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A Multidimensional Hardware Trojan Design Platform to Enhance Hardware Security

IEEE EMBEDDED SYSTEMS LETTERS 2025年第1期17卷 46-49页

作者： Das, Nilanjana Hasler, Mattis Pauls, Friedrich Haas, Sebastian Barkhausen Inst Scalable Comp Hardware Dept D-01067 Dresden Germany

This letter proposes a novel kind of HT design named multidimensional HTs (MDHTs) and develops a method to generate configurable MDHT benchmark platform. The proposed MDHT circuits include multiple net(s) as trigger signals from each of the rarely activated, highly activated, and partially activated categories to increase MDHT's adverse effects. The generated MDHT-infected circuits are tested by an unsupervised machine learning-based HT detection technique-controllability and observability for HT detection (COTD). Experimentation on ISCAS benchmarks ensures that the detection method is unable to detect the developed MDHT circuits as the nets belong to higher and partial activities are creating at least 50% and at most 80% false negative rate which validates the MDHT insertion framework in addition to the available HT benchmarks.

关键词： Benchmark testing detection algorithms Trojan horses Observability Switches Logic gates Controllability Benchmark controllability k-means clustering multidimensional hardware Trojan (MDHT) observability transition probability (TP)

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Adaptive R-Peak detection Algorithm Based on Brown Exponential Smoothing Model

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IEEE ACCESS 2022年 10卷 114355-114363页

作者： Hao, Zihao Zhang, Xiaoming Lai, Zhengxi North Univ China Natl Key Lab Elect Measurement Technol Taiyuan 0351 Peoples R China North Univ China Minist Educ Key Lab Instrumentat Sci & Dynam Measure Taiyuan 030051 Peoples R China Shenzhen ImyFit Technol Co Ltd Shenzhen 518107 Peoples R China

ECG is one of the most effective medical tests for heart disease diagnosis, and R-peak detection is the first step in ECG interpretation. For wearable ECG signals, the difficulty of R-peak detection mainly lies in the interference of dynamic strong noise, and the limited hardware computational resources limit the use of some complex algorithms. Therefore, an adaptive threshold R-peak detection algorithm based on Brown's exponential smoothing model is proposed in this paper. The algorithm selects features based on the morphological characteristics and occurrence law of R-peaks, updates the threshold parameters using the Brown exponential smoothing model, optimizes the smoothing coefficients in it using the relative error least squares method, corrects the smoothing ability of the observation error and the response speed to the change of the observation, so that the updated threshold can be more consistent with the R-peak detection. Finally the algorithm achieves 99.6% precision, 99.7% recall and 99.65% F1 score on the self-constructed ECG dataset, and compares with other R-peak detection algorithms to determine the superiority of the proposed algorithm in some performance metrics. It is demonstrated experimentally that the algorithm can adapt well to the strong noise environment and obtain satisfactory R-peak detection accuracy.

关键词： Electrocardiography Smoothing methods Adaptation models Interference detection algorithms Heuristic algorithms Biomedical monitoring Wearable ECG signal R-peak detection brown exponential smoothing model adaptive threshold

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Automatic welding imperfections detection in a smart factory via 2-D laser scanner

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JOURNAL OF MANUFACTURING PROCESSES 2022年 73卷 948-960页

作者： Bologna, Francesco Tannous, Michael Romano, Donato Stefanini, Cesare Scuola Super Sant Anna BioRobot Inst Viale R Piaggio 34 I-56025 Pontedera PI Italy Scuola Super Sant Anna Dept Excellence Robot & Artificial Intelligence I-56025 Pisa Italy Baker Hughes Co Nuovo Pignone Tecnol Srl Via Felice Matteucci 2 I-50127 Florence FI Italy Khalifa Univ Healthcare Engn Innovat Ctr Abu Dhabi U Arab Emirates

