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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Borah, Asha Rani Ravi, Nikhil Narayan, Nikhil D'Souza, Prathik Joel New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This study explores how advanced technology has revolutionized the use of drones for forest fire surveillance. This research study explores the historical development of Unmanned Aerial Vehicles (UAVs) in forest monitoring with a focus on control systems and detection algorithms. The drone's capabilities are improved via telemetry integration, sophisticated autopilot systems, and an emphasis on accuracy, autonomy, and real-Time communication. An analysis of object recognition, anomaly detection, and environmental monitoring algorithms in detail demonstrates how important a role these algorithms play in spotting dangers like forest fire. This study presents successful implementations through illustrative case studies, discusses obstacles, and lays forth future plans for utilizing drone technology for the forest fire surveillance in environmental conservation initiatives. © 2024 IEEE.

关键词： Autonomy Control systems Cutting-edge technology detection algorithms Forest surveillance drones Historical evolution Real-Time communication Unmanned Aerial Vehicles

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Density-Peak-Based Overlapping Community detection Algorithm

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第4期9卷 1211-1223页

作者： Sun, Liping Ye, Tao Sun, Jian Duan, Xiaoyu Luo, Yonglong Anhui Normal Univ Sch Comp & Informat Anhui Prov Key Lab Network & Informat Secur Wuhu 241003 Peoples R China Nanjing Univ Software Inst Nanjing 210023 Peoples R China

Overlapping community detection is essential for revealing the hidden structure of complex networks. In this work, we present an overlapping community detection algorithm that selects community centers adaptively based on density peaks. The proposed algorithm, called the density-peak-based overlapping community detection (DPOCD) algorithm, defines point link strength and edge link strength to construct distance matrix. Unlike the density peaks clustering algorithm, by which cluster centers are selected manually, the DPOCD algorithm uses the linear fitting method to select community centers. To evaluate the feasibility of the presented algorithm, we compared it with other advanced methods on artificial synthetic network and real complex network datasets. The experimental results demonstrate that our method achieves excellent performance in large-scale complex networks and the robustness of the algorithm.

关键词： Complex networks Clustering algorithms detection algorithms Sun Optimization Image edge detection Robustness Adaptive selection of cluster centers community detection algorithm density peak overlapping community

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SAUNet3+CD: A Siamese-Attentive UNet3+for Change detection in Remote Sensing Images

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IEEE ACCESS 2022年 10卷 101434-101444页

作者： Mo, Junsang Seong, Seonkyeong Oh, Jaehong Choi, Jaewan Korea Res Inst Human Settlements Geospatially Enabled Soc Res Div Sejong 30147 South Korea Chungbuk Natl Univ Dept Civil Engn Cheongju 28644 South Korea Korea Maritime & Ocean Univ Dept Civil Engn Interdisciplinary Major Ocean Renewable Energy En Busan 49112 South Korea

With the development of various optical sensors, change detection is one of the most actively researched areas in remotely sensed imagery with high spatial resolution. In particular, deep learning-based change detection techniques are very important for use in various fields, such as land monitoring and disaster analysis, because they can show superior performance compared to traditional unsupervised and supervised change detection methods. This manuscript proposes a Siamese-attentive UNet3+ for change detection (SAUNet3+CD) of multitemporal imagery with high spatial resolution. The existing UNet3+ was modified to a Siamese-based architecture, and a spatial and channel attention module was added to detect various changed areas. The proposed model was trained to effectively detect building growth and decay through the data augmentation of open datasets and a hybrid loss function. In experiments using two open datasets, the proposed deep learning model effectively detected changed areas in multitemporal images better than various methods, such as existing Siamese-based networks and a network for semantic segmentation.

