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作者： Schöne, Sandra Pol, Nirmal Kahrs, Lueder A. Medical Computer Vision and Robotics Lab University of Toronto Toronto Canada University of Stuttgart Stuttgart Germany Medical Computer Vision and Robotics Lab Institute of Biomedical Engineering University of Toronto Toronto Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga Mississauga Canada

To automate surgical (sub-)tasks in robotic surgery, the knowledge of the exact pose of the instrument is mandatory. The application of Optical Coherence Tomography (OCT) to the problem of pose measurement appears promising due to its advantages of 3D imaging and micron-scale resolution. To investigate this, 175 image sequences of the EndoWrist Round Tip Scissor Tool were acquired with an OCT system. The images differ in the opening angles of the scissor blades and the rotation angles of the entire instrument about its central axis. These image sequences were further processed through computer vision methods of the individual images followed by point cloud generation. For pose estimation, an Iterative Closest Point algorithm was implemented to register the acquired point clouds to reference point clouds created from the instrument CAD file. The implemented algorithm was able to determine the opening angle with an overall error of 2 ± 1.3 and the rotation angle with a standard deviation between several runs of 0.6 ±2.8. However, the overall processing time of (39 ± 17)s on a standard PC leaves room for further investigations. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Optical coherence tomography

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Large circuit models: opportunities and challenges

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Science China(Information Sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

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Multimedia Tools and Applications 2025年第16期84卷 16909-16943页

作者： El-Shafai, Walid El-Mesady, A. Kamal, F.M. Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Physics and Engineering Mathematics Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University PO 35516 Mansoura Egypt

The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t

关键词： Cryptography

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A Market-Clearing-Based Sensitivity Model for Locational Marginal and Average Carbon Emission

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 579-582页

作者： Lu, Zelong Yan, Lei Wang, Jianxue Kang, Chongqing Shahidehpour, Mohammad Li, Zuyi Xi'an Jiaotong University School of Electrical Engineeng Xi'an710049 China Zhejiang University College of Electrical Engineering Hangzhou310027 China Tsinghua University State Key Lab of Power Systems Department of Electrical Engineering Beijing100084 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States

This letter proposes a market-clearing-based locational marginal carbon emission (LMCE) metric to assess the marginal carbon emission effect of nodal load demand. Unlike the prevalent carbon emission flow (CEF) method that relies on a hypothetical power-flow tracking process, the proposed LMCE metric depends on a novel sensitivity analysis of market-clearing results, capable of revealing both energy-dependent and network-dependent impacts on emissions. Additionally, we introduce a locational average carbon emission (LACE) metric, derived from LMCE, to effectively measure the general emission effect. It offers insights into demand-side carbon emission effects, such as a negative LMCE and LACE indicating emission reduction even as load increases. It can also prevent excessive demand-side emission allocations. Overall, the proposed method provides a clear perspective for the ongoing decarbonization policies. © 2024 IEEE.

关键词： Sensitivity analysis

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2D Ear Recognition Using Data Augmentation and Deep CNN

2D Ear Recognition Using Data Augmentation and Deep CNN

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International Conference on Machine vision and Augmented Intelligence, MAI 2022

作者： Mehta, Ravishankar Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur India

ISBN: (纸本)9789819901883

Ear recognition is one of the challenging tasks in the era of computer vision. In this work, we have highlighted the role of augmentation technique on convolutional neural networks (CNNs) for the purpose of improving the recognition rate. Here, we developed a simple CNN-based model for feature extraction and classification task using ear image for person identification. Over the last few years, many researchers have worked on different techniques to improve the recognition accuracy for person identification using ear images. We evaluated our method on unconstrained and unconstrained dataset. For this purpose, we have used AWE and AMI ear datasets for the purpose of ear recognition. The results show that the application of augmentation technique improves the accuracy of the model. In contrast to other method without augmentation, training a model with augmentation of data increases the accuracy from 52 to 82%. Hence, we can conclude that the data augmentation can be applied for the recognition task using ear images when there is lack of sufficient data in the dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics

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Microsystems & Nanoengineering 2025年第1期11卷 465-476页