Welding defect detection is crucial to determine welding quality and assure correct mechanical performance of the welded joint during operation. Most of common detection techniques are performed offline, are time consuming, and strongly dependent on the operator skill. In the present work, two novel automatic online welding imperfections detection approaches are presented for classifying qualified and defective profiles acquired with a 2-D laser scanner on square grooves in a multi-pass Gas Tungsten Arc Welding process. In particular, a Heuristic Algorithm has been developed to analyze different geometrical characteristics of the profile for its classification. The Heuristic Algorithm has been validated on five welded coupons, created in Baker Hughes welding laboratory in Massa, Italy. Coupons have been scanned and resulting profiles have been manually labeled. The Heuristic Algorithm showed good performance, with a F1 score of 94.8%. Additionally, a multi-layer feed forward Neural Network has been trained and tested on the profiles acquired from the five welded coupons, with an overall F1 score of 91.0%. The promising results of the two proposed approaches will lead to a wider test campaign, before implementing a mixed solution in a multi-pass robotic Gas Tungsten Arc Welding industrial process.

关键词： Artificial intelligence Automation detection algorithms Industry 4 0 Welding

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CENSor: Detecting Illicit Bitcoin Operation via GCN-Based Hyperedge Classification

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IEEE ACCESS 2024年 12卷 152330-152346页

作者： Lee, Suyeol Kim, Jaehan Seo, Minjae Ho Na, Seung Shin, Seungwon Kim, Jinwoo FuriosaAI Seoul 06036 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea ETRI Daejeon 34129 South Korea Kwangwoon Univ Sch Software Seoul 01897 South Korea

Cryptocurrencies have increasingly been used as a medium for illicit financial activities by criminals. Annually, billions of dollars' worth of Bitcoin penetrate cryptocurrency exchanges. Despite the critical need for advanced Bitcoin financial forensics to investigate these criminal activities, no novel methods have been developed to detect illicit Bitcoin operations. Existing approaches to identifying illegal Bitcoin activity are limited due to their inadequate consideration of graph data. To address these limitations, we present a novel approach, Hyperedge Classification, to detect illegal transactions with greater precision. This approach introduces a novel cluster-based Hyperedge-Node Switching technique, which enables effective hyperedge classification and visualization of hyperedge relationships. Additionally, we propose a framework named CENSor (Cluster-based Edge Node Switching Detector), which offers more powerful and robust detection capabilities compared to traditional techniques for both illegal entity detection and illegal transaction detection. Our cluster-based Hyperedge-Node Switching technique demonstrates its effectiveness with an F1-score of 0.867, outperforming comparative baselines. Moreover, CENSor visualizes the Bitcoin cluster graph and the Hyperedge-Node switched graph, highlighting the importance of utilizing appropriate graph information in Bitcoin analysis. Finally, we demonstrate that CENSor is resilient to an adversarial attack aimed at evading detection.

关键词： Bitcoin Switches Image edge detection Forensics Feature extraction Visualization Scalability Cryptocurrency detection algorithms illicit entity detection hypergraph graph neural network

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GF-4 Satellite Fire detection With an Improved Contextual Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 163-172页

作者： Zhang, Ning Sun, Lin Sun, Zhendong Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Beijing Res Ctr Informat Technol Agr Key Lab Quantitat Remote Sensing Agr Minist Agr Beijing 100097 Peoples R China

The GF-4 satellite has a moderate spatial resolution and is capable of high temporal frequency observation. Thus, it can play an important role in the acquisition of information regarding fires. In this article, the characteristics of brightness temperature difference between fire point area and nonfire point area are comprehensively analyzed. Employing the advantages of high-frequency continuous observation of these data, an improved algorithm for joint spatial attribute detection using multitemporal data is proposed. This method is based on the traditional contextual method and incorporates the concept of time sequence, and uses the changes in time phase before and after the fire to reduce the false alarm caused by the high heterogeneity of pixels in a single spatial scene in the traditional method. In order to evaluate the accuracy of the algorithm, the perfect moderate resolution imaging spectrometer thermal anomaly product is used as the verification data to evaluate the feasibility of the algorithm. At the same time, it is necessary to compare the algorithm in this article with the fire algorithm currently applied to GF-4 with the four typical regions. The improved algorithm shows better drawing ability, and the consistency of fire description of biomass combustion for many days is improved. In the case of the Heilongjiang region on October 30, 2017, the accuracy rate is improved by 62%, compared with the contextual method.

