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Comparison of Deep Learning Classifier Models for sensor Array based Acoustic Source Localisation 1

Comparison of Deep Learning Classifier Models for Sensor Arr...

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1st international conference on Trends in Engineering systems and Technologies, ICTEst 2024

作者： Rashida, K. Pillai, Minu A. Supriya, M.H. Cochin University of Science and Technology Department of Electronics Kerala Ernakulam India Govt. Model Engineering College Department of Electronics Kerala Ernakulam India Indian Insititute of Information Technology Department of Electronics and Communication Engineering Kerala Kottayam India

ISBN: (纸本)9798350307795

Convolutional Neural Networks (CNNs) and Feed Forward Neural Networks (FFNNs) are used as classifier models for microphone array-based acoustic source localization. A microphone array captures spatial information of sound sources, enabling accurate localization through signal processing and deep learning techniques. A dataset comprising audio recordings from indoor environment, containing a single sound source was collected. The dataset was preprocessed to extract relevant features, and the localization ground truth was obtained using known source positions. Both CNN and FFNN models are implemented to learn the complex mapping between acoustic features and source locations. The CNN model leverages the inherent spatial relationships in the microphone array data through convolutional layers, capturing hierarchical representations of the input. On the other hand, the FFNN model processes the features sequentially through fully connected layers. Both the models are trained, validated, and tested using the data collected from a consistent experimental setup. The results demonstrate the comparative performance of CNNs and FFNNs in terms of localization accuracy, computational efficiency, and robustness to environmental variations. The strengths and limitations of each model are discussed and provide insights into their suitability for different acoustic source localization scenarios. This research contributes to the ongoing exploration of neural network architectures for improving the accuracy and efficiency of acoustic source localization systems. The findings offer valuable guidance for selecting appropriate models based on specific application requirements and computational considerations. © 2024 ieee.

关键词： Microphone array

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Leveraging Ultra-Law-Power Wearables Using distributed Neural Networks 20

Leveraging Ultra-Law-Power Wearables Using Distributed Neura...

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20th ieee international conference on Body sensor Networks, BSN 2024

作者： Chowdhury, Meghna Roy Ghosh, Archisman Bari, Md Faizul Sen, Shreyas

ISBN: (纸本)9798331530143

Traditional deep learning models incur high computational overhead (approximately 1-30W) and are unsuitable for Ultra-Low-Power (ULP) wearable systems like smart glasses. In contrast, TinyML architectures are power-efficient but less accurate. We propose distributing a neural network (NN) between a ULP wearable node and a resource-rich hub to maintain high accuracy and low power consumption for applications like human-machine vision. Output features from the node are transmitted to the hub via low-power communication, where the remaining network runs on traditional GPUs. We introduce a Figure of Merit (FoM) to determine the optimal NN distribution point and a customized multiplier unit for ULP operation. Achieving ULP of 284μW and 9mW for different Autoencoder (AE) networks, our approach is 700× lower in power consumption compared to traditional GPU implementation. © 2024 ieee.

关键词： Autoencoder GPU Neural Network power-efficient MAC tinyML ultra-low power wearables

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Civil engineering structure monitoring based on Integrated sensor fusion device with an optimized mathematical model 1

Civil engineering structure monitoring based on Integrated s...

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1st ieee international conference on Emerging Research in Computational Science, ICERCS 2023

作者： Ibrahim, Ameer Kaissar Fouad, Laith Ali Hussien, Naseer Hani Sherif, Noora Ali, Aqeel National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq

ISBN: (纸本)9798350359756

Integrated sensor fusion is a novel method in which data from various sensors is intelligently fused to enable software that enhances the performance of an application or an entire system. This technique uses a network of sensors to overcome individual sensors' limitations to provide accurate position and orientation data. It is possible to quantify the state of an entity as a data or information mixture by characterizing the merging of data. Increased confidence, dependability, and simplicity in assessing company conditions via engineering systems are only some of the benefits of this interdisciplinary area. In this research, the potential aids of data fusion in civil engineering (CE), as well as some of the more recent implementations of fusion in CE. When problems arise in an application, mathematical modeling (MM) reduces them to tractable mathematical formulations that may be used for theoretical and numerical analysis. Building planning issues were addressed using an integer linear programming mathematical model. The advent of pervasive sensor networks and communications systems has enabled MMCE to use this methodology to rapidly and accurately estimate civil systems, including traffic networks, structural systems, and construction projects. The system can quickly arrive at information-based decisions by checking its health. The implications of this are many, and the value is enormous. Information fusion is a crucial part of monitoring system health. Future applications and research requirements are highlighted. © 2023 ieee.

