del-Rey-Maestre"> Nerea del-Rey-Maestre</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20G%C3%B3mez-del-Hoyo"> Pedro Gómez-del-Hoyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa-Pilar%20Jarabo-Amores"> María-Pilar Jarabo-Amores</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions and sea state can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. A fast CFAR ship detector based on a robust statistical modeling of sea clutter with respect to sea states in SAR images is used. In this paper, the minimum SNR required to obtain a given detection probability with a given false alarm rate for any sea state is determined. A Gaussian target model using real SAR data is considered. Results show that SNR does not depend heavily on the class considered. Provided there is some variation in the backscattering of targets in SAR imagery, the detection probability is limited and a post-processing stage based on morphology would be suitable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SAR" title="SAR">SAR</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20gamma%20distribution" title=" generalized gamma distribution"> generalized gamma distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=detection%20curves" title=" detection curves"> detection curves</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20detection" title=" radar detection"> radar detection</a> </p> <a href="https://publications.waset.org/abstracts/52278/ship-detection-requirements-analysis-for-different-sea-states-validation-on-real-sar-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52278.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1669</span> Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Zavid%20Parvez">Mohammad Zavid Parvez</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoranjan%20Paul"> Manoranjan Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Epilepsy" title="Epilepsy">Epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20correlation" title=" phase correlation"> phase correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=fluctuation" title=" fluctuation"> fluctuation</a>, <a href="https://publications.waset.org/abstracts/search?q=deviation." title=" deviation. "> deviation. </a> </p> <a href="https://publications.waset.org/abstracts/37585/epileptic-seizure-prediction-by-exploiting-signal-transitions-phenomena" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37585.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">467</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1668</span> Energy Detection Based Sensing and Primary User Traffic Classification for Cognitive Radio</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Urvee%20B.%20Trivedi">Urvee B. Trivedi</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20D.%20Dalal"> U. D. Dalal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio%20%28CR%29" title="cognitive radio (CR)">cognitive radio (CR)</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20detection%20%28PD%29" title=" probability of detection (PD)"> probability of detection (PD)</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20of%20false%20alarm%20%28PF%29" title=" probability of false alarm (PF)"> probability of false alarm (PF)</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20user%20%28PU%29" title=" primary user (PU)"> primary user (PU)</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20user%20%28SU%29" title=" secondary user (SU)"> secondary user (SU)</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20Fourier%20transform%20%28FFT%29" title=" fast Fourier transform (FFT)"> fast Fourier transform (FFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20to%20noise%20ratio%20%28SNR%29" title=" signal to noise ratio (SNR)"> signal to noise ratio (SNR)</a> </p> <a href="https://publications.waset.org/abstracts/45944/energy-detection-based-sensing-and-primary-user-traffic-classification-for-cognitive-radio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45944.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1667</span> Genetic Algorithm for In-Theatre Military Logistics Search-and-Delivery Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean%20Berger">Jean Berger</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Barkaoui"> Mohamed Barkaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=search%20path%20planning" title="search path planning">search path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm" title=" false alarm"> false alarm</a>, <a href="https://publications.waset.org/abstracts/search?q=search-and-delivery" title=" search-and-delivery"> search-and-delivery</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/2263/genetic-algorithm-for-in-theatre-military-logistics-search-and-delivery-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2263.