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10th International Conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Al-Wani, Rawaa Al-Naday, Mays School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

ISBN: (纸本)9789897587504

Applications of the Internet of Things (IoT) face challenges related to interoperability and heterogeneity due to variations in data representation formats and the absence of connectivity standards across wireless networks. This has led to the emergence of context-oriented data brokering frameworks, with FIWARE being the most widely adopted. However, such frameworks are not able to differentiate malicious from benign data. Consequently, challenges related to data quality persist, and brokering overlays are susceptible to exploitation for the distribution of malicious data assets. We propose a novel Artificial Intelligence (AI) anomaly detection service that communicates with the FIWARE broker via the Fast Application Programming Interface (FastAPI). The system also uses the Publish/Subscribe (Pub/Sub) model of FIWARE to allow networking between brokers to validate data assets before disseminating them. This is to analyze the overhead that anomaly detection introduces as a cost of the solution. The results show that the solution can detect around 95% malicious data, with an approximate overhead of 12% increase in response time. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Application programs

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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An Extensible Framework for Hierarchical Dataflow Modeling in Spatial Accelerators

An Extensible Framework for Hierarchical Dataflow Modeling i...

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2025 International Conference on electronics, Information, and Communication, ICEIC 2025

作者： Park, Dongho Choi, Su-Il Chonnam National University Department of Electronic Computer Engineering Gwangju Korea Republic of

ISBN: (纸本)9798331510756

Spatial accelerators are a promising architectural paradigm. Several previous studies have focused on modeling the dataflow of spatial accelerators using the polyhedral model. However, these studies have notable limitations: (1) difficulty in representing hierarchical structures and (2) difficulty in extending algorithms for dataflow modeling. To address these limitations, we propose the PolyLink framework, which employs a polyhedral model-based hardware description graph to effectively represent hierarchical structures and data dependencies. Our framework offers several key contributions: (1) representing complex hierarchical structures as graphs, (2) integrating a polyhedral calculator with Diplomacy, a graph-based hardware generator. We demonstrated the effectiveness of PolyLink by applying it to CONV2D operations and analyzing the memory requirements. © 2025 IEEE.

关键词： Data flow graphs

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SLO and Cost-Driven Container Autoscaling on Kubernetes Clusters 15

SLO and Cost-Driven Container Autoscaling on Kubernetes Clus...

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15th International Conference on Cloud Computing and Services Science, CLOSER 2025

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789897587474

Modern web services must meet critical non-functional requirements such as availability, responsiveness, scalability, and reliability, which are formalized through Service Level Agreements (SLAs). These agreements specify Service Level Objectives (SLOs), which define performance targets like uptime, latency, and throughput, essential for ensuring consistent service quality. Failure to meet SLOs can result in penalties and reputational damage. Service providers also face the challenge of avoiding over-provisioning resources, as this leads to unnecessary costs and inefficient resource use. To address this, autoscaling mechanisms dynamically adjust the number of service replicas to match user demand. However, traditional autoscaling solutions typically rely on low-level metrics (e.g., CPU or memory usage), making it difficult for providers to optimize both SLOs and infrastructure costs. This paper proposes an enhanced autoscaling methodology for containerized workloads in Kubernetes clusters, integrating SLOs with a cost-driven autoscaling policy. This approach overcomes the limitations of conventional autoscaling by making more efficient decisions that balance service-level requirements with operational costs, offering a comprehensive solution for managing containerized applications and their infrastructure in Kubernetes environments. The results, obtained by evaluating a prototype of our system in a testbed environment, show significant advantages over the vanilla Kubernetes Horizontal Pod Autoscaler. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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A Passive RFID Temperature Sensor With 0.16°C Resolution, +0.42/-0.83 °C Inaccuracy and 0.234nJ / conversion

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IEEE Sensors Journal 2025年第10期25卷 18136-18144页

作者： Xiao, Yu Li, Songting Yan, Dun Wang, Feng Hunan University College of Computer Science and Electronic Engineering Changsha410082 China National Univ. of Defense Technology College of Aerospace Science and Engineering Changsha410073 China