关键词： sensor networks

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Analyzing Data Traffic in IoT Networks: A Novel Graph-Based Architecture 5th

Analyzing Data Traffic in IoT Networks: A Novel Graph-Based ...

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5th international conference on computing and Network Communications, CoCoNet 2023

作者： George, Gemini Department of Computer Applications St. George’s College Aruvithura Erattupetta Kerala Kottayam686122 India

ISBN: (纸本)9789819745395

The rapid deployment of IoT networks in different industrial services has caused the emanation of a huge volume of data from sensors and monitors. The efficient analysis and compact representation of the big data generated are critical to managing its transfer, storage, and latency. In this work, we propose a novel graphical model that has the flexibility to adapt to different architectural features of distributed IoT systems with low-power sensors. Further, we consider the traffic load minimization problem in the network and map the problems of network coverage and clustering onto the graph to find an optimal distributed network structure that minimizes the traffic load. The proposed approaches are tested under different network conditions, and the simulation results emphasize the improved performance of the method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph based architecture IoT Network construction sensor network

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Routing Protocols Focussed on Energy-Efficiency for Wireless sensor Networks: A Comparative Analysis 1

Routing Protocols Focussed on Energy-Efficiency for Wireless...

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1st ieee international conference on Emerging Research in Computational Science, ICERCS 2023

作者： Sajitha, M. Veni, S. Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

For dependable surveillance of various surroundings concerning civilian and defense purposes and to cooperate transmit the information they collect across the wireless network to a 'Base station (BS)', a 'Wireless sensor Network (WSN)' is made up of geographically dispersed self-governing 'sensor Nodes (SNs)'. The efficiency of energy is a major concern for SNs since their batteries aren't able to last a long time. The major problem with WSN networks revolves around the severe drain on energy resources caused by the transmission of data from the source SN to the intended SNs. The aggregate volume of energy used by WSN systems is significantly influenced by their routing methodologies. Due to their fundamental properties, WSN systems need energy-efficient routing approaches. The limited availability of resources in WSNs has rendered it crucial to find ways to extend the network's useful life. Energy savings and system longevity are currently being enhanced using a technique based on clustering. Several researchers are working on a clustering process that uses minimal energy. This study makes an effort to analyze three relatively new routing protocols, 'ADEM', 'DRAC', and 'DMSSS', which possess the potential to significantly affect the networks' total energy consumption. The metrics "Energy-Efficiency", "Packet-Delivery-Ratio (PDR)", 'Throughput', and "Routing-Overhead"were used to compare the aforementioned protocols. The purpose of this comparative article is to compare and contrast the above protocols currently in use to determine which one is superior in terms of providing higher energy-efficiency. © 2023 ieee.

关键词： Energy efficiency

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Multiplierless In-filter computing for tinyML Platforms 37

Multiplierless In-filter Computing for tinyML Platforms

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37th international conference on VLSI Design (VLSID) / 23rd international conference on Embedded systems (ES)

作者： Nair, A. R. Nath, P. K. Chakrabartty, S. Thakur, C. S. Indian Inst Sci Dept Elect Syst Engn Bangalore 560012 Karnataka India Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA Pandit Deendayal Energy Univ Dept Elect Commun Engn Gandhinagar India

ISBN: (纸本)9798350384406

Wildlife conservation using continuous monitoring of environmental factors and biomedical classification, which generate a vast amount of sensor data, is a challenge due to limited bandwidth in the case of remote monitoring. It becomes critical to have classification where data is generated. We present a novel multiplierless framework for in-filter acoustic classification using Margin Propagation (MP) approximation used in low-power edge devices deployable in remote areas with limited connectivity. The entire design of this classification framework is based on template-based kernel machine, which uses basic primitives like addition/subtraction, shift, and comparator operations, for hardware implementation. Unlike full precision training methods for traditional classification, we use MP-based approximation for training, including backpropagation mitigating approximation errors. The proposed framework is general enough for acoustic classification. However, we demonstrate the hardware friendliness of this framework by implementing a parallel Finite Impulse Response (FIR) filter bank in a kernel machine classifier optimized for a Field Programmable Gate Array (FPGA). The FIR filter acts as the feature extractor and non-linear kernel for the kernel machine implemented using MP approximation. The FPGA implementation on Spartan 7 shows that the MP-approximated in-filter kernel machine is more efficient than traditional classification frameworks with just less than 1K slices.

关键词： IoT FPGA Filtering Edge computing

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Cognitive Intelligence of a Cloud-Based Internet of Things in Precision Agriculture Applications 1

Cognitive Intelligence of a Cloud-Based Internet of Things i...

