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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Decentralised Biomedical signal Classification using Early Exits 21

Decentralised Biomedical Signal Classification using Early E...

引用

21st ieee Interregional NEWCAS conference (NEWCAS)

作者： Li Xiaolin Vandierendonck, Hans Nikolopoulos, Dimitrios S. Ji, Bo Cardiff, Barry John, Deepu Univ Coll Dublin Dublin Ireland Queens Univ Belfast Belfast Antrim North Ireland Virginia Tech Blacksburg VA USA

ISBN: (纸本)9798350300246

This paper presents a decentralised signal classification approach for data acquired using internet of Things (IoT) wearable sensors. Traditionally, data from IoT sensors are processed in a centralised fashion, and in a single node. This approach has several limitations, such as high energy consumption on the edge sensor, longer response times, etc. We present a distributed processing approach for convolutional neural network (CNN) based classifiers where a single CNN model can be split into multiple sub-networks using early exits. To reduce the transfer of large feature maps between sub-networks, we introduced an encoder-decoder pair at the exit points. Processing of inputs that can be classified with high confidence at an exit point will be terminated early, without needing to traverse the entire network. The initial sub-networks can be deployed on the edge to reduce sensor energy consumption and overall complexity. We also experimented with multiple exit point locations and show that the point of exit can be adjusted for trade-offs between complexity and performance. The proposed system can achieve a sensitivity of 98.45% and an accuracy of 97.55% for electrocardiogram (ECG) classification and save 60% of the data transmitted wirelessly while reducing 38.45% of the complexity.

关键词： ECG classification Arrhythmia Decentralised Inferencing Distributed Network Deep Learning

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Pedestrian Environment Model for Automated Driving 26

Pedestrian Environment Model for Automated Driving

引用

ieee 26th international conference on Intelligent Transportation systems (ITSC)

作者： Holzbock, Adrian Tsaregorodtsev, Alexander Belagiannis, Vasileios Univ Ulm Inst Measurement Control & Microtechnol D-89081 Ulm Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Multimedia Commun & Signal Proc D-91058 Erlangen Germany

ISBN: (纸本)9798350399462

Besides interacting correctly with other vehicles, automated vehicles should also be able to react in a safe manner to vulnerable road users like pedestrians or cyclists. For a safe interaction between pedestrians and automated vehicles, the vehicle must be able to interpret the pedestrian's behavior. Common environment models do not contain information like body poses used to understand the pedestrian's intent. In this work, we propose an environment model that includes the position of the pedestrians as well as their pose information. We only use images from a monocular camera and the vehicle's localization data as input to our pedestrian environment model. We extract the skeletal information with a neural network human pose estimator from the image. Furthermore, we track the skeletons with a simple tracking algorithm based on the Hungarian algorithm and an ego-motion compensation. To obtain the 3D information of the position, we aggregate the data from consecutive frames in conjunction with the vehicle position. We demonstrate our pedestrian environment model on data generated with the CARLA simulator and the nuScenes dataset. Overall, we reach a relative position error of around 16% on both datasets.

关键词： Vehicles

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Scene Text Involved "Text"-to-image Retrieval through Logically Hierarchical Matching

Scene Text Involved "Text"-to-Image Retrieval through Logica...

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Zhou, Xinyu Chen, Huen Zhu, Anna Pan, Wei Wuhan Univ Technol Sch Comp & Artificial Intelligence Wuhan Peoples R China

ISBN: (纸本)9781665468916

Text-to-image retrieval, one of the most important cross-modality tasks, aims to search the most relevant images through a given text query. Most recent approaches are based on large-scale models. The huge time costs make it impossible for real-time searching. They also ignore the fine-grained information, i.e., the scene text. To tackle these issues, we propose a novel matching method that considers the scene text in both modalities and adopts a fast matching way by aligning from objects to relations and finally to the global. This logically hierarchical process emulates the way humans understand information. To better implement our method, we relabel the TextCaps-OCR dataset, which contains 110K captions with word-level POS labeling and 22K corresponding scene text images with bounding boxes. Extensive experiments demonstrate the superiority and efficiency of our method, whose performance is significantly higher than the past SOTA on both OCR-contained and OCR-free datasets.

关键词： Text-to-image retrieval scene text logically hierachical matching

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Energy-Efficient ReRAM-based High-Precision Discrete Fourier Transform for image Reconstruction

Energy-Efficient ReRAM-Based High-Precision Discrete Fourier...