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1666</span> SIP Flooding Attacks Detection and Prevention Using Shannon, Renyi and Tsallis Entropy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neda%20Seyyedi">Neda Seyyedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Berangi"> Reza Berangi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Voice over IP (VOIP) network, also known as Internet telephony, is growing increasingly having occupied a large part of the communications market. With the growth of each technology, the related security issues become of particular importance. Taking advantage of this technology in different environments with numerous features put at our disposal, there arises an increasing need to address the security threats. Being IP-based and playing a signaling role in VOIP networks, Session Initiation Protocol (SIP) lets the invaders use weaknesses of the protocol to disable VOIP service. One of the most important threats is denial of service attack, a branch of which in this article we have discussed as flooding attacks. These attacks make server resources wasted and deprive it from delivering service to authorized users. Distributed denial of service attacks and attacks with a low rate can mislead many attack detection mechanisms. In this paper, we introduce a mechanism which not only detects distributed denial of service attacks and low rate attacks, but can also identify the attackers accurately. We detect and prevent flooding attacks in SIP protocol using Shannon (FDP-S), Renyi (FDP-R) and Tsallis (FDP-T) entropy. We conducted an experiment to compare the percentage of detection and rate of false alarm messages using any of the Shannon, Renyi and Tsallis entropy as a measure of disorder. Implementation results show that, according to the parametric nature of the Renyi and Tsallis entropy, by changing the parameters, different detection percentages and false alarm rates will be gained with the possibility to adjust the sensitivity of the detection mechanism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VOIP%20networks" title="VOIP networks">VOIP networks</a>, <a href="https://publications.waset.org/abstracts/search?q=flooding%20attacks" title=" flooding attacks"> flooding attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20networks" title=" computer networks"> computer networks</a> </p> <a href="https://publications.waset.org/abstracts/28214/sip-flooding-attacks-detection-and-prevention-using-shannon-renyi-and-tsallis-entropy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28214.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1665</span> Modeling Search-And-Rescue Operations by Autonomous Mobile Robots at Sea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Kriheli">B. Kriheli</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Levner"> E. Levner</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20C.%20E.%20Cheng"> T. C. E. Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20T.%20Ng"> C. T. Ng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> During the last decades, research interest in planning, scheduling, and control of emergency response operations, especially people rescue and evacuation from the dangerous zone of marine accidents, has increased dramatically. Until the survivors (called ‘targets’) are found and saved, it may cause loss or damage whose extent depends on the location of the targets and the search duration. The problem is to efficiently search for and detect/rescue the targets as soon as possible with the help of intelligent mobile robots so as to maximize the number of saved people and/or minimize the search cost under restrictions on the amount of saved people within the allowable response time. We consider a special situation when the autonomous mobile robots (AMR), e.g., unmanned aerial vehicles and remote-controlled robo-ships have no operator on board as they are guided and completely controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robots during the search-and rescue missions after an accident at sea. We presume that in the unknown environments, the AMR’s search-and-rescue activity is subject to two types of error: (i) a 'false-negative' detection error where a target object is not discovered (‘overlooked') by the AMR’s sensors in spite that the AMR is in a close neighborhood of the latter and (ii) a 'false-positive' detection error, also known as ‘a false alarm’, in which a clean place or area is wrongly classified by the AMR’s sensors as a correct target. As the general resource-constrained discrete search problem is NP-hard, we restrict our study to finding local-optimal strategies. A specificity of the considered operational research problem in comparison with the traditional Kadane-De Groot-Stone search models is that in our model the probability of the successful search outcome depends not only on cost/time/probability parameters assigned to each individual location but, as well, on parameters characterizing the entire history of (unsuccessful) search before selecting any next location. We provide a fast approximation algorithm for finding the AMR route adopting a greedy search strategy in which, in each step, the on-board computer computes a current search effectiveness value for each location in the zone and sequentially searches for a location with the highest search effectiveness value. Extensive experiments with random and real-life data provide strong evidence in favor of the suggested operations research model and corresponding algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disaster%20management" title="disaster management">disaster management</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20robots" title=" intelligent robots"> intelligent robots</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20algorithm" title=" scheduling algorithm"> scheduling algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=search-and-rescue%20at%20sea" title=" search-and-rescue at sea"> search-and-rescue at sea</a> </p> <a href="https://publications.waset.org/abstracts/79680/modeling-search-and-rescue-operations-by-autonomous-mobile-robots-at-sea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">172</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1664</span> Tsada-MobiMinder: A Location Based Alarm Mobile Reminder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marylene%20S.