This paper presents an ultra-low power RFID with an embedded CMOS temperature sensor. The thermometry working flow of the Query01-Query00-Ack sequence is introduced, which is compatible with the EPC GEN-2 standard. A 2k-bits multi-time programmable (MTP) non-volatile memory (NVM) array with standard CMOS process is used to store tag information, tag-identification (TID), electronic product code (EPC), initialization parameter and temperature sensor (TS) data. The proposed rectifier can achieve a wide power dynamic range (PDR) of 21.5 dB with peak power conversion efficiency (PCE) of 65%@-15 dBm under a 100kΩ load. A protection mechanism for reliable reading and writing is presented to ensure proper tag initialization and avoid the ghost tag phenomenon. Time domain digital quantization is adopted to convert the signal that period varies with temperature into a digital signal for low power consumption. The temperature sensor achieves a resolution of 0.16°C and inaccuracy of +0.42/-0.83 °C after one point calibration at 0 °C in the temperature range from -20 °C to 30 °C. When the temperature sensor is powered at 1 V, it can achieve 0.234 nJ of energy per conversion in a conversion time of 1.8ms. The passive RFID temperature sensor occupies an area of 0.5579 mm2 in 0.13um CMOS process. At the frequency of ~910 MHz, measured tag sensitivity can reach -22 dBm and the 3 dB bandwidth is 51.5 MHz. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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UltraADV: An Unsupervised Deep Learning Lightweight Framework for Anomaly Detection in V2X

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IEEE Internet of Things Journal 2025年第9期12卷 12735-12747页

作者： Huang, Weixian Xu, Hongyun Gong, Yujing Liu, Zhikang Li, Feng Lin, Zhihui Hu, Bin-Jie South China University of Technology School of Computer Science and Engineering Guangzhou510006 China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China

Designing anomaly detection systems for vehicle-to-everything (V2X) is a challenge. Deep learning has shown strong advantages in anomaly detection. However, labeling anomalies is often difficult and expensive, and deep neural networks often prioritize performances by incorporating a large number of parameters. Therefore, there is an urgent need for a lightweight anomaly detection framework which can be built with unsupervised learning methods. In this article, we propose a framework, the UltraADV, which is suitable for anomaly detection systems in V2X. In the framework, the autoencoder facilitates an unsupervised setting, while the knowledge distillation transfers the knowledge of a larger network to a smaller network. Ultimately, the compressed model can be deployed onto the resource-constrained devices for real-world applications in V2X. We evaluate the performances of multiple architectures of UltraADV on two publicly available datasets. Our best performing architecture demonstrably surpasses the benchmark in terms of performance metrics, parameter efficiency, and prediction speed. © 2014 IEEE.

关键词： Contrastive Learning

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Low-rank Winograd transformation for 3D convolutional neural networks

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Science China(Information Sciences) 2025年第5期68卷 409-410页

作者： Ziran QIN Mingbao LIN Huabin LIU John SEE Gui ZOU Weiyao LIN School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Skywork AI School of Mathematical and Computer Sciences Heriot-Watt University Malaysia

In recent years,3D convolutional neural networks (CNNs)have achieved improved accuracy across various 3D data processing tasks,such as video understanding,medical image analysis,and point cloud *** success is largely attributed to their ability to effectively extract spatio-temporal and volumetric ***,3D CNNs demand substantial computational resources,primarily because their 3D kernels are computationally *** deployment on real-world devices is often hindered by constraints such as inference runtime,memory limitations,and *** address these challenges,recent studies [1-3] have explored two complementary optimization *** the operator level,fast convolution algorithms such as Winograd convolution [4] replace spatial convolution operations with element-wise products,eliminating redundant multiplications and reducing the computational load of 3D *** the model level,network pruning techniques [5] simplify network complexity by removing unnecessary units,significantly decreasing parameters and computational *** Winograd convolution and network pruning holds promise for enabling deployment on resource-constrained devices.

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A Lightweight Object Detection Algorithm for Remote Sensing Images

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engineering Letters 2025年第3期33卷 704-711页

作者： Hou, Donghao Zhang, Yujun Ren, Jia School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

With the continuous advancement of satellite technology, remote sensing images has been increasingly applied in fields such as urban planning, environmental monitoring, and disaster response. However, remote sensing images often feature small target sizes and complex backgrounds, posing significant computational challenges for object detection tasks. To address this issue, this paper proposes a lightweight remote sensing images object detection algorithm based on YOLOv9. The proposed algorithm incorporates the SimRMB module, which effectively reduces computational complexity while improving the efficiency and accuracy of feature extraction. Through a dynamic attention mechanism, SimRMB is capable of focusing on important regions while minimizing background interference, and by integrating residual learning and skip connections, it ensures the stability of deep networks. To further enhance detection performance, the FasterRepNCSPELAN4 module is introduced, which employs PConv operations to reduce computational load and memory usage. It also utilizes dilated convolutions and DFC attention mechanisms to strengthen feature extraction, thereby increasing the efficiency and accuracy of object detection. Additionally, this study integrates the GhostModuleV2 module, which generates core feature maps and employs lightweight operations to create redundant features, greatly reducing the computational complexity of *** results show that on the SIMD dataset, the improved YOLOv9 model has a parameter size of 167.88 MB and GFLOPs of 208.6. Compared to the baseline YOLOv9 model (parameter size: 194.57 MB, GFLOPs: 239.0), the parameter size is reduced by 13.71%, GFLOPs are reduced by 12.72%, and detection accuracy is improved by 1.4%. These results demonstrate that the proposed lightweight YOLOv9 model effectively reduces computational overhead while maintaining excellent detection performance, providing an efficient solution for object detection tasks in resou

