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IEEE Symposium on Intelligent Vehicle

作者： Stefan Leitner M. Jehanzeb Mirza Wei Lin Jakub Micorek Marc Masana Mateusz Kozinski Horst Possegger Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Christian Doppler Laboratory for Semantic 3D Computer Vision Silicon Austria Labs TU Graz - SAL Dependable Embedded Systems Lab

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.

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Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

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computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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Improving Fault Tolerance of LoRaWAN With Predicting Packet Collision

Improving Fault Tolerance of LoRaWAN With Predicting Packet ...

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International CSI computer Conference, CSICC

作者： Reza Rezazadeh Yasser Sedaahat Ismail Ghodsollahee Farzad Azizi Department of Computer Engineering Dependable Distributed Embedded Systems (DDEms) Laboratory Ferdowsi University of Mashhad Mashhad Iran Department of Computer Engineering Amirkabir University of Technology Tehran Iran

the dependability of IIoT functionality is strongly influenced by its communication protocols. One of the most popular communication standards for the Internet of Things, whose features make it attractive in the IIoT field, is LoRaWAN. LoRaWAN can be considered a low-consumption competitor to cellular networks. This protocol has important features such as long-range coverage, low energy consumption, and a low implementation cost. Although the LoRaWAnfeatures make it a good choice for IoT applications, the existence of real-time constraints and a harsh environment in the IIoT field make it a challenge for network designers to guarantee the dependability of IIoT networks with LoRaWAN. Therefore, in this paper, to overcome this challenge, a novel method is presented for improving energy consumption efficiency, latency, and fault tolerance of the LoRaWAN protocol. The evaluation results show that the proposed method improves packet delivery and latency rate by 11% and 21% compared to standard LoRaWAn.

关键词： Energy consumption Fault tolerance Protocols Costs Fault tolerant systems Packet loss Real-time systems

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Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

arXiv

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arXiv 2023年

作者： Leitner, Stefan Mirza, M. Jehanzeb Lin, Wei Micorek, Jakub Masana, Marc Kozinski, Mateusz Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Austria TU Graz SAL Dependable Embedded Systems Lab Silicon Austria Labs Austria

关键词： Object detection

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ActMAD: Activation Matching to Align Distributions for Test-Time-Training

ActMAD: Activation Matching to Align Distributions for Test-...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Jehanzeb Mirza Pol Jané Soneira Wei Lin Mateusz Kozinski Horst Possegger Horst Bischof Institute for Computer Graphics and Vision TU Graz Austria Christian Doppler Laboratory for Embedded Machine Learning Institute of Control Systems KIT Germany Christian Doppler Laboratory for Semantic 3D Computer Vision

Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture-and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance.

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Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA

arXiv

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arXiv 2025年

作者： Nakano, Hiroshi Blachut, Krzysztof Jeziorek, Kamil Wzorek, Piotr Dampfhoffer, Manon Mesquida, Thomas Nishi, Hiroaki Kryjak, Tomasz Dalgaty, Thomas Graduate School of Science and Technology Keio University Tokyo Japan Embedded Vision Systems Group Computer Vision Laboratory AGH University of Krakow Poland CEA-List Université Grenoble Alpes Grenoble France

As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, based on which real-time predictions can be made locally using an AI model. However, a hardware-software approach capable of making low-latency predictions with low power consumption is required. In this paper, we present a hardware implementation of an event-graph neural network for time-series classification. We leverage an artificial cochlea model to convert the input time-series signals into a sparse event-data format that allows the event-graph to drastically reduce the number of calculations relative to other AI methods. We implemented the design on a SoC FPGA and applied it to the real-time processing of the Spiking Heidelberg Digits (SHD) dataset to benchmark our approach against competitive solutions. Our method achieves a floating-point accuracy of 92.7% on the SHD dataset for the base model, which is only 2.4% and 2% less than the state-of-the-art models with over 10× and 67× fewer model parameters, respectively. It also outperforms FPGA-based spiking neural network implementations by 19.3% and 4.5%, achieving 92.3% accuracy for the quantised model while using fewer computational resources and reducing latency. © 2025, CC BY.

关键词： Graph neural networks

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A New Compact Cantor Set Fractal MIMO Antenna for 5G Millimeter-Wave Applications

A New Compact Cantor Set Fractal MIMO Antenna for 5G Millime...

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Microwave and Antenna Symposium (IMAS), International

作者： Omaima Benkhadda Abdelati Reha Mohamed Saih Meryama Harrou Laboratory of Mathematics Computer Science Electrical Engineering and Physics (LAMIGEP) EMSI-Marrakech Marrakech Morocco Team of Microelectronics Embedded Systems and Telecommunication FST Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (数字)9798350356359

ISBN: (纸本)9798350356366

This article outlines the development of a compact MIMO antenna with two ports crafted for millimeter-wave band operations, specifically tailored to support fifth-generation (5G) wireless applications. The suggested antenna is constructed on a Rogers RT/duroid 5880 substrate, measuring ${12} \times {6}\times {0.508} \text{mm}{3}$ . The proposed antenna has a slotted ground on the bottom and two Fractal-shaped radiating elements inspired by the Cantor Set pattern, positioned atop the dielectric material. The suggested antenna covers the millimeter-wave spectrum utilized in 5G, showcasing a wide bandwidth extending from 26.6 GHz to 29.08 GHz and 38.5 to 39.2 GHz. The antenna attains a maximum gain of 7.64 dBi, robust isolation exceeding 26 dB and demonstrates exceptional diversity performance, with an envelope correlation coefficient (ECC) below 0.003 and a diversity gain (DG) surpassing 10 dB. The obtained results Confirm the validity of the designed MIMO antenna's robustness and make it a viable option for 5G wireless equipment.

关键词： Microwave antennas Wireless communication Correlation coefficient Antenna measurements Slot antennas 5G mobile communication Millimeter wave measurements Bandwidth Fractals Substrates

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Robust Beamforming for Downlink Multi-Cell systems: A Bilevel Optimization Perspective

arXiv

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arXiv 2024年

作者： Chen, Xingdi Xiong, Yu Yang, Kai Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming techniques that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Automated System Classification of ECG Heartbeat based on Support Vector Machine and Convolutional Neural Network

Automated System Classification of ECG Heartbeat based on Su...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Sameher Ajili Rym Cheour Mariem Abid Mohamed Abid Richard Hotte Computer & Embedded systems laboratory (Ces.lab) National Engineering School of Sfax (ENIS) University Sfax Computer & Embedded systems laboratory (Ces.lab) Higher Institue of Applied Science and Technology of Kasserine (ISSAT) University Kairaoun LICEF Institute TELUQ University Applicare.ai Montréal QC Canada Applied Artificial Intelligence Institute (I2A) TELUQ University Montréal QC Canada

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Detecting cardiac arrhythmias is a significant public health concern, but manual identification and classification of arrhythmias using electrocardiogram (ECG) signals is a challenging task. In this paper, we proposed an automatic arrhythmia classification system using Machine Learning and Deep Learning algorithms. The proposed pipeline used a wavelet transform technique to eliminate signal noise. Morphological and statistical features were extracted to classify ECG signal beats into five classes: normal,ventricular, supraven-tricular, fusion, and paced beats. The system was evaluated on the MIT-BIH arrhythmia database using two classification algorithms, SVM and CNN, achieving accuracy rates of 99.34% and 99.72%, respectively.

关键词： Support vector machines Wavelet transforms Accuracy Databases Arrhythmia Electrocardiography Feature extraction

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