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

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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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI Visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Ziyu Pan, Tengyu Li, Zhenyu Wu, Ji Li, Xiuxing Wang, Jianyong Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350368741

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask. © 2025 IEEE.

关键词： Computational neuroscience multi-modal learning region of interest analysis small sample learning

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Smart Parking System using YOLO, ANPR and MQTT with Artificial Intelligence Features 3

Smart Parking System using YOLO, ANPR and MQTT with Artifici...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yadav, Krrish Roy, Sritama School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798331527549

Smart parking systems leverage advanced technologies to optimize parking space utilization and enhance user experience. This research paper explores the design, implementation, and evaluation of a smart parking system aimed at improving accessibility and security in urban parking facilities. It utilizes real-time sensor data to provide accurate parking availability information and empowers users to book slots through a secure mobile web app. The app integrates user-friendly navigation to guide drivers to their booked spots. OTP generation bolsters security, preventing unauthorized parking occupancy. One of the most significant and difficult problems in image-based sequence recognition is scene text recognition, which is the focus of this study's investigation. A brand-new neural network architecture is put forth that unifies transcription, sequence modelling, and feature extraction into a single framework. It performs admirably in both lexicon-free and lexicon-based scene text recognition tasks and is not restricted to any predefined lexicon. It produces a much more manageable and effective model, making it more suitable for real-world application scenarios. © 2025 IEEE.

关键词： App development Neural network OTP generation Smart parking text recognition

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Causalseg: investigating causality modeling for semi-supervised video object segmentation

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Multimedia Systems 2025年第3期31卷 1-16页

作者： Zhang, Zhengjin Li, Nannan Wang, Wenmin Guo, Huiwen School of Computer Science and Engineering Macau University of Science and Technology Taipa999078 China School of Computer and Artificial Intelligence Chaohu University Bantang Road Anhui Hefei238024 China Shenzhen Bochuang Robot Technology Xixiang Guangdong Shenzhen518172 China

In Semi-Supervised Video Object Segmentation (SVOS), existing methods are often hindered by the issue of spurious association due to the lack of adequate supervision information. This issue negatively impacts the segmentation performance. To address this, we introduce a novel approach that leverages causal intervention to enhance SVOS performance. Causal intervention is particularly effective in mitigating spurious associations, thereby improving segmentation performance. Our proposed causal module is a versatile component that can be seamlessly integrated into matching-based SVOS to enhance segmentation accuracy. Our approach involves three key steps. Firstly, we introduce the Causal CAM method to identify and eliminate spurious association locations within the video frame. Secondly, we adapt the IRNet to effectively generate class-specific instance map information during training. Lastly, we incorporate the AFB-URR, STCN and XMem models with our causal intervention method. Experimental results validate the effectiveness of our approach in improving object segmentation accuracy. On the DAVIS2017 dataset, our method achieved an overall J & F Mean scores of 76.3%, 85.3% and 87.5% respectively. Compared to the three benchmark methods, our approach demonstrated improvements of 1.7%, 3.4% and 1.3% respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image segmentation

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Dynamic topology adaptability of adaptive multimodal transmission strategy based on environment perception in multi-hop routing of 5G vehicle network

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Tan, Shengxia Zong, Xianshuang Xiao, Feng School of Computer and Information Technology Anhui Vocational and Technical College Anhui Hefei230031 China School of Electrical Engineering Anhui Technical College Of Mechanical and Electrical Engineering Anhui Wuhu241002 China

In multi-hop routing of 5G vehicle network, node movement and link failure often lead to frequent changes in network topology, which in turn cause delay and packet loss problems. A multimodal transmission strategy based on environment perception is proposed in this study to achieve adaptive switching of routing protocols. Vehicle-mounted sensors are used to collect location, speed, network load and traffic density information in real-time. Then, graph sample and aggregation (GraphSAGE) is used to process the network topology and extract node and edge features of the data. The features are input into gated recurrent unit (GRU) for time series modeling to predict the link node movement pattern. Then, ad hoc on-demand distance vector routing (AODV), greedy perimeter stateless routing (GPSR) and dynamic source routing (DSR) are integrated, and the best protocol is automatically selected through the classification and regression trees (CART) algorithm. Finally, a route request packet (RREQ) is generated and broadcast according to the selected routing protocol. After being processed by the intermediate node, the route reply packet (RREP) is returned to the source node to achieve real-time update of the routing table and maintain the link status. The results show that when the number of nodes reaches 1200, the system delay is 146.99 ms;when the average vehicle speed is 30 km/h and the network load is 50 Mbps, the packet loss rate of switching to the DSR protocol is only 0.833%. The method can significantly reduce the real-time delay in multi-hop routing of 5G vehicle network. Clinical trial number: Not applicable. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Integrating deep and handcrafted features for enhanced decision-making assistance in breast cancer diagnosis on ultrasound images

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Multimedia Tools and Applications 2025年 1-23页

作者： Abhisheka, Barsha Biswas, Saroj Kumar Purkayastha, Biswajit Das, Soumen School of Computer Science UPES Bidholi Uttrakhand Dehradun248007 India Computer Science Engineering National Institute of Technology Silchar NIT Road Assam Silchar788010 India