关键词： Semantics Feature extraction Remote sensing Convolutional neural networks Deep learning Spatial resolution detection algorithms Change detection algorithms Change detection attention module deep learning UNet3+ Siamese-based architecture

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iSensMex-POT, a Potentiostat Platform for Human Health Applications

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IEEE ACCESS 2024年 12卷 169050-169068页

作者： Zamora-Mejia, Gregorio Carbajal-Gomez, Victor H. Alvarez-Simon, Luis C. Silva-Juarez, Alejandro Bautista-Castillo, Alejandro I. Rosales-Nunez, Sergio A. Rocha-Perez, Jose M. Barbosa-Escudero, Francisco Sanchez-Dominguez, Margarita Durini, Daniel Consejo Nacl Humanidades Ciencia & Tecnol CONAHCyT Mexico City 03940 Mexico Inst Nacl Astrofis Opt & Elect INAOE Puebla 72840 Mexico Benemerita Univ Autonoma Puebla BUAP Dept Elect Sci Puebla 72570 Mexico Ctr Invest Mat Avanzados CIMAV Subsede Monterrey Ciudad Apodaca 66628 Mexico

This work introduces the iSensMEX-POT platform, a potentiostat-based detection system for conducting cyclic voltammetry measurements. It consists of a mixed-mode application-specific integrated circuit (ASIC) and a user digital interface unit based on Raspberry/Python platform. The ASIC consists of three main building blocks: a potentiostat built by three operational amplifiers (OpAmps) working as an analog front-end, a 10-bit R2R digital-to-analog converter (DAC) and a 10-bit successive-approximation analog-to-digital converter (SAR-ADC) composing the mixed signal mode processing stage, and a digital finite state machine using the Inter-Integrated Circuit (I2C) protocol conforming the digital data processing and transmission section. The Raspberry controls the ASIC using the I2C, writes a digital words into the ASIC's registers to replicate a triangular cyclic voltammetry signal with a scan rate of 200 mV/s over a range of +/- 1 V, and reads the digitized data from the SAR-ADC. The Raspberry platform displays the resulting waveform on a touch-screen using a graphical user interface (GUI). Also, a precision model based on the open-loop gain of the amplifier and an output noise quantification model were developed and applied to design a highly accurate potentiostat based on three amplifiers. The proposed platform's functionality and accuracy was validated by comparing the data gathered by the iSensMex-POT with the data obtained with the EVAL-AD5940ELCZ commercial board. The proposed ASIC was mathematically modeled, simulated and fabricated in a 180 nm CMOS standard technology, featuring a silicon area of 1.39 mm2 and a power consumption of 52.99 mW (16.05 mA @ 3.3 V).

关键词： Computer architecture Noise measurement Gain measurement Transistors Universal Serial Bus Microprocessors Electrodes Graphical user interfaces Costs Voltage control detection algorithms Arsenic bacteria creatinine cyclic voltammetry drinking water human health (IC)-C-2 molecules potentiostat virus

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BECT Spike detection Algorithm Based on Optimal Template Matching and Morphological Feature Selection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第4期69卷 2366-2370页

作者： Wu, Duanpo Shi, Haichao Jiang, Lurong Dong, Fang Liu, Junbiao Cao, Jiuwen Jiang, Tiejia Wu, Xunyi Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China Zhejiang Univ City Coll Coll Informat & Elect Engn Hangzhou 310015 Peoples R China Zhejiang Univ City Coll Artist Design & Creat Sch Hangzhou 310015 Peoples R China Hangzhou Dianzi Univ Machine Learning I Hlth Int Cooperat Base Zhejian Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Sch Med Childrens Hosp Hangzhou 310052 Peoples R China Fudan Univ Huashan Hosp Dept Neurol Shanghai 200040 Peoples R China

Over 15% of children with epilepsy belong to benign childhood epilepsy with centro-temporal spikes (BECT), facing with educational difficulties. The accurate recognition of spikes in electroencephalogram (EEG) signals collected from BECT patients can help the doctor to effectively make diagnosis and give therapeutic schedule. Traditionally, template matching method can extract spike-like waves from EEG signals and is adopted by many researches. However, the patterns of the spikes appeared in different patients or different time in one patient varies greatly. The brief proposes a spike detection algorithm based on optimal template matching and morphological feature selection, which includes universal template matching, spike clustering, universal template optimization based on particle swarm optimization (PSO) algorithm and false positive spike (FPS) elimination based on spike morphological feature. Based on the testing EEG data set adopted in this brief, the sensitivity (Sen), specificity (Spe) and accuracy (AC) of the proposed algorithm reaches 98.2%, 95.1% and 96.5%, respectively.