%20Eder">Marylene S. Eder</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Existing location based alarm applications has inability to give information to user’s particular direction to a specified place of destination and does not display a particular scenic spot from its current location going to the destination. With this problem, a location based alarm mobile reminder was developed. The application is implemented on Android based smart phones to provide services like providing routing information, helping to find nearby hotels, restaurants and scenic spots and offer many advantages to the mobile users to retrieve the information about their current location and process that data to get more useful information near to their location. It reminds the user about the location when the user enters some predefined location. All the user needs to have is the mobile phone with android platform with version 4.0 and above, and then the user can select the destination and find the destination on the application. The main objective of the project is to develop a location based application that provides tourists with real time information for scenic spots and provides alarm to a specified place of destination. This mobile application service will act as assistance for the frequent travelers to visit new places around the City. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=location%20based%20alarm" title="location based alarm">location based alarm</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20application" title=" mobile application"> mobile application</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20reminder" title=" mobile reminder"> mobile reminder</a>, <a href="https://publications.waset.org/abstracts/search?q=tourist%E2%80%99s%20spots" title=" tourist’s spots"> tourist’s spots</a> </p> <a href="https://publications.waset.org/abstracts/37282/tsada-mobiminder-a-location-based-alarm-mobile-reminder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37282.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1663</span> Modern Spectrum Sensing Techniques for Cognitive Radio Networks: Practical Implementation and Performance Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antoni%20Ivanov">Antoni Ivanov</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolay%20Dandanov"> Nikolay Dandanov</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicole%20Christoff"> Nicole Christoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20Poulkov"> Vladimir Poulkov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20spectrum%20access" title=" dynamic spectrum access"> dynamic spectrum access</a>, <a href="https://publications.waset.org/abstracts/search?q=GNU%20Radio" title=" GNU Radio"> GNU Radio</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title=" spectrum sensing"> spectrum sensing</a> </p> <a href="https://publications.waset.org/abstracts/81419/modern-spectrum-sensing-techniques-for-cognitive-radio-networks-practical-implementation-and-performance-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81419.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">245</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1662</span> Safety-critical Alarming Strategy Based on Statistically Defined Slope Deformation Behaviour Model Case Study: Upright-dipping Highwall in a Coal Mining Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lintang%20Putra%20Sadewa">Lintang Putra Sadewa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilham%20Prasetya%20Budhi"> Ilham Prasetya Budhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Slope monitoring program has now become a mandatory campaign for any open pit mines around the world to operate safely. Utilizing various slope monitoring instruments and strategies, miners are now able to deliver precise decisions in mitigating the risk of slope failures which can be catastrophic. Currently, the most sophisticated slope monitoring technology available is the Slope Stability Radar (SSR), whichcan measure wall deformation in submillimeter accuracy. One of its eminent features is that SSRcan provide a timely warning by automatically raise an alarm when a predetermined rate-of-movement threshold is reached. However, establishing proper alarm thresholds is arguably one of the onerous challenges faced in any slope monitoring program. The difficulty mainly lies in the number of considerations that must be taken when generating a threshold becausean alarm must be effectivethat it should limit the occurrences of false alarms while alsobeing able to capture any real wall deformations. In this sense, experience shows that a site-specific alarm thresholdtendsto produce more reliable results because it considers site distinctive variables. This study will attempt to determinealarming thresholds for safety-critical monitoring based on an empirical model of slope deformation behaviour that is defined statistically fromdeformation data captured by the Slope Stability Radar (SSR). The study area comprises of upright-dipping highwall setting in a coal mining area with intense mining activities, andthe deformation data used for the study were recorded by the SSR throughout