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Conference on Vehicle Control and Intelligence (CVCI)

作者： Jiaqi Fan Tianjiao Huo Xin Li Jilin University College of Automotive Engineering Changchun China

ISBN: (数字)9781728184975

ISBN: (纸本)9781728184982

With the deep research on autonomous driving, the target detection algorithms based on 2D images have become a hot topic in recent years. In this paper, we mainly study the six mainstream deep learning detection algorithms, namely YOLO, YOLOv2, YOLOv3, YOLOv4, SSD and RetinaNet, which are used as the representative algorithms of the one-stage object detection methods. This paper is a review of the working principles of these six detection algorithms in deep learning. Firstly, this paper explains and compares the detection network structure, loss function and improvements of these six detection algorithms in detail according to the order in which they were presented. Especially the YOLOv3, YOLOv4, SSD and RetinaNet algorithms, which are the most common used algorithms at presented with a very high detection speed and detection precision for real-time detection of the autonomous driving. Secondly, by comparing the detection accuracy and speed of the YOLOv3, YOLOv4, SSD and RetinaNet algorithms, this paper gives their respective suitable application scenarios.

关键词： Deep learning Roads Two dimensional displays Object detection Real-time systems detection algorithms Autonomous vehicles

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A Comparative Study of Various Pitch detection algorithms

A Comparative Study of Various Pitch Detection Algorithms

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IEEE International Conference on Computing, Communication and Security (ICCCS)

作者： Prajwal S Rao S Khoushikh Sriram Ravishankar R Advaith Ananthkrishnan K Balachandra B.M.S. College of Engineering Bengaluru India

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

Digital Signal Processing deals with performing varied mathematical operations on different types of signals and in this paper, we study pitch estimation algorithms applied on music signals. We give a detailed study of time domain and frequency domain pitch detection algorithms namely Modified Autocorrelation, Average Magnitude Difference function, Yin, Cepstrum. The algorithms have been tested with real and synthesized music signals and the performances were compared based on time complexity and error obtained.

关键词： Signal processing algorithms Music Multiple signal classification Security detection algorithms Time-domain analysis Time complexity

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Impact of Evolutionary Community detection algorithms for Edge Selection Strategies

Impact of Evolutionary Community Detection Algorithms for Ed...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Paolo Barsocchi Stefano Chessa Luca Foschini Dimitri Belli Michele Girolami Istituto di Scienza e Tecnologie dell'Informazione ISTI-CNR National Council of Research Pisa Italy Dell'Informazione ISTI-CNR University of Pisa Italy University of Bologna Bologna Italy University of Pisa Italy

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key-metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

关键词： Crowdsensing Image edge detection Computer architecture Market research Broadband communication detection algorithms Edge computing

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Secure Traffic Networks in Smart Cities: Analysis and Design of Cyber-Attack detection algorithms

Secure Traffic Networks in Smart Cities: Analysis and Design...

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American Control Conference

作者： Tanushree Roy Satadru Dey Department of Electrical Engineering University of Colorado Denver Colorado USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

In this paper, we focus on cyber attacks in the context of macroscopic transportation network models. For our studies, we consider a strip of freeway traffic network that is actuated on the upstream boundary by ramp-metering, which are controlled remotely from a centralized command center. In our framework, we first formulate analytical conditions for generating stealthy cyber-attacks using Aw-Rascle-Zhang (ARZ) macroscopic traffic model. Such conditions elucidate the capability of attackers and theoretical limitations of attack detection algorithms. Subsequently, we propose a design framework for cyber attack detection algorithms that considers several desirable detection characteristics such as stability, robustness and attack sensitivity. Finally, we illustrate the effectiveness of our framework via simulation studies.

