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The Confluence of Edge Computing and Artificial Neural Networks for Real-time Data processing in IoT Devices

The Confluence of Edge Computing and Artificial Neural Netwo...

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ieee international conference on System, Computation, Automation and Networking (ICSCAN)

作者： Bhuvana Jayabalan Chetan Chaudhary Renuka Bhagwat Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

In this study, we present a novel approach for efficient real-time data processing in IoT devices at the edge by dynamically adapting artificial neural networks (ANNs) based on available computational resources and input data complexity. We address the challenges of resource constraints and latency reduction, crucial for enabling seamless and responsive IoT applications. Our proposed method comprises three main algorithms: Dynamic Neural Network Adaptation (DNNA), Edge Inference Scheduler (EIS), and Latency-aware Activation Thresholding (LAT). Algorithm 1, DNNA, is responsible for dynamically adjusting the complexity of the deployed ANN to match the available resources and data complexity. It optimizes the trade-off between computational efficiency and model accuracy, making the best use of limited edge resources. Algorithm 2, EIS, optimizes the allocation of computational resources while minimizing latency during task scheduling on edge devices. It ensures that tasks are assigned to devices that can meet latency requirements, improving overall system responsiveness. Algorithm 3, LAT, dynamically adjusts activation thresholds within ANNs based on predicted latency, allowing for a fine balance between accuracy and processing speed. This adaptation ensures that the ANN performs optimally under varying conditions. Our experimental results demonstrate the superiority of the proposed method over traditional approaches in terms of latency, inference accuracy, energy efficiency, scalability, privacy, security, resource utilization, and overall efficiency. The proposed method not only reduces latency but also maintains high accuracy levels, making it a valuable solution for real-time data processing in IoT devices at the edge.

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Printed Mathematical Expression Recognition from Document images Based on Augmentation Techniques and Transformer

Printed Mathematical Expression Recognition from Document Im...

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ieee international conference on Research, Innovation and Vision for the Future

作者： Tran-Huy Le Minh-Dzung Vo Huu-Hung Nguyen Thuy-Binh Nguyen Hong-Quan Nguyen Thi-Lan Le School of Electrical and Electronic Engineering (SEEE) Hanoi University of Science and Technology (HUST) Hanoi Vietnam Faculty of Electrical- Electronic Engineering University of Transport and Communications Hanoi Vietnam Faculty of Information Technology Viet-Hung Industrial University Hanoi Vietnam MICA International Research Institute Hanoi University of Science and Technology Hanoi Vietnam

Mathematical expression recognition with the aim to convert from images to Latex sequence has emerged recently thanks to its potential applications. Transformers have shown its superior performance in different computer vision tasks including mathematical expression recognition. However, one drawback of the transformer is to be lack of coverage information, useful cues for image-text alignment. Inspired from the previous work that integrated coverage information in the decoder of transformer for handwritten mathematical expression recognition, in this paper, we propose a novel framework which adopts expression augmentation step into the above mentioned model for printed mathematical expression recognition. Ablation study shows that the use of data augmentation allows to improve mathematical expression recognition performance. The proposed method obtained the state of the art methods on the subset of Marmot dataset that is prepared for ME recognition evaluation. The values of ExpRate, <=1 error, <=2 error, and WER are 70.72%, 73.21%, 81.31%, and 4.10%.

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Joint Task Scheduling and Container image Caching in Edge Computing

Joint Task Scheduling and Container Image Caching in Edge Co...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Fangyi Mou Zhiqing Tang Jiong Lou Jianxiong Guo Wenhua Wang Tian Wang Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Institute of Artificial Intelligence and Future Networks Beijing Normal University China Yunnan Key Laboratory of Software Engineering Kunming Yunnan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

关键词： Processor scheduling Computational modeling Markov decision processes image edge detection Containers Dynamic scheduling Delays

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Channel Estimation Using RIDNet Assisted OMP for Hybrid-Field THz Massive MIMO Systems

Channel Estimation Using RIDNet Assisted OMP for Hybrid-Fiel...

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ieee international conference on Communications (ICC)

作者： Hasan Nayir Erhan Karakoca Ali Görçin Khalid Qaraqe Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electronics and Communication Engineering Istanbul Technical University İstanbul Turkey Communications and Signal Processing Research (HİSAR) Lab. TÜBİTAK BİLGEM Kocaeli Turkey Department of Electronics and Communication Engineering Yildiz Technical University İstanbul Turkey

The terahertz (THz) band radio access with larger available bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modeling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a real image denoising network (RIDNet) and orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including far-field and near-field constituents. The simulation experiments are performed using the ray-tracing tool. The results demonstrate that the proposed RIDNet-based method consistently provides lower channel estimation errors than the conventional OMP algorithm for all signal-to-noise ratio (SNR) regions. The performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.