the year 2022. The model is site-specific in nature thus, valuable information extracted from the model (e.g., time-to-failure, onset-of-acceleration, and velocity) will be applicable in setting up site-specific alarm thresholds and will give a clear understanding of how deformation trends evolve over the area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=safety-critical%20monitoring" title="safety-critical monitoring">safety-critical monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=alarming%20strategy" title=" alarming strategy"> alarming strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=slope%20deformation%20behaviour%20model" title=" slope deformation behaviour model"> slope deformation behaviour model</a>, <a href="https://publications.waset.org/abstracts/search?q=coal%20mining" title=" coal mining"> coal mining</a> </p> <a href="https://publications.waset.org/abstracts/158244/safety-critical-alarming-strategy-based-on-statistically-defined-slope-deformation-behaviour-model-case-study-upright-dipping-highwall-in-a-coal-mining-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158244.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1661</span> Fast and Accurate Model to Detect Ictal Waveforms in Electroencephalogram Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piyush%20Swami">Piyush Swami</a>, <a href="https://publications.waset.org/abstracts/search?q=Bijaya%20Ketan%20Panigrahi"> Bijaya Ketan Panigrahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneh%20Anand"> Sneh Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Manvir%20Bhatia"> Manvir Bhatia</a>, <a href="https://publications.waset.org/abstracts/search?q=Tapan%20Gandhi"> Tapan Gandhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Visual inspection of electroencephalogram (EEG) signals to detect epileptic signals is very challenging and time-consuming task even for any expert neurophysiologist. This problem is most challenging in under-developed and developing countries due to shortage of skilled neurophysiologists. In the past, notable research efforts have gone in trying to automate the seizure detection process. However, due to high false alarm detections and complexity of the models developed so far, have vastly delimited their practical implementation. In this paper, we present a novel scheme for epileptic seizure detection using empirical mode decomposition technique. The intrinsic mode functions obtained were then used to calculate the standard deviations. This was followed by probability density based classifier to discriminate between non-ictal and ictal patterns in EEG signals. The model presented here demonstrated very high classification rates ( > 97%) without compromising the statistical performance. The computation timings for each testing phase were also very low ( < 0.029 s) which makes this model ideal for practical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=ictal%20patterns" title=" ictal patterns"> ictal patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a> </p> <a href="https://publications.waset.org/abstracts/64484/fast-and-accurate-model-to-detect-ictal-waveforms-in-electroencephalogram-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64484.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1660</span> Improvements in OpenCV's Viola Jones Algorithm in Face Detection–Skin Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jyoti%20Bharti">Jyoti Bharti</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Gupta"> M. K. Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Astha%20Jain"> Astha Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a new improved approach for false positives filtering of detected face images on OpenCV’s Viola Jones Algorithm In this approach, for Filtering of False Positives, Skin Detection in two colour spaces i.e. HSV (Hue, Saturation and Value) and YCrCb (Y is luma component and Cr- red difference, Cb- Blue difference) is used. As a result, it is found that false detection has been reduced. Our proposed method reaches the accuracy of about 98.7%. Thus, a better recognition rate is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title="face detection">face detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Viola%20Jones" title=" Viola Jones"> Viola Jones</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title=" OpenCV"> OpenCV</a> </p> <a href="https://publications.waset.org/abstracts/48849/improvements-in-opencvs-viola-jones-algorithm-in-face-detection-skin-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">407</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1659</span> Fault Prognostic and Prediction Based on the Importance Degree of Test Point</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Junfeng%20Yan">Junfeng Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenkui%20Hou"> Wenkui Hou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prognostics and Health Management (PHM) is a technology to monitor the equipment status and predict impending faults. It is used to predict the potential fault and provide fault information and track trends of system degradation by capturing characteristics signals. So how to detect characteristics signals is very important. The select of test point plays a very important role in detecting characteristics signal. Traditionally, we use dependency model to select the test point containing the most detecting information. But, facing the large complicated