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检索条件"主题词=detection algorithms"

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Outlier and Trend detection Using Approximate Median and Median Absolute Deviation

Outlier and Trend Detection Using Approximate Median and Med...

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International Conference on Computational Intelligence and Networks (CINE)

作者： Gagandeep Singh Suman Kundu Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9781665464666

In this modern era of technologies of scale, vast amounts of data are generated both by users and machines every day. This data comes as streams that may contain outliers. Detecting those outliers can be helpful in many ways, such as machine failures due to overload. Similarly, trends in social media posts are also outliers, and detecting them at different levels has great benefits. The current paper proposes an algorithm to approximate median and median absolute deviation from a stream of numerical values. The algorithm takes a fixed number of memory spaces and linear to the size of the memory. The median and median absolute deviation are then used to detect outliers and multi-level trends without being prone to noise in the data. Experimental results with CPU usage benchmark data and Twitter post data show the effectiveness of the proposed algorithms.

关键词： Social networking (online) Blogs Benchmark testing Approximation algorithms Market research Noise measurement detection algorithms

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Effectiveness and Influence of Parts of Speech like adjectives and interjections for examining the feelings in sentences containing sarcasm: A study

Effectiveness and Influence of Parts of Speech like adjectiv...

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International Conference on Advances in Information Technology (ICAIT)

作者： Adarsh M J Pushpa Ravikumar Dept. of CS&E (Data Science) Adichunchanagiri Institute of Technology (Affiliated to VTU Belagavi) Chikkamagaluru Karnataka India Dept. of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru Karnataka India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

In contemporary text analytics, examining the feelings in customer evaluations has taken centre stage. It is difficult to identify emotions in sentences written with irony. Social media portals and e-commerce portals are offering users to come up with comments and reviews with realistic funny emotions. As a way to identify the feelings expressed in sentences written in a mocking tone purpose, we have attempted to highlight the Adjectives function in this study by taking into account positive and negative adjectives as well as an interjection, both good and bad words. We propose the Adjective-based Sarcasm detection Algorithm (ABSA) and the Interjection-based Sarcasm detection Algorithm (IBSA) based on lexicon approach as machine learning approaches won't yield better results in detection of irony and have compared it with the existing approach with more sarcastic sentences. Our F score results for ABSA and IBSA are 0.86 and 0.91, respectively. This paper sheds light on investigating humour or irony in context of parts of speech (POS) words.

关键词： Social networking (online) Reviews Machine learning Electronic commerce detection algorithms Information technology Portals

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Iterative Turbo Receiver for FTN Based on Semi-Definite Relaxation

Iterative Turbo Receiver for FTN Based on Semi-Definite Rela...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： YuTao Jiang WenBin Gong Honghao Chen Yu Gu Tangju Yuan Yu Su School of Electronic and Information Engineering Chongqing Three Gorges University Wanzhou Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Faster-than-Nyquist (FTN) technology, as a transmission technique that can improve spectrum efficiency, has received widespread attention. However, it introduces severe Inter-Symbol Interference (ISI), leading to increased complexity in symbol detection at the receiver end. An FTN detector based on Semidefinite Relaxation (SDR) exhibits good performance and low complexity. In this paper, an iterative FTN detector based on soft-output SDR is proposed. In traditional turbo receivers, forward error correction codes are only used at the decoder. However, the SDR detector proposed in this paper outputs external information to the decoder with higher reliability. By simplifying the SDR Turbo receiver, this approach solves only one SDR problem for each codeword instead of multiple SDR problems in the iterative turbo processing, thus reducing the time complexity of the SDR turbo receiver while maintaining the original error performance.

关键词： Symbols Receivers Detectors Iterative algorithms Decoding Reliability Time complexity detection algorithms Iterative decoding Software engineering

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Design of A Novel NTN PRACH Format Combining Zadoff-Chu and Golden Sequences

Design of A Novel NTN PRACH Format Combining Zadoff-Chu and ...

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IEEE Conference on Wireless Communications and Networking

作者： Yanyi Song Liang Yin Yitong Liu Hongwen Yang School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing P.R. China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Non-terrestrial network (NTN) is the next generation access technology between terminals and satellites, based on new air interface technology developed by the 3rd Generation Partnership Project(3GPP) in the Release 17. While 3GPP integrates 5G NR's Physical Random Access Channel (PRACH) into NTN scenarios, it faces an issue of insufficient preambles. Currently, numerous studies have combined M sequences or Golden sequences with Zadoff-Chu (ZC) sequences to design PRACH formats to address this challenge. This paper introduces a novel PRACH format based on Golden sequences and ZC sequences. Golden sequences and ZC sequences are individually detected, resulting in the introduction of two detection methods: the Golden priority detection algorithm and the ZC priority detection algorithm. This innovative design resolves the problem of massive detection calculations found in current common detection algorithms and also increases the quantity of preambles. Finally, the simulation results show that the new scheme can obtain the accurate results with less calculation.