作者： Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Center for Cyber-Physical Systems-System on Chip Lab Khalifa UniversityAbu Dhabi 127788UAE Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering Khalifa UniversityAbu Dhabi 127788UAE Division of Engineering New York UniversityAbu Dhabi 129188UAE Department of Mechanical and Nuclear Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Chemistry Khalifa UniversityAbu Dhabi 127788UAE

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing *** this study,we present an approach that integrates an iontronic fluidic memristive(IFM)device with low input impedance and a triboelectric nanogenerator(TENG)based on ferrofluid(FF),which has high input *** incorporating contact separation electromagnetic(EMG)signals with low input impedance into our FF TENG device,we enhance the FF TENG’s performance by increasing energy harvesting,thereby enabling the autonomous powering of IFM devices for self-powered ***,replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic *** fluidic devices,composed of soft-matter materials,dynamically adjust their conductance by altering the solution *** developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane(PDMS)structures,utilising a fluidic interface of FF and polyacrylic acid partial sodium salt(PAA Na^(+)).The confined ion interactions in this system induce hysteresis in ion transport across various frequencies,resulting in significant ion memory *** IFM successfully replicates diverse electric pulse patterns,making it highly suitable for neuromorphic ***,our system demonstrates synapse-like learning functions,storing and retrieving short-term(STM)and long-term memory(LTM).The fluidic memristor exhibits dynamic synapse-like features,making it a promising candidate for the hardware implementation of neural *** TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials,further enhanced by intricate chemical designs for self-powered electronics.

关键词： iontronic memristive ionic fluidic devices memcapacitive ff teng s ff teng fluidic triboelectric nanogenerator teng based

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Learn & drop: fast learning of cnns based on layer dropping

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Neural Computing and Applications 2024年第18期36卷 10839-10851页

作者： Cruciata, Giorgio Cruciata, Luca Lo Presti, Liliana van Gemert, Jan La Cascia, Marco Engineering Department University of Palermo V.le delle Scienze Ed. 6 Palermo90128 Italy Computer Vision Lab Delft University of Technology Van Mourik Broekmanweg 6 Delft2628 XE Netherlands

This paper proposes a new method to improve the training efficiency of deep convolutional neural networks. During training, the method evaluates scores to measure how much each layer’s parameters change and whether the layer will continue learning or not. Based on these scores, the network is scaled down such that the number of parameters to be learned is reduced, yielding a speed-up in training. Unlike state-of-the-art methods that try to compress the network to be used in the inference phase or to limit the number of operations performed in the back-propagation phase, the proposed method is novel in that it focuses on reducing the number of operations performed by the network in the forward propagation during training. The proposed training strategy has been validated on two widely used architecture families: VGG and ResNet. Experiments on MNIST, CIFAR-10 and Imagenette show that, with the proposed method, the training time of the models is more than halved without significantly impacting accuracy. The FLOPs reduction in the forward propagation during training ranges from 17.83% for VGG-11 to 83.74% for ResNet-152. As for the accuracy, the impact depends on the depth of the model and the decrease is between 0.26% and 2.38% for VGGs and between 0.4 and 3.2% for ResNets. These results demonstrate the effectiveness of the proposed technique in speeding up learning of CNNs. The technique will be especially useful in applications where fine-tuning or online training of convolutional models is required, for instance because data arrive sequentially. © The Author(s) 2024.

关键词： Backpropagation

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A Comparative Analysis of 2D Ear Recognition for Constrained and Unconstrained Dataset Using Deep Learning Approach

A Comparative Analysis of 2D Ear Recognition for Constrained...

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International Conference on Machine vision and Augmented Intelligence, MAI 2022

ISBN: (纸本)9789819901883

In this paper, we have made a comparative analysis of ear recognition for constrained and unconstrained dataset. We have also analyzed the effect of image quality like illumination, occlusion, brightness, and pose variation in our work. We have addressed the problem of limited dataset for ear recognition task using deep learning-based approach. We aim at building a deep convolutional neural network model that does the task of classification for ear recognition task. Our model considers both constrained and unconstrained dataset of ear image for analysis purpose. In this paper, we have explored the benefits of deep learning and its different approached to enhance the performance ear based biometric systems where sample images are captured in constrained and unconstrained condition. This paper mainly focuses on creation of deep learning model and compared its performance on three different datasets (AWE, AMI, and IITD-II) of ear images by varying the network architecture. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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