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Survey on measures of image similarity applied in image inpainting base on deep learning 22

Survey on measures of image similarity applied in image inpa...

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Proceedings of the 2022 5th international conference on Algorithms, Computing and Artificial Intelligence

作者： Libo He Zhenping Qiang Yunyun Wu Meijiao Wang Information Security College Yunnan Police College China College of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (纸本)9781450398336

The research of image similarity measures is an important content of the computer image processing. It have been widely studied and applied in image inpainting, image super resolution, image matching and other applications. In these application, the choice of image similarity measure has a direct impact on the final results. In order to enable more researchers to explore the image similarity theory and its specific role in different applications, this paper summarizes the research status in this field. This paper first classifies the existing image similarity evaluation methods from two different perspectives. Next, since the performance of image similarity measure is closely related to its application field, so we select the image inpainting field based on depth learning, and illustrate the application of image similarity measurement methods in this field. Then we roughly compare the advantage and disadvantage of these methods. Finally, we gave our conclusions and prospects.

关键词： distance and similarity of vectors

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Wireless Sensors with Edge Deep Learning for Detecting and Alerting the Freezing of Gait Symptoms in Parkinson's Patients*

Wireless Sensors with Edge Deep Learning for Detecting and A...

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international ieee/EMBS conference on Neural Engineering, CNE

作者： Ourong Lin Tian Yu Yuhan Hou Yi Zhu Xilin Liu Department of Electrical and Computer Engineering (ECE) University of Toronto Toronto ON Canada Toronto Rehabilitation Institute University Health Network (UHN) Toronto ON Canada

This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. A novel button pin type sensor node design was developed for easy attachment. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at their ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a convolutional neural network (CNN). The DL model outputs from the three sensor nodes are processed in a central node using a majority voting algorithm. In a validation using a public dataset, the prototype developed achieved an FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20k trainable parameters per sensor node. Once FoG is detected, an auditory signal will be generated to alert users, and the alarm signal will also be sent to mobile phones for further actions if needed. The sensor node can be easily recharged wirelessly by inductive coupling. The system is self-contained and processes all user data locally without streaming data to external devices or the cloud, thus eliminating the cybersecurity risks and power penalty associated with wireless data transmission. The developed methodology can be used in a wide range of applications.

关键词： Wireless communication Deep learning Wireless sensor networks image edge detection Sociology Thigh Data models

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Federated Learning based on Feature Transfer: Multi-center Nuclear Segmentation of Histological images

Federated Learning based on Feature Transfer: Multi-center N...

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Frontiers Technology of Information and computer (ICFTIC), ieee international conference on

作者： Senliang Lu Bao Feng Yehang Chen Zijun Huang School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China Laboratory of Intelligent Detection and Information Processing Guilin University of Aerospace Technology Guilin City Guangxi Province China

Federated learning has enabled multiple medical data centers to collaboratively train segmentation models without sharing data. However, the existence of Non-IID data across different data centers leads to poor model generalization. To address this issue, we propose a personalized federated learning approach based on feature transfer, suitable for multi-center medical image segmentation tasks. Firstly, to construct personalized local segmentation models and reduce the influence of heterogeneous feature information in the global segmentation model, we employ a meta-network to automatically identify and select transferable features from the encoding layers of U-Net. It generates feature transfer weights and constructs a feature transfer loss to enhance the local models’ attention to informative features. Secondly, to filter out heterogeneous feature information from each local model and enable the global model to identify and transfer robust features from local models, the meta-network computes feature transfer weights based on the representations of each local model and construct an aggregation loss. This loss is then used to update the feature transfer weights based on the variation of soft dice loss of the global segmentation model. Experimental evaluations are conducted on six central datasets for cell nuclei segmentation tasks, and comparisons are made with three existing federated learning algorithms. The experimental results demonstrate that the proposed method achieves the highest dice coefficient in centers A, B, C, E, and F, with a nearly 10% improvement in the dice coefficient for center F.

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Implementation of Braun and Baugh-Wooley Multipliers Using QCA

Implementation of Braun and Baugh-Wooley Multipliers Using Q...