关键词： Electroencephalography Feature extraction Clustering algorithms Signal processing algorithms detection algorithms Pediatrics Morphology BECT spikes PSO algorithm FPS elimination spike morphological feature

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NeSiFC: Neighbors' Similarity-Based Fuzzy Community detection Using Modified Local Random Walk

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 10014-10026页

作者： Roy, Uttam K. Muhuri, Pranab K. Biswas, Sajib K. South Asian Univ Dept Comp Sci New Delhi 110021 India

This article proposes a neighbors' similarity-based fuzzy community detection (FCD) method, which we call ``NeSiFC.'' In the proposed NeSiFC approach, we compute the similarity between two neighbors by introducing a modified local random walk (mLRW). Basically, in a network, a node and its' neighbors with noticeable similarities among them construct a community. To measure this similarity, we introduce a new metric, called the peripheral similarity index (PSI). This PSI is used to construct the transition probability matrix for the mLRW. The mLRW is applied for each node until it meets a parameter called step coefficient. The mLRW gives better neighbors' similarity for community detection. Finally, a fuzzy membership function is used iteratively to compute the membership degrees for all nodes with reference to existing communities. The proposed NeSiFC has no dependence on the network characteristics, and no adjustment or fine tuning of more than one parameter is needed. To show the efficacy of the proposed NeSiFC approach, we provide a thorough comparative performance analysis considering a set of well-known FCD algorithms viz., the genetic algorithm for fuzzy community detection, membership degree propagation, center-based fuzzy graph clustering, FMM/H2, and FuzAg on a set of popular benchmarks, as well as real-world datasets. For both disjoint and overlapping community structures, results of various accuracy and quality metrics indicate the outstanding performance of our proposed NeSiFC approach. The asymptotic complexity of the proposed NeSiFC is found as O(n(2)).

关键词： detection algorithms Social networking (online) Measurement Tuning Indexes Benchmark testing Complex networks Community detection complex social network analysis fuzzy community detection (FCD) modified local random walk (mLRW) overlapping communities peripheral similarity index (PSI) quality and accuracy metrics

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EEG Peak detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 1548-1556页

作者： Dinh, Tran Hiep Singh, Avinash Kumar Linh Trung, Nguyen Nguyen, Diep N. Lin, Chin-Teng Vietnam Natl Univ Univ Engn & Technol JTIRC Hanoi 100000 Vietnam Univ Technol Sydney Australian Artificial Intelligence Inst Sch Comp Sci Fac Engn & Informat Technol Ultimo NSW 2007 Australia Vietnam Natl Univ Univ Engn & Technol Adv Inst Engn & Technol AVITECH Hanoi 100000 Vietnam Univ Technol Sydney Sch Elect & Data Engn Fac Engn & Informat Technol Ultimo NSW 2007 Australia

Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).

关键词： Electroencephalography Feature extraction Electric potential Task analysis Windows detection algorithms Three-dimensional displays Summit navigator peak detection spike detection electroencephalogram (EEG) cognitive conflict prediction error negativity (PEN) error-related positive potential (Pe) clustering

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detection of defective areas and hidden weak patterns in the wafer using massive measurement data 37

Detection of defective areas and hidden weak patterns in the...