关键词： detection algorithms traffic network Frameworks Transport networks Attackers Information warfare macro-traffic model Secure

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Fidelity Measurement on Signal detection algorithms with Application to AFM Imaging

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IEEE ACCESS 2018年 6卷 52831-52842页

作者： Ghosal, Sayan Salapaka, Murti Seagate Technol Shakopee MN 55379 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Many signal processing and decision making algorithms reported in contemporary literature characterize the performance of the proposed methods utilizing decision error rates. However there is significant need for strategies that provide quantitative assessment of the fidelity of decisions made by the algorithms. This paper develops novel techniques utilizing which fidelity measures can be assigned quantitatively on some prevalent signal detection algorithms. The developed fidelity measurement methods with the detection algorithms are employed for topography imaging utilizing dynamic mode atomic force microscope (AFM). The AFM is a versatile metrology tool for interrogating material at the nano-scale. In spite of its remarkable achievements, a key issue that remains largely unaddressed is the assessment of fidelity of the measurement data. The developed paradigm facilitates user specific priority for either detection of sample features with high decision confidence or on not missing detection of true features. The fidelity measures presented here are suitable for real-time implementation. A detailed comparative study is presented to characterize the proposed signal detection algorithms and fidelity measurement techniques under practical AFM applications. Comprehensive simulation and experimental data corroborate the effectiveness of proposed methods.

关键词： Atomic force microscopy detection algorithms estimation Kalman filters observers scanning probe microscopy signal detection signal processing algorithms

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Measurement-based evaluation of data-parallelism for OpenCV feature-detection algorithms 42

Measurement-based evaluation of data-parallelism for OpenCV ...

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42nd Annual IEEE-Computer-Society Computers, Software and Applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Danielsson, Jakob Jagemar, Marcus Behnam, Moris Sjodin, Mikael Seceleanu, Tiberiu Malardalen Univ Vasteras Sweden Ericsson AB Stockholm Sweden

ISBN: (纸本)9781538626672

We investigate the effects on the execution time, shared cache usage and speed-up gains when using data-partitioned parallelism for the feature detection algorithms available in the OpenCV library. We use a data set of three different images which are scaled to six different sizes to exercise the different cache memories of our test architectures. Our measurements reveal that the algorithms using the default settings of OpenCV behave very differently when using data-partitioned parallelism. Our investigation shows that the executions of the algorithms SURF, Dense and MSER correlate to L3-cache usage and they are therefore not suitable for data-partitioned parallelism on multi-core CPUs. Other algorithms: BRISK, FAST, ORB, HARRIS, GFTT, SimpleBlob and SIFT, do not correlate to L3-cache in the same extent, and they are therefore more suitable for data-partitioned parallelism. Furthermore, the SIFT algorithm provides the most stable speed-up, resulting in an execution between 3 and 3.5 times faster than the original execution time for all image sizes. We also have evaluated the hardware resource usage by measuring the algorithm execution time simultaneously with the L3-cache usage. We have used our measurements to conclude which algorithms are suitable for parallelization on hardware with shared resources.

关键词： Feature extraction detection algorithms Parallel processing Detectors Multicore processing Hardware Libraries

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Performance Variation of Morphed Face Image detection algorithms across different Datasets 6

Performance Variation of Morphed Face Image Detection Algori...

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6th International Workshop on Biometrics and Forensics (IWBF)

作者： Scherhag, Ulrich Rathgeb, Christian Busch, Christoph Hsch Darmstadt Da Sec Biometr & Internet Secur Res Grp Darmstadt Germany

ISBN: (纸本)9781538613665

In past years, different researchers have shown the vulnerability of face recognition systems to attacks based on morphed face images. More recently, first morph detection subsystems have been proposed to automatically detect this kind of fraud. While some algorithms have been reported to reveal practical detection performance on individual datasets a systematic analysis of proposed detectors with respect to their robustness across different databases has remained elusive. In this work, we evaluate the performance of different morph detection algorithms across disjoint datasets of 2,745 bona fide and 14,337 automatically generated morphed face images. Within a generic evaluation framework a systematic robustness estimation scheme is proposed to identify reliable detection algorithms. Finally, the robustness of algorithms which have been determined as most promising is verified on another disjoint dataset. Hence, this paper represents the first attempt towards a comprehensive cross-database performance evaluation and a systematic evaluation of the robustness of morphed face image detection algorithms.