关键词： Satellite imagery

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Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images (Physical and engineering Sciences in Medicine, (2024), 10.1007/s13246-024-01420-1)

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Physical and engineering Sciences in Medicine 2025年第1期48卷 19-20页

作者： Mostafavi, Mohammadreza Ko, Seok-Bum Shokouhi, Shahriar Baradaran Ayatollahi, Ahmad Department of Electrical Engineering Electronic Engineering Iran University of Science and Technology Tehran Iran Division of Biomedical Engineering Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship" to "The generic teacher-student framework for knowledge distillation [33]." In Sharma et al.’s research, two results obtained from the "KNN with ten-fold cross-validation" and "KNN with leave-one-subject-out cross-validation (LOSOCV)" methods are reported as the final results. Unfortunately, we mistakenly attributed the accuracy of one method. While we correctly listed the name of the method used, which is KNN with ten-fold cross-validation, we inadvertently entered the precision of the KNN with leave-one-subject-out cross-validation (LOSOCV) method. Therefore, we updated our results table (Table 7). The correct Table 7 is shown below. (Table presented.) Comparison of the performance of the proposed method with existing methods Study Method Dataset Accuracy Sensitivity Specificity Shim et al. [38] SVM 34 SZ-34 healthy 88.24% – – Siuly et al. [7] EBT – EMD (Empirical Mode Decomposition) 49 SZ-32 healthy 89.59% 0.8976 0.8932 Phang et al. [39] Multi-domain connectome CNN 45 SZ-39 healthy 91.69% 0.9111 0.925 Jahmunah et al. [40] Non-linear features, decision-tree, neural network, SVM 14 SZ and 14 healthy 92.90% 0.9345 0.9224 Akbari et al. [41] Phase space dynamic & graphical features 14 SZ and 14 healthy 94.8% 0.943 0.952 Shalbaf et al. [20] ResNet-18-SVM 14 SZ and 14 healthy 94.84% 0.980 0.9239 Bagherzadeh et al. [42] DenseNet121 model 14 SZ and 14 healthy 96.26% 0.9548 0.9702 Sharma et al. [43] KNN with ten-fold 14 SZ and 14 healthy 99.21% 0.9942 0.9905 LOSOCV 97.20% 0.9649 0.9806 Proposed method CWT—Self distillation- VGG16 14 SZ and 14 healthy 97.81 0.970 0.986 The original article has been corrected. © Australasian College of Physical Scientists and Engineers in Medicine 2024.

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Intelligent Vehicle Monitoring: Distributed Acoustic Sensors Enabled Smart Road Infrastructure

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IEEE Internet of Things Journal 2025年第11期12卷 15211-15223页

作者： Zhong, Ruikang Chiang, Chia-Yen Jaber, Mona Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

A distributed acoustic sensors (DAS) enabled smart vehicle monitoring system is investigated in this paper to detect the type and passenger occupancy of vehicles for intelligent transportation systems (ITSs). Accurate detection of the number of occupants and the vehicle type is critical for ITS to monitor the occupancy of vehicles, improve vehicle operation efficiency, and achieve intelligent traffic management. We have developed several deep learning (DL) algorithms for occupancy detection and vehicle type discrimination according to the signals provided by the DAS sensor. To be more specific, a novel type of basic neural network structure, namely sparse residual (SR) block is proposed, and several DL models are developed for DAS signals based on the basic SR block *** proposed DL approaches are tested using a unique dataset collected from a road experiment. The test results indicate that 1) the proposed sparse residual network (SR-Net) and Alex sparse residual (Alex-SR) network can achieve detection accuracy of over 90%;2) the proposed models exhibit superior convergence, stability, and accuracy than the conventional residual network (ResNet);3) the proposed DL solutions have superiority in terms of complexity and model size compared to benchmarks. © 2014 IEEE.

关键词： Optical fibers Monitoring Roads Optical fiber sensors Optical fiber cables Optical pulses Accuracy Acoustics Transportation Optical transmitters

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