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Innovation in Technology (INOCON), ieee international conference for

作者： P. Kishore Rohan Sirimalla K. Sai Sushma R. Saikiran Reddy Dept. of ECE VNRVJIET Hyderabad India ECE VNRVJIET Hyderabad India

Matrix multiplication is a key element in digital signal processing systems, as well as a repetitive procedure in several signal processing and computing tasks. The circuit complexity is mostly determined by the number of multiplications necessary to create the system. A parallel array multiplier is a method for meeting high execution speed requirements. In the last step of a standard Braun multiplier, there is an assembly containing 16 AND gates, 9 Full Adders, as well as a ripple carry adder (RCA). Researchers studied other 4nano devices as an alternative to CMOS since circuits are constrained by technological scalability. Quantum-dot Cellular Automata (QCA) system is really a viable alternative to CMOS technology in a variety of nano devices. It is appealing because of its quick speed, small size, with low power consumption. The creation of a small and high-speed Baugh Wooley multiplier utilizing Quantum Dot Cellular Automata is presented in this study. In DSP processors, multipliers are the fundamental building elements of many calculations. Various adder as well as multiplier models on QCA have already been presented, however there has been little effort done on signed multiplication. This paper utilizes the unique QCA characteristics to design a Baugh-Wooley Multiplier that is fast and efficient to implement both signed and unsigned multiplication and comparison will be done with present implemented multipliers. Multiplication is the basic building block for several DSP processors, image processing and many other. QCA Designer is used to display simulation results. The computational complexity of algorithms employed inside Digital Signal Processors (DSPs) has significantly grown over time. Braun design is a simulation model of a similar array multiplier. A parallel array multiplier is a form of Braun multiplier. Braun multiplier architecture comprises mainly multiple Carry Save Adders, an array of AND gates, and a Ripple Carry Adder.

关键词： Program processors Power demand Array signal processing Quantum dots Digital signal processors computer architecture Logic gates

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PS3DT: Synthetic Speech Detection Using Patched Spectrogram Transformer

PS3DT: Synthetic Speech Detection Using Patched Spectrogram ...

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international conference on Machine Learning and applications (ICMLA)

作者： Amit Kumar Singh Yadav Ziyue Xiang Kratika Bhagtani Paolo Bestagini Stefano Tubaro Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

Many deep learning synthetic speech generation tools are readily available. The use of synthetic speech has caused financial fraud, impersonation of people, and misinformation to spread. For this reason forensic methods that can detect synthetic speech have been proposed. Existing methods often overfit on one dataset and their performance reduces substantially in practical scenarios such as detecting synthetic speech shared on social platforms. In this paper we propose, Patched Spectrogram Synthetic Speech Detection Transformer (PS3DT), a synthetic speech detector that converts a time domain speech signal to a mel-spectrogram and processes it in patches using a trans-former neural network. We evaluate the detection performance of PS3DT on ASVspoof2019 dataset. Our experiments show that PS3DT performs well on ASVspoof2019 dataset compared to other approaches using spectrogram for synthetic speech detection. We also investigate generalization performance of PS3DT on In-the-Wild dataset. PS3DT generalizes well than several existing methods on detecting synthetic speech from an out-of-distribution dataset. We also evaluate robustness of PS3DT to detect telephone quality synthetic speech and synthetic speech shared on social platforms (compressed speech). PS3DT is robust to compression and can detect telephone quality synthetic speech better than several existing methods.

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Dual Knowledge Distillation for Efficient Sound Event Detection

Dual Knowledge Distillation for Efficient Sound Event Detect...

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), ieee international conference on

作者： Yang Xiao Rohan Kumar Das Fortemedia Singapore Singapore

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Sound event detection (SED) is essential for recognizing specific sounds and their temporal locations within acoustic signals. This becomes challenging particularly for on-device applications, where computational resources are limited. To address this issue, we introduce a novel framework referred to as dual knowledge distillation for developing efficient SED systems in this work. Our proposed dual knowledge distillation commences with temporal-averaging knowledge distillation (TAKD), utilizing a mean student model derived from the temporal averaging of the student model’s parameters. This allows the student model to indirectly learn from a pre-trained teacher model, ensuring a stable knowledge distillation. Subsequently, we introduce embedding-enhanced feature distillation (EEFD), which involves incorporating an embedding distillation layer within the student model to bolster contextual learning. On DCASE 2023 Task 4A public evaluation dataset, our proposed SED system with dual knowledge distillation having merely one-third of the baseline model’s parameters, demonstrates superior performance in terms of PSDS1 and PSDS2. This highlights the importance of proposed dual knowledge distillation for compact SED systems, which can be ideal for edge devices.

关键词： Performance evaluation Event detection Databases image edge detection Signal processing Acoustics Stability analysis

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