引用

2023 ieee international conference on Integrated Circuits, Technologies and Applications, ICTA 2023

作者： Lu, Fei-Xiong Li, Yi Li, Jian-Cong Xue, Yi-Bai Zhou, Zhi-Wei Miao, Xiang-Shui School of Integrated Circuits Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350344288

In this work, we implemented the discrete Fourier transform (DFT) using a Pt/Al2O3/AlOx/W resistive random-access memory (ReRAM) for high-precision signal processing. By introducing the bit-slicing method, the ReRAM devices are capable of overcoming the imprecise analog computing limitation and can achieve software-comparable processing precision. Validated through the image reconstruction tasks, the ReRAM technology is demonstrated to achieve reconstruction quality virtually equivalent to 8-bit integer precision. Furthermore, this work is evaluated to achieve an energy efficiency of 622 GOPS/W, which is 22× higher than the state-of-the-art digital processors. We expect the ReRAM-technology can be further employed to perform more complex signal processing tasks with high efficiency in practical applications. © 2023 ieee.

关键词： Discrete Fourier transforms

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Deep Learning and Time-Frequency Analysis based Automatic Low Probability of Intercept Radar Waveform Recognition Method 23

Deep Learning and Time-Frequency Analysis Based Automatic Lo...

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23rd ieee international conference on Communication technology, ICCT 2023

作者： Li, Xiangzhen Cai, Zhuoran School of Physics and Electronic Information YanTai University Yantai China

ISBN: (纸本)9798350325959

As a critical component of modern electronic warfare, radar plays an essential role in target detection. Due to the characteristics of low interception, the low probability of interception (LPI) radar has better stealth;on the other hand, it challenges the accurate identification of its signal waveform simultaneously. Existing LPI radar waveform recognition approaches are considered inadequate under sufficient channel conduction. In this paper, we selected 13 radar waveforms under 31 signal-to-noise ratios, utilized the time-frequency analysis method of CWD (Choi-Williams Distribution) transform for signal processing, and used our proposed convolutional neural network LeeNet for classification. The results show that the proposed method can achieve an accuracy higher than 96% when the signal-to-noise ratio is 0 dB while performing better at all signal-to-noise ratios than other deep learning architectures. © 2023 ieee.

关键词： Automatic waveform recognition deep learning LPI radar signal time-frequency analysis

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An Optimized SIMON Lightweight image Encryption Algorithm for internet of Things: Balancing Performance and Security 1

An Optimized SIMON Lightweight Image Encryption Algorithm fo...

引用

1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Verma, Abhay Thokchom, Surmila National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India

ISBN: (纸本)9798350365016

The paper presents an optimized SIMON lightweight cryptographic algorithm that balances security, performance, and cost in contexts with limited resources. By lowering the computational cost and preserving strong security characteristics, the suggested changes make the algorithm appropriate for use in embedded systems, internet of Things (IoT) devices, and mobile platforms. To improve encryption efficiency, changes have been made, such as fewer bit shift operations and the consolidation of bit-wise functions. A thorough evaluation process is applied to the updated algorithm, which involves calculating measures like information entropy, UACI (Unified Average Changing Intensity), and NPCR (Normalized Pixel Change Rate). Empirical findings show that the optimized SIMON algorithm significantly increases encryption efficiency and speed while maintaining the security of the algorithm. © 2024 ieee.

关键词： Encryption algorithms

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Layer-Wise Personalized Federated Learning for DAG Blockchain-Enabled internet of Vehicles 16

Layer-Wise Personalized Federated Learning for DAG Blockchai...

引用

16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Huang, Xiaoge Li, Wenjing Yu, Caiwei Liang, Chengchao Chen, Qianbin School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350390643

In the internet of Vehicles (IoV), personalized federated learning (PFL) can generate personalized models tailored to different local data distributions, thereby improving driving decisions. Existing approaches often perform weighted aggregation on similar vehicles, where the weights are determined by the entire model parameters or loss values. Such model-wise aggre-gation results in inaccurate combination weights, as it overlooks differences in layer-level functionality and imbalances in inter-user accuracy. Additionally, the openness of IoV poses significant threats to system security. In this paper, we propose a blockchain-enabled layer-wise PFL (BLPFL) algorithm to improve system security, model accuracy, and user fairness. Specifically, to preserve personalized knowledge, each local model is split into a globally shared feature extractor and a locally private decision head. To secure the sharing of extractors, Directed Acyclic Graph (DAG)-based blockchain technology is employed. Furthermore, a fine-grained personalized model aggregation mechanism is designed to enhance the model accuracy and user fairness, which explores vehicle similarity at layer granularity and considers special vehicles with severely skewed label distributions. Finally, simulation results demonstrate the effectiveness of the proposed approach in highly heterogeneous scenarios. © 2024 ieee.