关键词： Face Feature extraction Robustness detection algorithms Databases Biometrics (access control) Estimation

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Evaluation of Anomaly detection algorithms Made Easy with RELOAD

Evaluation of Anomaly Detection Algorithms Made Easy with RE...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Tommaso Zoppi Andrea Ceccarelli Andrea Bondavalli Department of Mathematics and Informatics University of Florence Florence (IT)

ISBN: (纸本)9781728149837

Anomaly detection aims at identifying patterns in data that do not conform to the expected behavior. Despite anomaly detection has been arising as one of the most powerful techniques to suspect attacks or failures, dedicated support for the experimental evaluation is actually scarce. In fact, existing frameworks are mostly intended for the broad purposes of data mining and machine learning. Intuitive tools tailored for evaluating anomaly detection algorithms for failure and attack detection with an intuitive support to sliding windows are currently missing. This paper presents RELOAD, a flexible and intuitive tool for the Rapid EvaLuation Of Anomaly detection algorithms. RELOAD is able to automatically i) fetch data from an existing data set, ii) identify the most informative features of the data set, iii) run anomaly detection algorithms, including those based on sliding windows, iv) apply multiple strategies to features and decide on anomalies, and v) provide conclusive results following an extensive set of metrics, along with plots of algorithms scores. Finally, RELOAD includes a simple GUI to set up the experiments and examine results. After describing the structure of the tool and detailing inputs and outputs of RELOAD, we exercise RELOAD to analyze an intrusion detection dataset available on a public platform, showing its setup, metric scores and plots.

关键词： data mining graphical user interfaces learning (artificial intelligence) security of data anomaly detection Data Security graphical user interfaces data mining sliding windows algorithms detection algorithms Intuitive Metronidazole

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A Neural Network-Based Optimal Nonlinear Fusion of Speech Pitch detection algorithms

A Neural Network-Based Optimal Nonlinear Fusion of Speech Pi...

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International Conference on Knowledge-Based Engineering and Innovation (KBEI)

作者： Ziba Imani Seyed Jahanshah Kabudian Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

Fundamental frequency estimation is one of the most important issues in the field of speech processing. An accurate estimate of the fundamental frequency plays a key role in the field of speech and music analysis. So far, various methods have been proposed in the time- and frequency-domain. However, the main challenge is the strong noises in speech signals. In this paper, to improve the accuracy of fundamental frequency estimation, we propose a method for optimal nonlinear combination of fundamental frequency estimation methods, in noisy signals. In this method, to discriminate voiced frames from unvoiced frames in a better way, the Voiced/Unvoiced (V/U) scores of four pitch detection methods are combined with nonlinear fusion. These methods are: Autocorrelation (AC), Yin, YAAPT and SWIPE. After identifying the Voiced/Unvoiced label of each frame, the fundamental frequency (F 0 ) of the frame is estimated using the SWIPE method. The optimal function for nonlinear combination is determined using Multi-Layer Perceptron (MLP) neural network (NN). To evaluate the proposed method, 10 speech files (5 female and 5 male voices) are selected from the PTDB-TUG standard database and the results are presented in terms of GPE, VDE, PTE and FFE standard error criteria. The results indicate that our proposed method relatively reduced the aforementioned criteria (averaged in various SNRs) by 25.06%, 20.92%, 13.94%, and 25.94% respectively, which demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.

关键词： Noise measurement Estimation detection algorithms Frequency estimation Standards Mathematical model Correlation

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Application of Community detection algorithms on Social Internet-of-things Networks

Application of Community Detection Algorithms on Social Inte...

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International Conference on Microelectronics, ICM

作者： Abdullah Khanfor Hakim Ghazzai Ye Yang Yehia Massoud School of Systems & Enterprises Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9781728140582

ISBN: (纸本)9781728140599

The Internet-of-things (IoT) networks are witnessing a drastic increase over the years. Twenty billion devices connected to the Internet are expected in 2022. The need for identifying communities within such networks can serve as a strong complexity reduction mean for many discovery and identification services. The idea of communities in IoT networks is also motivated by the emerging concept of socializing IoT devices. In this paper, we investigate the application of two community detection algorithms, namely Louvain and Bron-Kerbosch algorithms, on IoT networks usually represented by large-scale graphs. The objective is to convert the complex IoT network into multiple overlapping and non-overlapping communities where its elements share common characteristics. Starting from a real-world IoT networks, we use its dataset to extract community-structured IoT network based on different types of relationships such as co-location, owner social relationships, and autonomously build object relationships among objects. Our analysis showcases how community detection algorithms structure the IoT network into communities based on the different relationships established between objects.

关键词： detection algorithms Sensors Complexity theory Internet Smart phones Quality of service Quality of experience

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