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检索条件"任意字段=24th International Conference on Control Systems and Computer Science, CSCS 2023"

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Application of Compensation Method for Layout Parasitic Elements control in Precision DACs

Application of Compensation Method for Layout Parasitic Elem...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Dmitry B. Koloskov Valeriy P. Dragunov Alexander V. Gluhov Dept. of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Dept. of Computer Aided Design Siberian State University of Telecommunications and Information Science Novosibirsk Russia

the paper deals with the problem of counteracting parasitic layout elements in precise analog circuits, in particular, precision voltage references and high-bit DACs and ADCs. We described the two main methods of countering them - minimization and compensation ones. On the example of a 14-bit 1 GHz DAC with embedded Bandgap voltage reference, we evaluated the effect of parasitic resistances on the device performance and its static characteristics. We showed that parasitic resistances could increase the TC of the Bandgap voltage reference source by more than 4 times, from 4 ppm/C to 17.3 ppm/C. this impact was assessed as critical for studied 14-bit DAC functioning. With the compensation method of countering the parasitic resistances, we were able to reduce the TC of the reference to 4.8 ppm/C. We concluded that dealing with parasitic elements is mandatory for precision Bandgap voltage references and high-bit DACs, and the compensation method is effective for this task.

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Numerical Simulation of Grating Input/Output Elements into Optical Waveguides Based on Si3N4/SiO2 thin Films

Numerical Simulation of Grating Input/Output Elements into O...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgenij S. Barbin Ivan V. Kulinich Aleksej N. Koleda Ayan Myrzakhmetov Anton Perin Laboratory of Radiophotonics The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Intelligent Computer Systems Institute of Optics and Atmosphere Siberian Branch of the Russian Academy of Sciences Tomsk Russia Laboratory of Photonic Integrated Circuits The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

this paper presents the results of modeling a grating element for input/output of optical radiation into Si 3 N 4 /SiO 2 waveguide structures in the wavelength range from 1.5 to $1.6\ \mu \mathrm{m}$ . the results of the study of the geometric parameters of a diffraction grating on the optical transmission coefficient, in particular: the grating period, undercutting period, etching depth, filling factor, are presented. For a wavelength of $1.55\ \mu \mathrm{m}$ the maximum optical transmission coefficient is observed at: grating period $0.875\ \mu \mathrm{m}$ ; etching depth $0.181\ \mu \mathrm{m}$ ; fill factor of $0.595\ \mu \mathrm{m}$ ; undercutting $\pm 0.015\ \mu \mathrm{m}$ . the greatest contribution to the change in the transmission coefficient is made by the change in the grating period and is $\pm 0.0022\ \mu \mathrm{m}/{\%}$ and the etching depth is $\pm 0.00762\ \mu \mathrm{m}/{\%}$ . Based on the results of simulations, taking into account the operating wavelengths and refractive indices of Si 3 N 4 /SiO 2 films, the following permissible deviations of diffraction lattice parameters were put forward: grating period $\pm 0.022\ \mu \mathrm{m}$ ; etching depth $\pm 0.0762\ \mu \mathrm{m}$ ; filling factor ±20%; undercutting period $\pm 0.14\ \mu \mathrm{m}$ . Permissible insertion angles were 10–12 degrees. the reflection loss at the input of light into the waveguide was −17 dB, and at the output from −20 to −35 dB. Transmission loss at −3dB was $0.09\ \mu \mathrm{m}$ .

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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images 1

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2021

作者： Andrearczyk, Vincent Oreiller, Valentin Boughdad, Sarah Rest, Catherine Cheze Le Elhalawani, Hesham Jreige, Mario Prior, John O. Vallières, Martin Visvikis, Dimitris Hatt, Mathieu Depeursinge, Adrien Institute of Information Systems School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Lausanne Switzerland Poitiers France Department of Radiation Oncology Cleveland Clinic Foundation ClevelandOH United States Department of Computer Science University of Sherbrooke SherbrookeQC Canada LaTIM INSERM UMR 1101 Univ Brest Brest France

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

this paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th international conference on Medical Image Computing and computer Assisted Intervention (MICCAI) 2021. the challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. the data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. the interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. the best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. the comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. this potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects. © 2022, Springer Nature Switzerland AG.

关键词： Medical imaging

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Comparation of Rotational Deep Learning based Methods

Comparation of Rotational Deep Learning based Methods

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international Carpathian control conference (ICCC)

作者： Sándor Tihamér Brassai András Németh Attila Hammas István Dávid Sagyebó Department of Electrical Engineerin Sapientia Hungarian University of Transylvania Corunca Romania Department of Electronic Warfare National University of Public Service Budapest Hungary Intelligent Control Systems Sapientia Hungarian University of Transylvania Corunca Romania National University of Public Service Budapest Hungary

Unmanned Aerial Vehicles (UAV) imagery-based object detection has become more and more widespread, especially in fields like agriculture, city planning, construction oversight or even military reconnaissance. this research focused on a review of the state-of-the-art object detection algorithms which use oriented bounding boxes. the images were collected using DJI Tello and Zll SG906 Pro 2. the images were annotated using computer Vision Annotation Tool (CVAT). the different detectors were trained using MMDetection and MMRotate modules from MMLab framework. the advantages and disadvantages of different deep neural network approaches are aimed to be compared: Rotated Faster R-CNN (two-stage anchor based detector), Single Shot Alignment Network (S 2 ANet) (one-stage anchor detector) and FCOSR (one-stage anchor-free detector). On neural network models two series of measurements were performed. In the first version, all the image samples were trained during the epochs. In the second version the images were grouped in tasks, and in each epoch a new tasks were load to train. Based on the measurement results, it can be concluded that better results were achieved with the Rotated Faster R-CNN model and the S 2 ANet model, the FCOSR performance lagged behind the previous models.