关键词： Federated learning

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Deep Learning-based LOS/NLOS Classification for Reliable Communication in Industrial IoT 9

Deep Learning-based LOS/NLOS Classification for Reliable Com...

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9th ieee international conference for Convergence in technology, I2CT 2024

作者： Pradhan, Annapurna Das, Susmita Mati, Gyana Ranjan Xalxo, Ankit Oscar National Institute of Technology Department of Electrical Engineering Rourkela India

ISBN: (纸本)9798350394474

The optimal channel selection for data transmission is essential for reliable communication in an indoor environment like industrial IoT (IIoT). Due to the presence of complex objects in the indoor factory environment, signals might get reflected. This leads to reliability loss and degradation of transmitted signal quality by increasing signal outage. Again, the optimal channel selection for efficient scheduling of the heterogeneous data packets generated by delay-sensitive ultra-reliable low latency (URLLC) service and delay-tolerant broadband service in IIoT demand for accurate identification of wireless link status. Therefore, the identification of wireless channel status like Line-of-Sight (LOS), None-Line-of-Sight (NLOS), and Multi-Path (MP) between the transmitter and the receiver becomes essential to reduce packet error probability in IIoT. In this regard, we propose a deep learning-based classifier model using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) to identify the LOS/NLOS or MP signals and enhance the reliability of the signal by improving system throughput and accurate positioning. We compare the performance of the proposed model with various machine learning classifier models to evaluate the performance of the proposed CNN-LSTM model. We have used an open-source dataset collected from two different indoor industrial sites to be used for the training and testing of the classifier models. We have evaluated the performance based on the accuracy and time complexity of the proposed classifier model, which shows superiority in comparison to baseline machine learning models. Additionally, the results show that the system Bit Error Rate (BER) improved significantly with the optimal channel selection during scheduling of heterogenous data type using the proposed CNN-LSTM model. © 2024 ieee.

关键词： internet of things

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Research on Ubiquitous Power internet of Things in Distribution Networks Using Adaptive Gradient Algorithm with Convolution Neural Network 2

Research on Ubiquitous Power Internet of Things in Distribut...

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2nd ieee international conference on Data Science and Network Security, ICDSNS 2024

作者： Liu, Songmei Wanjiang University of Technology Ma'anshan China

ISBN: (纸本)9798350373110

In China, several distribution automation devices are established in distribution network to increase reliability of power supply. The major challenge of ubiquitous electric internet of Things (IoT) is a resource aspect which enables distributed computational sources. Therefore, the Adaptive Gradient Algorithm with Convolution Neural Network (Adagrad-CNN) is proposed for ubiquitous electric IoT in distribution networks. The Adagrad is helpful when dealing with sparse features or data that has extensive-range of values. The CNN has multiple-layer architecture for classifying image pixels patterns through processing and it filtered noise captures significant predictive properties. The Adagrad-CNN leads to better generalization and enhance the CNN performance through mitigating issues based on exploding gradients that is essential when dealing with difficult and large datasets in ubiquitous electric IoT. The dataset is collected from various devices and sensors in the power grid and it is preprocessed by Z-score normalization. This preprocessing enhances convergence rates and reduces the large scales. It is essential when features are in different ranges and it prevents particular feature dominance. The Adagrad-CNN attains 96.54%, 94.21 %, 95.73% and 94.59% of accuracy, recall, precision and f1-score. © 2024 ieee.

关键词： Power distribution networks

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Thermal Infrared image Inpainting Via Edge-Aware Guidance 48

Thermal Infrared Image Inpainting Via Edge-Aware Guidance

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48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Wang, Zeyu Shen, Haibin Men, Changyou Sun, Quan Huang, Kejie Zhejiang University Hangzhou China Hangzhou Vango Technologies Inc. Hangzhou China

ISBN: (纸本)9781728163277

image inpainting has achieved fundamental advances with deep learning. However, almost all existing inpainting methods aim to process natural images, while few target Thermal Infrared (TIR) images, which have widespread applications. When applied to TIR images, conventional inpainting methods usually generate distorted or blurry content. In this paper, we propose a novel task - Thermal Infrared image Inpainting, which aims to reconstruct missing regions of TIR images. Crucially, we propose a novel deep-learning-based model TIR-Fill. We adopt the edge generator to complete the canny edges of broken TIR images. The completed edges are projected to the normalization weights and biases to enhance edge awareness of the model. In addition, a refinement network based on gated convolution is employed to improve TIR image consistency. The experiments demonstrate that our method outperforms state-of-the-art image inpainting approaches on FLIR thermal dataset. © 2023 ieee.

关键词： Convolution

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