The classification of breast cancer has emerged as a significant concern in the healthcare sector in recent times. This is primarily due to its status as the second leading cause of cancer-related fatalities among women. Prior research in the field of breast cancer classification has mainly focused on either traditional Machine Learning (ML) based models or employing Deep Learning (DL) models. However, both approaches solely fail to capture the complete spectrum of features necessary for accurate classification. Therefore, this paper introduces a classification model named Hybrid Breast Cancer Prediction System (HBCPS), which utilizes a combination of features derived from deep Convolutional Neural Network (CNN) model and handcrafted features, to improve classification performance. The proposed HBCPS uses the pre-trained ResNet50 network to extract the deep features, while the handcrafted features are obtained using Histogram Orientation Gradient (HOG). For classification the proposed approach uses Support Vector Machine (SVM). In addition, the proposed method integrates the Block Matching 3D(BM3D) denoising filter. This filter efficiently reduces multiplicative noise such as speckle noise from Breast Ultrasound (BUS) images, resulting in improved image quality. This enhancement technique has significantly contributed to improve the overall performance of the system. The proposed framework is evaluated using a widely used Breast Ultrasound Image (BUSI) dataset. The proposed HBCPS exhibited satisfactory performance with an accuracy, precision, recall, F1-score, specificity, and AUC values of 89.02%, 87.67%, 87.17%, 87.36%, 83.87%, and 0.8717, respectively. These metrics indicate the robustness and reliability of the proposed method and offer a comprehensive framework for breast cancer classification using BUS images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1191-1237页

作者： Liu, Yan Guo, Bin Li, Nuo Ding, Yasan Zhang, Zhouyangzi Yu, Zhiwen Northwestern Polytechnical University School of Computer Science Shaanxi Xi’an710129 China Harbin Engineering University School of Computer Science and Technology Heilongjiang Harbin150001 China Northwestern Polytechnical University Department of Computer Science Shaanxi Xi’an710129 China

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of AIoT is to establish a self-organizing, self-learning, self-adaptive, and continuous-evolving AIoT system by orchestrating intelligent connections among Humans, Machines, and IoT devices. Although advanced deep learning techniques enhance the efficient data processing and intelligent analysis of complex IoT data, they still suffer from notable challenges when deployed to practical AIoT applications, such as constrained resources, dynamic environments, and diverse task requirements. Knowledge transfer, a popular and promising area in machine learning, is an effective method to enhance learning performance by avoiding the exorbitant costs associated with data recollection and model retraining. Notably, although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances of various knowledge transfer techniques for AIoT field. This survey endeavors to introduce a new concept of knowledge transfer, referred to as Crowd Knowledge Transfer (CrowdTransfer), which aims to transfer prior knowledge learned from a crowd of agents to reduce the training cost and as well as improve the performance of the model in real-world complicated scenarios. Particularly, we present four transfer modes from the perspective of crowd intelligence, including derivation, sharing, evolution and fusion modes. Building upon conventional transfer learning methods, we further delve into advanced crowd knowledge transfer models from three perspectives for various AIoT applications: intra-agent knowledge transfer, centralized inter-agent knowledge transfer, and decentralized inter-agent knowledge transfer. Furthermore, we explore some applications of AIoT areas, such as human activity recognition, urban comp

关键词： Internet of things

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Saliency Feature Learning for Multi-modality Person Retrieval in Visual Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 15883-15896页

作者： You, Shuai Feng, Yujian Wang, Shitao Wu, Fei Sha, Yuchen Ji, Yimu Jing, Xiao-Yuan School of Internet of Things Nanjing China Jiangsu University of Technology School of Computer Engineering Changzhou China NJUPT School of Internet of Things Nanjing China NJUPT School of Computer Science Nanjing China NJUPT College of Automation College of Artificial Intelligence Nanjing China Wuhan University School of Computer Wuhan430072 China

In the Visual Internet of Things (VIoT), smart surveillance is an important component of the Multi-modality Person Retrieval (MPR) task. Capturing discriminative pedestrian information from images aids in identifying target individuals through sketch or text descriptions, improving the reliability of VIoT systems. Current granularity-level matching methods face challenges with information redundancy and modality differences in MPR tasks. In this paper, we propose a novel approach for intra-and inter-modality saliency feature learning by Multi-granularity Feature Selection and Relation (MFSR). Firstly, to reduce redundant information within each modality, the Intra-modality Feature Selection Module (IFSM) employs the adaptive weighting mechanism to enhance salient pedestrian features while suppressing irrelevant features. Secondly, the Multi-granularity Feature Relation Module (MFRM) aligns cross-modality salient person features by increasing the similarity scores of local and global features across modalities, to reduce differences between multi-modality visual-text pairs. Finally, the Cross-modality Similarity Matching (CSM) loss is designed to enhance consistency in visual-text pairs of the same identity, ensuring compact intra-class features by minimizing discrepancies between cross-modality similarity distributions and identity-matching distributions. Experimental results show that our approach achieves state-of-the-art performance on benchmark datasets. © 2014 IEEE.

关键词： Feature Selection

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Integration of smart Sensors and Machine Learning Algorithms for Automated Pest Management 1

Integration of smart Sensors and Machine Learning Algorithms...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Yadav, Krrish Roy, Sritama Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798331520540

This paper presents a new IoT-based approach for agricultural pest control, incorporating machine learning techniques. Traditional pest monitoring methods are labor-intensive and often result in the overuse of pesticides. The proposed system solves this problem by predicting pest populations using environmental data collected via IoT sensors. We use random forest regression and random search cross-validation to predict pests and identify important factors. The system calculates the amount of pesticides based on forecasts, helping farmers optimize use and reduce environmental impact. The experimental results demonstrate the effectiveness of the system in terms of accurate prediction and optimal pesticide application, and thereby providing a scalable and cost-effective solution for sustainable agriculture. © 2025 IEEE.

关键词： Smart agriculture

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