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Comparison of Artificial Intelligence algorithms for ELINT Signals Classification

Comparison of Artificial Intelligence Algorithms for ELINT S...

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New Trends in Signal Processing (NTSP)

作者： Jozef Perdoch Stanislava Gazovova Zdenek Matousek Jan Ochodnicky Armed Forces Academy of gen. M. R. Stefanik Liptovsky Mikulas Slovakia

ISBN: (数字)9781728161556

ISBN: (纸本)9781728161563

Ability to automatically identify objects of interest and to automatically classify their signals belongs to essential functionalities of electronic intelligence systems. Objects identification results and signals classification results are conditioned by accurate measurement and technical analysis of objects signal parameters. Classification algorithm based on Feedforward Neural Network, and classification algorithms based on Support-Vector Machine, with three types of Kernel Function, are tested and compared in this paper as the first stage of objects identification in electronic intelligence systems.

关键词： classification algorithms signal analysis electronic intelligence feedforward neural networks support vector machine feedforward neural nets Support Vector Network Signal analysis sorting algorithm Taxonomy signals AUTOMATIC precise measurement

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DETECTION OF MALICIOUS URLs IN BIG DATA USING RIPPER algorithm

DETECTION OF MALICIOUS URLs IN BIG DATA USING RIPPER ALGORIT...

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Sonika Thakur Er. Meenakshi Akansha Priya Centre for Computer Science & Technology Central University of Punjab

ISBN: (纸本)9781509037056

'Big Data' is the term that describes a large amount of datasets. Datasets like web logs, call records, medical records, military surveillance, photography archives, etc. are often so large and complex, and as the data is stored in Big Data in the form of both structured and unstructured therefore, big data cannot be processed using database queries like SQL queries. In big data, malicious URLs have become a station for internet criminal activities such as drive- bydownload,information warfare, spamming and phishing. Malicious URLs detection techniques can be classified into Non-Machine Learning (e.g. blacklisting) and Machine learning approach (e.g. data mining techniques). Data mining helps in the analysis of large and complex datasets in order to detect common patterns or learn new *** data is the collection of large and complex datasets and the processing of these datasets can be done either by using tool like Hadoop or data mining algorithms. Data mining techniques can generate classification models which is used to manage data, modelling of data that helps to make prediction about whether it is malicious or legitimate. In this paper analysis of RIPPER i.e. JRip data mining algorithm has been done using WEKA tool. A training dataset of 6000 URLs has been made to train the JRip algorithm which is an implementation of RIPPER algorithm in WEKA. Training dataset will generate a model which is used to predict the testing dataset of 1050 URLs. Accuracy are calculated after testing process. Result shows JRip has an accuracy of 82%.

关键词： Training Data mining Classification algorithms Testing Big Data algorithm design and analysis Feature extraction big data algorithm design and analysis data mining sorting algorithm scarifier Uniform Resource Locator Dataset Feature extraction Functional training

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SPATIAL-SPECTRAL CLASSIFICATION BASED ON GROUP SPARSE CODING FOR HYPERSPECTRAL IMAGE

SPATIAL-SPECTRAL CLASSIFICATION BASED ON GROUP SPARSE CODING...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xiangrong Zhang Peng Weng Jie Feng Erlei Zhang Biao Hou Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University

ISBN: (纸本)9781479911127

In this paper, a novel hyperspectral image classification method is proposed, based on group sparse coding. The method is based on this acknowledgement that larger spatial variation exists in high spatial resolution hyperspectral image, which degrades the separability of hyperspectral image. In order to obtain a smooth representation, each pixel and its spatial neighbors are coded together by group sparse coding. Although nothing about class information is included, the neighbor pixels in a small spatial window are inclined to belong to the same class. Thus, that will reduce the within-class scatter and be favorable to the classification task. Then, the obtained sparse representation vectors are used for hyperspectral image classification with SVM. Experimental results show that our method exceeds the classical classification algorithms in accuracy and regional consistency.

关键词： Hyperspectral image classification Spatial variance Group sparse Spatial-spectral hyperspectral imagery Image classification Taxonomy Sparse coding sorting algorithm images Classification Methods Spatial variations pixel

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Improved waveform design for non-Gaussian target classification in cognitive radar

Improved waveform design for non-Gaussian target classificat...