system, the dependency model is not built so easily sometimes and the greater trouble is how to calculate the matrix. Rely on this premise, the paper provide a highly effective method to select test point without dependency model. Because signal flow model is a diagnosis model based on failure mode, which focuses on system’s failure mode and the dependency relationship between the test points and faults. In the signal flow model, a fault information can flow from the beginning to the end. According to the signal flow model, we can find out location and structure information of every test point and module. We break the signal flow model up into serial and parallel parts to obtain the final relationship function between the system’s testability or prediction metrics and test points. Further, through the partial derivatives operation, we can obtain every test point’s importance degree in determining the testability metrics, such as undetected rate, false alarm rate, untrusted rate. This contributes to installing the test point according to the real requirement and also provides a solid foundation for the Prognostics and Health Management. According to the real effect of the practical engineering application, the method is very efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=false%20alarm%20rate" title="false alarm rate">false alarm rate</a>, <a href="https://publications.waset.org/abstracts/search?q=importance%20degree" title=" importance degree"> importance degree</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20flow%20model" title=" signal flow model"> signal flow model</a>, <a href="https://publications.waset.org/abstracts/search?q=undetected%20rate" title=" undetected rate"> undetected rate</a>, <a href="https://publications.waset.org/abstracts/search?q=untrusted%20rate" title=" untrusted rate"> untrusted rate</a> </p> <a href="https://publications.waset.org/abstracts/28396/fault-prognostic-and-prediction-based-on-the-importance-degree-of-test-point" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28396.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">377</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1658</span> Jointly Optimal Statistical Process Control and Maintenance Policy for Deteriorating Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lucas%20Paganin">Lucas Paganin</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the advent of globalization, the market competition has become a major issue for most companies. One of the main strategies to overcome this situation is the quality improvement of the product at a lower cost to meet customers’ expectations. In order to achieve the desired quality of products, it is important to control the process to meet the specifications, and to implement the optimal maintenance policy for the machines and the production lines. Thus, the overall objective is to reduce process variation and the production and maintenance costs. In this paper, an integrated model involving Statistical Process Control (SPC) and maintenance is developed to achieve this goal. Therefore, the main focus of this paper is to develop the jointly optimal maintenance and statistical process control policy minimizing the total long run expected average cost per unit time. In our model, the production process can go out of control due to either the deterioration of equipment or other assignable causes. The equipment is also subject to failures in any of the operating states due to deterioration and aging. Hence, the process mean is controlled by an Xbar control chart using equidistant sampling epochs. We assume that the machine inspection epochs are the times when the control chart signals an out-of-control condition, considering both true and false alarms. At these times, the production process will be stopped, and an investigation will be conducted not only to determine whether it is a true or false alarm, but also to identify the causes of the true alarm, whether it was caused by the change in the machine setting, by other assignable causes, or by both. If the system is out of control, the proper actions will be taken to bring it back to the in-control state. At these epochs, a maintenance action can be taken, which can be no action, or preventive replacement of the unit. When the equipment is in the failure state, a corrective maintenance action is performed, which can be minimal repair or replacement of the machine and the process is brought to the in-control state. SMDP framework is used to formulate and solve the joint control problem. Numerical example is developed to demonstrate the effectiveness of the control policy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=maintenance" title="maintenance">maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=Xbar%20control%20chart" title=" Xbar control chart"> Xbar control chart</a> </p> <a href="https://publications.waset.org/abstracts/122378/jointly-optimal-statistical-process-control-and-maintenance-policy-for-deteriorating-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122378.