关键词： Technological innovation Satellites Correlation Simulation Resists Frequency estimation detection algorithms

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CIGA: Detecting Adversarial Samples via Critical Inference Graph Analysis

CIGA: Detecting Adversarial Samples via Critical Inference G...

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Annual Computer Security Applications Conference

作者： Fei Zhang Zhe Li Yahang Hu Yaohua Wang National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China

ISBN: (数字)9798331520885

ISBN: (纸本)9798331520892

Deep neural networks (DNNs) exhibit significant vulnerability under adversarial sample attacks, where carefully crafted small perturbations added to benign samples lead to misclassification during testing. A large amount of work has been proposed to detect adversarial samples. However, existing works primarily rely on data or activation features of DNNs, focusing only on the impact of individual neurons at each layer while neglecting the effects of inter-layer correlations on decisions. Our key observation is that benign and adversarial samples tend to experience not only distinctive neurons in each layer but also different connection patterns among different layers during inference. Leveraging this insight, we extract Critical Inference Graphs (CIGs) of samples from DNNs. Based on the statistical features of CIGs, we analyze the data features of each layer and the structural features between layers during the classification process. We then propose an unsupervised adversarial sample detection algorithm via CIG Analysis (CIGA). We evaluate CIGA against 7 white-box attacks, 2 black-box attacks and 1 real-world attack. We also compare CIGA with six state-of-the-art (SOTA) detection algorithms. The experimental results demonstrate that our proposed CIGA maintains high sensitivity (over 90%) while keeping a low false positive rate (below 3%) across all attacks, showing better generalization performance compared to SOTA algorithms.

关键词： Support vector machines Sensitivity Correlation Perturbation methods Neurons Artificial neural networks Feature extraction Robustness detection algorithms Testing

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Integrating UAV-Based Aerial Monitoring and SSD for Enhanced Traffic Management in Smart Cities

Integrating UAV-Based Aerial Monitoring and SSD for Enhanced...

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Mediterranean Smart Cities Conference (MSCC)

作者： Murat Bakirci Irem Bayraktar Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey

ISBN: (数字)9798350374001

ISBN: (纸本)9798350374018

In the context of smart cities, transportation stands out as a critical aspect, showcasing innovative solutions to tackle congestion, emissions, and overall mobility challenges. From intelligent traffic management systems to the proliferation of shared mobility services, these cities prioritize efficiency and sustainability. Moreover, they foster the development of electric and autonomous vehicles, enhancing safety and reducing carbon footprints. Intelligent transportation systems are crucial in addressing transportation challenges within smart cities. These systems harness cutting-edge technology to optimize efficiency, safety, and sustainability by enabling dynamic traffic management and promoting multimodal connectivity. Object detection technology further enhances traffic management by providing real-time data for proactive intervention and optimization of traffic patterns. Object detection algorithms revolutionize traffic management by accurately identifying and tracking vehicles in real-time. Additionally, unmanned aerial vehicles (UAVs) serve as powerful tools for aerial traffic monitoring, providing valuable real-time imagery for object detection algorithms. This study focuses on enhancing ITS within smart cities through the integration of UAV-based aerial monitoring and the SSD detection algorithm for vehicle detection. By compiling a large dataset using vehicle images acquired through UAV aerial monitoring, the study demonstrates robust and real-time vehicle detection capabilities. The integration of UAV-based aerial monitoring with SSD vehicle detection holds immense potential for improving overall transportation within cities, contributing to safer, more efficient, and sustainable urban environments.

关键词： Accuracy Smart cities Object detection Autonomous aerial vehicles Real-time systems Safety detection algorithms Sustainable development Vehicle dynamics Monitoring

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Dynamic Community detection Algorithm based on Allocating and Splitting

Dynamic Community Detection Algorithm based on Allocating an...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Wanchang Jiang Xiaoxi Zhang School of Computer Science Northeast Electric Power University Jilin China

Since the incremental community detection method is affected by the initial network community and the incremental detection process, it is prone to generate the problem of error accumulation. To solve the above problem, a dynamic community detection algorithm is proposed. First, considering the closeness between nodes to detect the community structure of the first snapshot network. After that, active nodes that reflect network changes are identified in adjacent snapshots and allocated to communities. Then, during the incremental detection process, considering the influence of adding edges on the accuracy of the community, the edge-added node is defined. In this way, the allocated community structure can be split into multiple local communities and singleton communities constructed by edge-added nodes and other nodes. By merging and optimizing the split communities, the final community structure can be detected. Finally, experiments show that the normalized mutual information and the modularity are improved by an average of 5.23% and 5.00% on synthetic dynamic networks. The modularity is improved by an average of 2.39% on real dynamic networks.