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IET International Radar Conference 2015

作者： Zhou Rui Zhou Bo Liu Lin Zhang Xiaofen Jiao Bin Aviation University of Air Force Changchun 130022 China Luoyang Electronic Equipment Testing Center Luoyang 471003 China

Because the performance of traditional Gaussian cognitive radar(CR) system for target response varying from Gaussian will be degraded, this paper extended the Gaussian target response to arbitrary non-Gaussian target distribution. In this paper, the CR multiple hypothesis classification algorithm was used for non-Gaussian targets, and the sparse spectrum of correlated narrowband target responses was utilized. In addition, we simulated the multiple nonGaussian distribution targets, and the simulation results demonstrate the non-Gaussian target classification algorithm is very effective.

关键词： Cognitive Radar Non-Gaussian Target Waveform Design Non-Gaussian Targets Gauss Simulation results sorting algorithm waveforms

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VOICE ACTIVITY DETECTION USING A SLIDING-WINDOW, MAXIMUM MARGIN CLUSTERING APPROACH

VOICE ACTIVITY DETECTION USING A SLIDING-WINDOW, MAXIMUM MAR...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Phillip De Leon Salvador Sanchez New Mexico State University Klipsch School of Electrical and Computer Engineering Las Cruces New Mexico U.S.A.

ISBN: (纸本)9781479903573

Recently, an unsupervised, data clustering algorithm based on maximum margin, i.e. support vector machine (SVM) was reported. The maximum margin clustering (MMC) algorithm was later applied to the problem of voice activity detection, however, the application did not allow for real-time detection which is important in speech processing applications. In this paper, we propose a voice activity detector (VAD) based on a sliding window, MMC algorithm which allows for real-time detection. Our system requires a separate initialization stage which imposes an initial detection delay, however, once initialized the system can operate in real-time. Using TIMIT speech under several NOISEX-92 noise backgrounds at various SNRs, we show that our average speech and non-speech hit rates are better than state-of-the-art VADs.

关键词： Speech analysis Classification algorithms Speech analysis sorting algorithm real-time detection Support Vector Network Voice Speech processing Cluster Analysis algorithms activity METAL MATRIX COMPOSITES Mitocin-C

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Unlocking the Smartphone's Senses for Smart City Parking

Unlocking the Smartphone's Senses for Smart City Parking

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Ad-Hoc and Sensor Networks Symposium

作者： Jean-Gabriel Krieg Gentian Jakllari Hadrien Toma Andre-Luc Beylot ALSETT University of Toulouse - IRIT

ISBN: (纸本)9781479966653

Studies have shown that in places like New York City drivers often spend over 20 min looking for parking, contributing to as much as 30% of the total traffic. In response, cities like San Francisco have deployed systems capable of pointing drivers to the closest available parking spot. Unfortunately, such systems have gained little traction as they rely on specialized infrastructure that is expensive to build and maintain. We present SmartPark, a smartphone based system that relaxes the requirement for specialized infrastructure by relying on the smartphone's sensors and ubiquitous Wi-Fi and cellular infrastructure. To accomplish this, SmartPark addresses two major challenges, under the constraint of minimum impact on battery life: automatic transportation mode detection and location matching. Solved together, they enable SmartPark to automatically detect when a user pulls out of a parking spot, making it available again. It addresses each challenge using a combination of thorough statistical analysis of the sensor readings and a novel Random Forest based classification algorithm. Experimental results from 12 volunteers, using 7 different smartphones, in 3 different cities show that SmartPark can distinguish 9 different transportation modes with accuracy between 95.57 - 100%, enabling it to correctly detect unparking events virtually 100% of the time. This is accomplished with a minimum impact on battery life - running SmartPark on a fully charged LG Google Nexus 5 for 5 h straight caused the battery level to drop only about 4%.

关键词： Smartphone Battery Life city parking Transportation modes total traffic Parking sorting algorithm unlocking INFRASTRUCTURE senses

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Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection

Exploration of Gait Parameters Affecting the Accuracy of For...

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IEEE International Conference on Biomedical Robotics and Biomechatronics

作者： X. Jiang L. Tory M. Khoshnam K. H. T. Chu C. Menon Menrva Research Group Schools of Mechatronic System and Engineering Science Simon Fraser University

ISBN: (纸本)9781538681848

Gait analysis has been considered in various scenarios to provide information about the ambulatory physical activity. In this regard, studying gait phases can provide valuable information about the quality of gait. Force myography (FMG) techniques have been successfully employed to detect gait events using pattern recognition methods. This paper explores how the accuracy of detecting gait phases is correlated with the parameters of gait and FMG signal. To this end, FMG data were collected from 11 volunteers walking on a treadmill with a custom-designed FMG ankle band. The collected FMG data were classified into four gait phases using Linear Discriminant Analysis (LDA) algorithm. The correlation between the error in classification and the parameters of gait and FMG signal was then investigated. The results show that in comparison with other studied parameters, variations in stride length have the most impact on the accuracy of gait phase classification with a coefficient of determination (R~2) of 0.80. Such an effect is more pronounced when signal power-related features, such as root mean square (RMS), are used in the classification algorithm. This study provides insight into the factors affecting the accuracy of FMG-based techniques for gait analysis and is a preliminary step towards developing high performance FMG-based wearable ambulatory activity monitoring systems.