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1657</span> Probability-Based Damage Detection of Structures Using Kriging Surrogates and Enhanced Ideal Gas Molecular Movement Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Ghasemi">M. R. Ghasemi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Ghiasi"> R. Ghiasi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Varaee"> H. Varaee </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=probability-based%20damage%20detection%20%28PBDD%29" title="probability-based damage detection (PBDD)">probability-based damage detection (PBDD)</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriging" title=" Kriging"> Kriging</a>, <a href="https://publications.waset.org/abstracts/search?q=surrogate%20modeling" title=" surrogate modeling"> surrogate modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced%20ideal%20gas%20molecular%20movement%20%28EIGMM%29" title=" enhanced ideal gas molecular movement (EIGMM)"> enhanced ideal gas molecular movement (EIGMM)</a> </p> <a href="https://publications.waset.org/abstracts/56392/probability-based-damage-detection-of-structures-using-kriging-surrogates-and-enhanced-ideal-gas-molecular-movement-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56392.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">239</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1656</span> Maintaining Energy Security in Natural Gas Pipeline Operations by Empowering Process Safety Principles Through Alarm Management Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huseyin%20Sinan%20Gunesli">Huseyin Sinan Gunesli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process Safety Management is a disciplined framework for managing the integrity of systems and processes that handle hazardous substances. It relies on good design principles, well-implemented automation systems, and operating and maintenance practices. Alarm Management Systems play a critically important role in the safe and efficient operation of modern industrial plants. In that respect, Alarm Management is one of the critical factors feeding the safe operations of the plants in the manner of applying effective process safety principles. Trans Anatolian Natural Gas Pipeline (TANAP) is part of the Southern Gas Corridor, which extends from the Caspian Sea to Italy. TANAP transports Natural Gas from the Shah Deniz gas field of Azerbaijan, and possibly from other neighboring countries, to Turkey and through Trans Adriatic Pipeline (TAP) Pipeline to Europe. TANAP plays a crucial role in maintaining Energy Security for the region and Europe. In that respect, the application of Process Safety principles is vital to deliver safe, reliable and efficient Natural Gas delivery to Shippers both in the region and Europe. Effective Alarm Management is one of those Process Safety principles which feeds safe operations of the TANAP pipeline. Alarm Philosophy was designed and implemented in TANAP Pipeline according to the relevant standards. However, it is essential to manage the alarms received in the control room effectively to maintain safe operations. In that respect, TANAP has commenced Alarm Management & Rationalization program as of February 2022 after transferring to Plateau Regime, reaching the design parameters. While Alarm Rationalization started, there were more than circa 2300 alarms received per hour from one of the compressor stations. After applying alarm management principles such as reviewing and removal of bad actors, standing, stale, chattering, fleeting alarms, comprehensive review and revision of alarm set points through a change management principle, conducting alarm audits/design verification and etc., it has been achieved to reduce down to circa 40 alarms per hour. After the successful implementation of alarm management principles as specified above, the number of alarms has been reduced to industry standards. That significantly improved operator vigilance to focus on mainly important and critical alarms to avoid any excursion beyond safe operating limits leading to any potential process safety events. Following the ‟What Gets Measured, Gets Managed” principle, TANAP has identified key Performance Indicators (KPIs) to manage Process Safety principles effectively, where Alarm Management has formed one of the key parameters of those KPIs. However, review and analysis of the alarms were performed manually. Without utilizing Alarm Management Software, achieving full compliance with international standards is almost infeasible. In that respect, TANAP has started using one of the industry-wide known Alarm Management Applications to maintain full review and analysis of alarms and define actions as required. That actually significantly empowered TANAP’s process safety principles in terms of Alarm Management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20safety%20principles" title="process safety principles">process safety principles</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20security" title=" energy security"> energy security</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20gas%20pipeline%20operations" title=" natural gas pipeline operations"> natural gas pipeline operations</a>, <a href="https://publications.waset.org/abstracts/search?q=alarm%20rationalization" title=" alarm rationalization"> alarm rationalization</a>, <a href="https://publications.waset.org/abstracts/search?q=alarm%20management" title=" alarm management"> alarm management</a>, <a href="https://publications.waset.org/abstracts/search?q=alarm%20management%20application" title=" alarm management application"> alarm management application</a> </p> <a href="https://publications.waset.org/abstracts/164904/maintaining-energy-security-in-natural-gas-pipeline-operations-by-empowering-process-safety-principles-through-alarm-management-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164904.