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Conference on Metrology, Inspection, and Process Control XXXVII

作者： Kim, DongHoon Ryu, Sungyoon Jun, Sunhong Han, Heeyoon Choi, Wonjun Jeon, Yong-Ju Lee, Hyun Kim, Souk Sohn, Younghoon SAMSUNG Elect Co Ltd Hwaseong Si Gyeonggi Do South Korea

ISBN: (纸本)9781510660991;9781510661004

As the measurability (sampling capacity, measurement coverage, and measurement speed) of metrology systems are being enhanced to keep pace with the evolution of semiconductor manufacturing processes, the detection of defective areas and hidden weak patterns by analyzing the massive measurement data is becoming significantly important. In this study, we propose new methods to detect defective areas and hidden weak patterns by mathematically processing massive measurement data. By applying the methods we propose, we were able to successfully detect the hidden weak signals of the millimeter scale in the wafer.

关键词： Semiconductor wafer Metrology Data processing detection algorithms Fourier transforms

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Low-Complexity detection for Multidimensional Codebooks Over Fading Channels

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第10期21卷 8063-8076页

作者： Camilo Inacio, Juliana Uchoa-Filho, Bartolomeu F. Le Ruyet, Didier SIDI BR-13098321 Campinas SP Brazil Univ Fed Santa Catarina GPqCom LCS EEL BR-88040900 Florianopolis SC Brazil Conservatoire Natl Arts & Metiers CEDRIC Lab Traitement Signal & Architectures Elec F-75003 Paris France

Signal space diversity (SSD) introduced by Boutros and Viterbo tremendously improves the error performance over fading channels without any power or bandwidth sacrifice. Maximum benefit is obtained with full diversity (FD) multidimensional codebooks and the rather complex maximum likelihood (ML) detection. In the present work, we propose a generalized combinatorial representation and an associated low-complexity list-based detection algorithm that works for any non-full or full diversity multidimensional codebook. The algorithm includes three options of lists offering different complexity-performance trade-offs. One of the lists has random size and is guaranteed to contain the ML point, while the other two lists have fixed or limited size. Some analytical results are presented for the list sizes. The algorithm also includes a smart search to find the most probable codebook point in the list. Numerical results show that the proposed algorithm yields optimal or close-to-optimum performance with a noteworthy detection complexity reduction. Therefore, its adoption can be of practical interest, especially in applications where powerful error-correcting codes are not supported.

关键词： Complexity theory Fading channels Iterative decoding Decoding NOMA Digital video broadcasting detection algorithms Signal space diversity diversity fading multidimensional constellations detection algorithms list detection

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Improved Spike detection Algorithm Based on Multi-Template Matching and Feature Extraction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第1期69卷 249-253页

作者： Jiang, Tiejia Wu, Duanpo Gao, Feng Cao, Jiuwen Dai, Shenyi Liu, Junbiao Li, Yan Zhejiang Univ Childrens Hosp Sch Med Hangzhou 310052 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Artifificial Intelligence Inst Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Machine Learning & I Hlth Int Cooperat Base Zheji Hangzhou 310018 Peoples R China Hangzhou Neuro Sci & Technol Co Ltd Hangzhou 310052 Peoples R China

Spike detection is of great significance in the detection of epileptic seizures. Many spike detection algorithms have been proposed, but available algorithms often miss spikes during high firing rate epochs or with artifacts. Also, there exists methodological limitations when there are great variations in spike morphology between different patients or the same patient in different EEG epoch. Thus a data-driven spike detection method using multi-template matching, feature extraction and threshold method is proposed in this work to improve the detection performance under different circumstances. First, universal template matching and feature extraction are used to get putative spikes. Then clustering algorithm and adaptive template matching algorithm are applied to maximize the number of detected spikes for these specific candidate single units obtained from the clustering method. In addition, a data-driven approach based on receiver operating characteristic (ROC) is used for low false identification rate detection. The performance is evaluated on real EEG samples and the evaluating results shows that this algorithm has achieved 97.12% average sensitivity and 0.55 average false negative rate per minute. The least sensitivity is 94.32% and the most false negative rate per minute is 1.23.

关键词： Feature extraction Electroencephalography Scalp Morphology Sensitivity detection algorithms Clustering algorithms Neurophysiology EEG spike detection template matching feature extraction clustering

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