关键词： Locomotion Gait gait analysis Exploration reduced mass pattern recognition method Phase detection sorting algorithm

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Increasing Flexibility of Time Triggered Ethernet based Systems by Optimal Greedy Scheduling Approach

Increasing Flexibility of Time Triggered Ethernet based Syst...

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IEEE Conference on Emerging Technologies and Factory Automation

作者： Lukasz Wisniewski Markus Schumacher Juergen Jasperneite Christian Diedrich inIT - Institute Industrial IT Ostwestfalen-Lippe University of Applied Sciences Fraunhofer IOSB-INA Application Center Industrial Automation Institute for Automation Technology Otto-von-Guericke University Magdeburg

ISBN: (纸本)9781467379304

Many aspects have to be considered while achieving flexibility of production systems. One of them is flexible communication system that on one hand fulfils requirements of a particular industrial application and on the other hand allows integration of new network components optimally in a seamless way. In this paper, an approach to increase, rather static, Time Triggered Ethernet based systems is proposed. It is based on a greedy scheduling approach that can be implemented directly in a processing limited hardware such as IO Controller. This, together with the discovery and auto configuration mechanism proposed in [1] allows to shift the engineering process typically performed offline using engineering tool to the field level. Furthermore, it has been proven that the proposed scheduling approach is not only simple but also powerful. It is able to calculate an optimal schedule for any kind of tree graph and the complexity of the algorithm is defined by the complexity of the used sorting algorithm.

关键词： Tree graphs Ethernet networks greedy method sorting algorithm Engineering tools Network components field level IO Flexibility

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FPGA Implementation of Real-Time Soldier Activity Detection based on Neural Network Classifier in Smart Military Suit

FPGA Implementation of Real-Time Soldier Activity Detection ...

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IEEE Bombay Section Signature Conference (IBSSC)

作者： Nikhil B. Gaikwad Avinash G. Keskar Varun Tiwari NC Shivaprakash Electronics and Communication Department Visvesvaraya National Institute of Technology (VNIT) Nagpur India Department of Instrumentation and Applied Physics Indian Institute of Science (IISc) C V Raman Ave Bengaluru India

ISBN: (纸本)9781538674765

The wearable technology carries sufficient potential to incorporate smartness into working of the military workforces like the Military Control Unit, Medical Responders, Backup Unit and War Strategist. The proposed work focuses on real-time soldier activity detection, which is essential for the operation of the smart military suit. The customized Artificial Neural Network (ANN) IP core is developed for the soldier activity classification, which is an integral component of suit gateway design. The multilayer perceptron (7-5-4) classification algorithm is implemented on the low-cost (99$) FPGA evaluation platform by using Xilinx vivado and system generator development tools. The training (70%) and testing (30%) of this ANN design is performed on the UCI human activity dataset. The LabVIEW GUI and IP test design completed the hardware testing of this IP. The presented ANN IP is able to achieve 98.5% classification accuracy by utilizing minimal FPGA (Artix-7 xc7a35t) resources. The implemented ANN design requires only 285 nanoseconds for a classification and consumes 103 milliwatts of dynamic power. The system's accuracy at different development levels is also studied in this work.

关键词： Smart military suit Soldier activity detection Artificial neural network Smart sensor gateway Network centric warfare Field programmable gate array Field programmable gate arrays Artificial neural networks Soldiers Activity Detection Military Personnel Hardware Test brightness EIS IMPLEMENTATION PLANS Bayesian classifier sorting algorithm

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Incremental fuzzy learning algorithms in big data problems: a study on the size of learning subsets

Incremental fuzzy learning algorithms in big data problems: ...

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IEEE International Conference on Fuzzy Systems

作者： Rocio Romero-Zaliz Antonio González Raúl Pérez Department of Computer Science and Artificial Intelligence ETSIIT University of Granada Granada Spain

Incremental approaches may be used to speed up the learning process when a classification algorithm is dealing with big data bases. In this work we present a study on how the size and composition of the set of learnin... 详细信息

ISBN: (纸本)9781509060351

关键词： Big Data Adaptation models Prediction algorithms Databases Pragmatics Iris Computer science big data Pragmatics Prediction algorithms computer science Iris Iris Database Adaptation models sorting algorithm Incremental

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