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1655</span> Attack Redirection and Detection using Honeypots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chowduru%20Ramachandra%20Sharma">Chowduru Ramachandra Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Shatunjay%20Rawat"> Shatunjay Rawat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A false positive state is when the IDS/IPS identifies an activity as an attack, but the activity is acceptable behavior in the system. False positives in a Network Intrusion Detection System ( NIDS ) is an issue because they desensitize the administrator. It wastes computational power and valuable resources when rules are not tuned properly, which is the main issue with anomaly NIDS. Furthermore, most false positives reduction techniques are not performed during the real-time of attempted intrusions; instead, they have applied afterward on collected traffic data and generate alerts. Of course, false positives detection in ‘offline mode’ is tremendously valuable. Nevertheless, there is room for improvement here; automated techniques still need to reduce False Positives in real-time. This paper uses the Snort signature detection model to redirect the alerted attacks to Honeypots and verify attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=honeypot" title="honeypot">honeypot</a>, <a href="https://publications.waset.org/abstracts/search?q=TPOT" title=" TPOT"> TPOT</a>, <a href="https://publications.waset.org/abstracts/search?q=snort" title=" snort"> snort</a>, <a href="https://publications.waset.org/abstracts/search?q=NIDS" title=" NIDS"> NIDS</a>, <a href="https://publications.waset.org/abstracts/search?q=honeybird" title=" honeybird"> honeybird</a>, <a href="https://publications.waset.org/abstracts/search?q=iptables" title=" iptables"> iptables</a>, <a href="https://publications.waset.org/abstracts/search?q=netfilter" title=" netfilter"> netfilter</a>, <a href="https://publications.waset.org/abstracts/search?q=redirection" title=" redirection"> redirection</a>, <a href="https://publications.waset.org/abstracts/search?q=attack%20detection" title=" attack detection"> attack detection</a>, <a href="https://publications.waset.org/abstracts/search?q=docker" title=" docker"> docker</a>, <a href="https://publications.waset.org/abstracts/search?q=snare" title=" snare"> snare</a>, <a href="https://publications.waset.org/abstracts/search?q=tanner" title=" tanner"> tanner</a> </p> <a href="https://publications.waset.org/abstracts/143612/attack-redirection-and-detection-using-honeypots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143612.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1654</span> Epileptic Seizure Onset Detection via Energy and Neural Synchronization Decision Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marwa%20Qaraqe">Marwa Qaraqe</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ismail"> Muhammad Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=Erchin%20Serpedin"> Erchin Serpedin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20onset" title=" seizure onset"> seizure onset</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalography" title=" electroencephalography"> electroencephalography</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron" title=" neuron"> neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/24040/epileptic-seizure-onset-detection-via-energy-and-neural-synchronization-decision-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24040.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1653</span> Reduction of False Positives in Head-Shoulder Detection Based on Multi-Part Color Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lae-Jeong%20Park">Lae-Jeong Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20detection" title="pedestrian detection">pedestrian detection</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20segmentation" title=" color segmentation"> color segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20positive" title=" false positive"> false positive</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/61932/reduction-of-false-positives-in-head-shoulder-detection-based-on-multi-part-color-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1652</span> Developing Artificial Neural Networks (ANN) for Falls Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nantakrit%20Yodpijit">Nantakrit Yodpijit</a>, <a href="https://publications.waset.org/abstracts/search?q=Teppakorn%20Sittiwanchai"> Teppakorn Sittiwanchai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aging" title="aging">aging</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks%20%28ANN%29" title=" artificial neural networks (ANN)"> artificial neural networks (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=fall%20detection%20system" title=" fall detection system"> fall detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20sensorsthreshold" title=" motion sensorsthreshold"> motion sensorsthreshold</a> </p> <a href="https://publications.waset.org/abstracts/27966/developing-artificial-neural-networks-ann-for-falls-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27966.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=probability%20of%20false%20alarm&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=probability%20of%20false%20alarm&page=3">3</a></li> <li class="page-item"><a class="page-link" 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