关键词： Reflectivity Temperature measurement MODIS Combustion Brightness temperature Monitoring detection algorithms Brightness temperature fire point identification GF-4 spatial characteristics time phase change

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YOLO8-FASG: A High-Accuracy Fish Identification Method for Underwater Robotic System

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IEEE ACCESS 2024年 12卷 73354-73362页

作者： Qin, Xiangrong Yu, Changdong Liu, Baisheng Zhang, Zhihao Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China Dalian Maritime Univ Coll Artificial Intelligence Dalian 116026 Peoples R China Liaoning Tech Univ Coll Software Huludao 125000 Peoples R China

In underwater robots, accurate identification of small fish is still a major challenge, because small fish move faster and occupy less screen space, which requires higher detection flexibility and receptive field of the model. To solve this challenge, we propose a high-precision small fish identification and tracking method named YOLO8-FASG in this paper. Specifically, the proposed method is improved in three aspects based on the YOLOv8 framework. First, Alterable Kernel Convolution (AKConv) is used in the neck network of the model to automatically adjust the shape of the convolution kernel according to the size and shape of the object. In this way, the shape and contour characteristics of rapidly changing fish can be captured more accurately and efficiently;Second, we introduce a global attention mechanism (GAM) to broaden the receptive field of the model by enhancing attention to fish features from the two dimensions of channel and space;Third, we employ Simplified Spatial Pyramid Pooling-Fast (SimSPPF) to replace the standard Spatial Pyramid Pooling-Fast (SPPF) to enhance prediction accuracy. These improvements enable the model to effectively extract image features of small, fast fish, thereby improving the robot's accuracy in identifying small fish underwater. Experiments results in the public dataset Fish4Knowledge show that YOLO8-FASG performs significantly better than traditional YOLOv8 in underwater environments. Specifically, Precision and Recall increased by 1.6% and 3.5% respectively, while mAP50 and mAP50-95 increased by 1.3% and 6.1% respectively, and our method provides an effective solution for underwater robots to identify fish schools.

关键词： Feature extraction Task analysis Convolutional neural networks YOLO Object detection Predictive models Autonomous underwater vehicles Mobile robots Fish schools detection algorithms Spatial resolution YOLOv8 global attention mechanism AkConv SimSPPF object detection

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Retinal Health Screening Using Artificial Intelligence With Digital Fundus Images: A Review of the Last Decade (2012-2023)

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IEEE ACCESS 2024年 12卷 176630-176685页

作者： Islam, Saad Deo, Ravinesh C. Datta Barua, Prabal Soar, Jeffrey Yu, Ping Rajendra Acharya, U. Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Sch Business Springfield Qld 4300 Australia Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Univ Wollongong Fac Engn & Informat Sci Sch Comp & Informat Technol Wollongong NSW 2516 Australia

Prolonged diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD) may lead to vision loss. Hence, early detection and treatment are crucial to prevent irreversible vision loss. Fundus retinal images have been widely used to help detect these diseases. Manual screening is susceptible to human errors, tedious, and expensive. Hence, artificial intelligence (AI) techniques have been widely employed to overcome these constraints. This paper reviewed the work published on automated retinal health detection models using various machine learning (ML) and deep learning (DL) techniques. We reviewed 142 papers and 262 studies (124 on glaucoma, 60 on AMD, and 78 on DR) from January 2012 to June 2024 using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We found that Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) models were widely used in DL and ML techniques, respectively. To the best of our knowledge, this is the first review developed for detecting AMD, DR, and glaucoma using AI techniques over the last decade. We have discussed the limitations of the present methods and also suggested future directions for accurately detecting eye diseases.