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1st ieee international conference on Blockchain and distributed systems Security, ICBDS 2022

作者： Keru Patil, Rahul Patil, Suhas Shivlal SVPM's College of Engineering Entc Department Malegaon Bk Pune India Kbp College of Engineering Electronics Department Satara India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

Improvement in Precision Agriculture is the field that is a very crucial requirement for any country on this earth. Using Wireless sensor Network, we can receive and control the agriculture parameters. Arduino microcontroller is used to collect and process the received data and sent it to the cloud for getting current information of land to the individual farmer for taking corrective action for improvement of the yield of crops. Cloud-Based Internet of Thing technologies in Precision Agriculture will be the solution to recover the human being's requirements mostly related to the quality and quantity of foods. This technology will be very useful to a farmer to monitor and control their land any time and anywhere through their smartphone. So the requirements of any country and individual farmers should satisfy with less effort and less cost. © 2022 ieee.

关键词： Wireless sensor networks

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2024 ieee international Symposium on Consumer Technology: Toward Innovation in Consumer Technology for A Sustainable Environment, ISCT 2024 - Proceeding

2024 IEEE International Symposium on Consumer Technology: To...

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1st ieee international Symposium on Consumer Technology, ISCT 2024

ISBN: (纸本)9798350365191

The proceedings contain 127 papers. The topics discussed include: wireless edge controller for robot education using MQTT on dobot magician lite;a PID control algorithm for path tracking of a nonholonomic car-like robot;optimizing Latin to Balinese script transliteration: hybrid Jaro Winkler and Damerau Levenshtein methods;productivity improvement through energy efficiency training in manufacturing industry;validation of an academic resilience scale for Indonesian university students;assessing the relationship between regional economic advancement and digital activity;optimizing optical character recognition accuracy in livestock weighting systems through distributed intelligent computer vision;and early warning forecasting of large induction motor in the oil and gas industry from deep learning.

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When Less is More: Achieving Faster Convergence in distributed Edge Machine Learning 31

When Less is More: Achieving Faster Convergence in Distribut...

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31st Annual ieee international conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Basani, Advik Raj Vivek, Siddharth Chaitra Krishna, Advaith Paul, Arnab K. DaSHLab Department of Computer Science and Information Systems BITS Pilani K. K. Birla Goa Campus India

ISBN: (纸本)9798331509095

distributed Machine Learning (DML) on resource-constrained edge devices holds immense potential for real-world applications. However, achieving fast convergence in DML in these heterogeneous environments remains a significant challenge. Traditional frameworks like Bulk Synchronous Parallel (BSP) and Asynchronous stochastic Parallel (ASP) rely on frequent, small updates that incur substantial communication overhead and hinder convergence speed. Furthermore, these frameworks often employ static dataset sizes, neglecting the heterogeneity of edge devices and potentially leading to straggler nodes that slow down the entire training process. The straggler nodes, i.e., edge devices that take significantly longer to process their assigned data chunk – hinder the overall training speed. To address these limitations, this paper proposes Hermes, a novel probabilistic framework for efficient DML on edge devices. This framework leverages a dynamic threshold based on recent test loss behavior to identify statistically significant improvements in the model’s generalization capability, hence transmitting updates only when major improvements are detected, thereby significantly reducing communication overhead. Additionally, Hermes employs dynamic dataset allocation to optimize resource utilization and prevents performance degradation caused by straggler nodes. Our evaluations on a real-world heterogeneous resource-constrained environment demonstrate that Hermes achieves faster convergence compared to state-of-the-art methods, resulting in a remarkable 13.22x reduction in training time and a 62.1% decrease in communication overhead. © 2024 ieee.

关键词： stochastic systems

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distributed Energy Management strategy for Ship Integrated Energy System considering Energy Efficiency Operation Index 1

Distributed Energy Management Strategy for Ship Integrated E...

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1st international conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2022

作者： Shan, Qihe Zhang, Yuxin Ren, Yihao Li, Shuo Liu, Haoran Teng, Fei Dalian Maritime University Navigation College Dalian China Dalian Maritime University Marine Electrical Engineering College Dalian China

ISBN: (纸本)9781665452151

With the extreme increase in the global warming, it is necessary to investigate the energy management problem (EMP) along with the consideration of low carbon emission for the ship-integrated energy system during the period of navigation. Firstly, a ship-integrated energy system model is constructed with renewable generators and conventional fuel-based generators, concerning the economic benefits and social benefits, simultaneously. Furthermore, a fully distributed energy management algorithm is proposed to solve the EMP with a lower energy efficiency operation index (EEOI) and lower operation cost based on the multi-agent system theory. Finally, the effectiveness of the proposed distributed energy management strategy is proved by simulation results. © 2023 ieee.

关键词： Energy efficiency

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