关键词： Image edge detection Heuristic algorithms Merging Dynamic scheduling detection algorithms Artificial intelligence Mutual information

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Radar Sistemlerinde Çoklu Sahte Hedef Karıştırma Tekniklerine Karşı CFAR Algoritmalarının Performans Değerlendirmesi

Radar Sistemlerinde Çoklu Sahte Hedef Karıştırma Teknikl...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Ahmet Uslu Gülşah Yıldız Altıntaş Baran Kırdar Elektrik - Elektronik Fakültesi REHİS İstanbul Teknik Üniversitesi ASELSAN A.Ş. İstanbul Türkiye Ankara Türkiye Elektrik - Elektronik Fakültesi İstanbul Teknik Üniversitesi İstanbul Türkiye REHİS ASELSAN A.Ş. Ankara Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

This study comprehensively evaluates the performance of various Constant False Alarm Rate (CFAR) detection algorithms used in Radar systems against multiple false target clutter techniques. The algorithms under investigation include Cell Averaging (CA-CFAR), Ordered Statistics (OS-CFAR), Greatest Cell Averaging (GO-CA-CFAR), and Smallest Cell Averaging (SO-CA-CFAR). CFAR algorithms provide adaptive adjustment of the detection threshold, offering variable detection performance based on the characteristics of clutter signals. Multiple false target clutter is a strategy aiming for Radar receivers to successfully detect as many false targets as possible. The foundation of this success lies in the ability to detect false targets above the Radar’s detection threshold. The existing literature often focuses on the CA-CFAR algorithm, and there is a lack of analysis regarding the effectiveness of other CFAR detection algorithms and their performance against multiple false target clutter techniques. This study is conducted to fill this gap in the literature.

关键词： Radar clutter Radar detection Receivers Data processing Radar signal processing Clutter Jamming detection algorithms Artificial intelligence

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RetryTRACK: Recovering Misses in Multi-Camera 3D Pedestrian Tracking

RetryTRACK: Recovering Misses in Multi-Camera 3D Pedestrian ...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Isabella de Andrade João Paulo Lima Veronica Teichrieb Voxar Labs Centro de Informática Universidade Federal de Pernambuco Departamento de Computação Universidade Federal Rural de Pernambuco

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Tracking pedestrians commonly relies on detection algorithms. However, these algorithms are not always correct and may miss some pedestrians. Although using multiple cameras is a way to handle this, some failures still need to be fixed. Thus, it is desirable that the tracker attempt to fix the detections. This work introduces an online and untrained module designed to recover missing detections during multiple camera tracking. The module applies linear extrapolation and Gaussian process regression techniques to produce new smoothed coordinates. Furthermore, we propose a filter to remove duplicate detections. We attached the module to a multi-camera baseline tracker and evaluated it on the WILDTRACK and MultiviewX datasets. The multiple object tracking accuracy was improved by 0.95 p.p. on WILDTRACK and 2.34 p.p. on MultiviewX with the addition of the module. Moreover, this strategy successfully recovered 17.98% of missing detections in WILDTRACK and a significant 40.12% in MultiviewX, underscoring its practical application and potential to address the problem.

关键词： Graphics Extrapolation Pedestrians Three-dimensional displays Accuracy Gaussian processes Filtering algorithms Cameras Object tracking detection algorithms

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Wi-locind: Location-Independent Respiration Sensing Based on WIFI CSI

Wi-locind: Location-Independent Respiration Sensing Based on...

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IEEE Conference on Wireless Communications and Networking

作者： Jun Wan Ting Jiang Xinyi Zhou Danlan Huang Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Current WiFi-based respiration detection algorithms may experience performance degradation due to variations in user location, as the relationship between user location and patterns of respiration has not been adequately considered. To overcome this limitation, this paper proposes a spatially directional respiration detection approach, named Wi-locind. Wi-locind employs antenna arrays on commercial WiFi receivers to achieve directional enhancement of respiration signals. Combined with post-filtering techniques, Wi-locind is capable of extracting respiration patterns that are independent of changes in the user's location. Specifically, the Minimum Variance Distortionless Response algorithm is used to identify the arrival angle of the target user and directionally enhance the received signal in the corresponding direction. The Empirical Mode Decomposition algorithm is subsequently utilized to suppress the environmental noise and time domain artifacts caused by the enhancement method, enabling the extraction of the target's respiration pat-tern. Our results show that the proposed approach consistently achieves an average absolute error of less than 0.3 breaths per minute across all positions, significantly outperforming the baseline approaches.

关键词： Working environment noise Receiving antennas Robustness Real-time systems Sensors Channel models detection algorithms

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