关键词： Retina Glaucoma Artificial intelligence Imaging Reviews Diabetic retinopathy Costs Accuracy Monitoring Magnetic resonance imaging detection algorithms Deep learning Machine learning Retinal health automated detection deep learning machine learning glaucoma fundus

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A 40-nm 131-mW 6.4-Gb/s 256 x 32 Multi-User MIMO OTFS Detector for Next-Gen Communication Systems

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2025年

作者： Lee, Tang Chen, Ting-Yang Liu, I-Hsuan Yang, Chia-Hsiang Natl Taiwan Univ Grad Inst Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Dept Elect Engn 10617 10617 Taiwan

High-mobility communication technology enables important applications in the near future. In such scenarios, the wireless channel exhibits high Doppler spread, which makes orthogonal frequency division multiplexing (OFDM) adopted in current wireless system suffer from severe inter-carrier interference (ICI). The orthogonal time frequency space (OTFS) technique is a promising modulation to address this issue. It demonstrates higher resilience to Doppler spread than OFDM in terms of bit error rate (BER) at the expense of higher detection complexity. This work presents the first high-throughput multi-user multi-input multi-output (MU-MIMO) detector for OTFS communication systems. A low-complexity message-passing (MP) detection algorithm is proposed to achieve 93% lower computational complexity by leveraging the structure of Gram matrix. A memory-efficient residual noise (RN) update scheme is devised to reduce the memory size for storing the partial interference by 94%. The proposed MP detector achieves a 60% reduction in latency by employing mean computation unit (MCU) and dual-mode multiplier. In addition, a 91% memory access reduction and an 89% memory size reduction in the channel memory bank are achieved, respectively, by leveraging layer ordering, partial Gram matrix saving, and block diagonal approximation. The chip supports up to 32 users, 256 receive antennas, and 256-QAM modulation. Fabricated in a 40-nm CMOS technology, the chip integrates 6.76 M gates in area of 6.47 mm(2) and it delivers a maximal throughput of 6.4 Gb/s. The power consumption is 131 mW at 200 MHz from a 0.9 V supply. In comparison to state-of-the-art MU-MIMO OFDM detectors, this work achieves 3.3-to-21.3x higher maximal throughput and 2.2-to-67.0x lower normalized energy, in addition to higher resilience to Doppler spread.

关键词： Detectors OFDM Symbols Doppler effect Modulation Vectors Wireless communication Bit error rate Interference detection algorithms Algorithm-architecture co-optimization digital integrated circuit message-passing (MP) detector multi-user multi-input multi-output (MU-MIMO) system orthogonal time frequency space (OTFS) modulation

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Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

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IEEE ACCESS 2024年 12卷 36622-36635页

作者： Altabaji, Wassem I. A. E. Umair, Muhammad Tan, Wooi-Haw Foo, Yee-Loo Ooi, Chee-Pun Multimedia Univ Fac Engn Cyberjaya 63100 Selangor Malaysia

Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet's architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.

关键词： Plant diseases detection algorithms Deep learning Convolutional neural networks Agriculture Classification algorithms Crop yield Transfer learning Hyperparameter optimization convolutional neural networks transfer learning image classification

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Perceptual Hashing With Deep and Texture Features

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IEEE MULTIMEDIA 2024年第1期31卷 65-75页

作者： Yu, Mengzhu Tang, Zhenjun Liang, Xiaoping Zhang, Xianquan Zhang, Xinpeng Guangxi Normal Univ Software Engn Guilin 541004 Peoples R China Guangxi Normal Univ Sch Comp Sci & Engn Guilin 541004 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Image hashing is a useful technique for many multimedia systems, such as image authentication, image copy detection, tampering detection, and image quality assessment (IQA). However, most of the image hashing schemes do not make desirable performance of IQA. To tackle this, a new hashing scheme with deep and texture features is proposed for reduced-reference (RR)-IQA. In the proposed hashing, deep features are calculated from the discrete cosine transform coefficients of the three-order tensor stacked by the feature maps generated by the pretrained ResNet18. Texture features are extracted by the gray-level co-occurrence matrix in the nonsubsampled shearlet transform domain. Hash is determined by combining the quantized versions of the deep and texture features. Extensive experiments performed on open datasets indicate that the proposed perceptual hashing is superior to some baseline schemes in the performances of RR-IQA and classification.

关键词： Feature extraction Discrete cosine transforms Tensors Distortion Robustness Three-dimensional displays Sensitivity Transforms Image quality Multimedia systems Classification algorithms detection algorithms Hash functions

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