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Deep Learning for Violence Detection in Surveillance: the Role of Transfer Learning and Pre-Trained Models

Deep Learning for Violence Detection in Surveillance: The Ro...

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Information Technology (ACIT)

作者： Anum Ilyas Surayya Obaid Narmeen Zakaria Bawany Department of Computer Science and Software Engineering Center for Computing Research Jinah University for Women Karachi Pakistan

In recent years, the use of CCTV footage for proactive crime prevention has surged, particularly in public places like airports, train stations, and malls. However, the efficacy of these surveillance systems becomes questionable for their substantial reliance on human resources which may lead to erroneous or delayed responses. this research takes a comprehensive approach for violence detection which was previously limited to binary classification, by categorizing violence into four distinct classes: abuse, arson, assault, and fight. It employs transfer learning combined with computer vision techniques for violence detection and classification in video footages. the study compares the performance of four pre-trained neural networks, namely DenseNet121, VGG16, MobileNet, and Xception. the dataset created for this research is compiled from three datasets available on Kaggle. the results reveal that the Xception model performed comparatively better achieving the highest AUC score of 98.39%, while the VGG16 model attained the lowest AUC score of 96%. In addition to the AUC score, precision, recall, and fl-score are employed as performance metrics. Transfer learning with convolutional neural networks (CNN) significantly reduced computational requirements and time. Automating the detection and categorization of violent behavior through the employed approach has the potential to reduce the risk of fatalities and injuries in public areas. It also improves the speed and accuracy of threat detection, enabling swift preventive actions by authorities.

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Optimizing Connection Weights in Neural Networks Using Ali Baba and the Forty thieves Algorithm

Optimizing Connection Weights in Neural Networks Using Ali B...

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Information Technology (ACIT)

作者： Heba Al-Hiary Malik Braik Mohammed Azmi Al-Betar Department of Computer Information Systems Al-Balqa Applied University Al-salt Jordan Department of Computer Science Al-Balqa Applied University Al-salt Jordan Department of Information Technology Artificial Intelligence Research Center (AIRC) Ajman University Ajman UAE Al-Balqa Applied University Irbid Jordan

Learning neural networks (NNs) is one of the most challenging problems in machine learning and has lately drawn the attention of many academics. the nonlinear structure of NNs and the unknown optimal set of key governing parameters (biases and weights) make training NNs particularly challenging. the primary drawbacks of conventional training methods are delayed convergence and local optima stagnation. Due to this, stochastic optimization techniques are dependable substitutes for addressing these problems. this study presents a new training method on the basis of the newly announced ali Baba and the forty thieves (AFT) algorithm. It has been shown that this algorithm can tackle a variety of optimization problems and can outperform many existing algorithms. Our efforts to measure its performance were prompted by this when training NNs. the proposed AFT-based trainer was tested using a bunch of 15 datasets that vary in intricacy. A comparison of AFT-based training NNs with other meta-heuristics presented for the same purpose was conducted to explore the efficiency of this training method. In most datasets, the training method-based AFT converged faster and was more adept at avoiding local optima than other rival methods.

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th international conference on Computing, control and Industrial Engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. this makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. the global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. the verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Resonators in Modal Filter as a Resorse for Improving its Characteristics

Resonators in Modal Filter as a Resorse for Improving its Ch...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Ilya A. Ivantsov Yuri V. Kalashnikov Yevgeniy S. Zhechev Scientific Research Laboratory of Basic Research on Electromagnetic Compatibility Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

this paper presents a novel approach to suppressing ultra-wideband (UWB) interference using a strong-coupled modal filter. Specifically, T-resonators are proposed as a means of improving the interference-suppression characteristics. the optimal resonators geometric parameters were obtained by the heuristic search method. Results show that the use of a pair of resonators can reduce the amplitude of UWB interference at the output by half. To validate the results, simulations were conducted using different approaches, which showed good agreement with each other based on the waveforms of the frequency response. the paper also highlights the use of N-norms to estimate the hazard of UWB interference after suppression. the N-norm values suggest a reduced probability of component burnout and equipment damage. In conclusion, this paper demonstrates the potential for additional suppression of UWB interference in modal filters using T-resonators. this approach could have significant implications for improving the functionality and reliability of electrical circuits in various applications.

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Brain Tumor Segmentation of MRI Images Based on K-Means and White Shark Optimizer

Brain Tumor Segmentation of MRI Images Based on K-Means and ...

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Information Technology (ACIT)

作者： Malik Braik Heba Al-Hiary Mohammed Azmi Al-Betar Department of Computer Science Al-Balqa Applied University Al-salt Jordan Department of Computer Information Systems Al-Balqa Applied University Al-salt Jordan Department of Information Technology Artificial Intelligence Research Center (AIRC) Ajman University Ajman UAE Al-Balqa Applied University Irbid Jordan

Due to the intricacy of the process involved in segmenting and extracting the tumor regions in magnetic resonance imaging (MRI), it is quite difficult to successfully detect the diseases. Due to its critical significance in subsequent image processing phases, this creates a requirement for a suitable clustering algorithm. In this study, a clustering method is presented that may address a variety problems for MRI brain tumor segmentation. One of the most well-known conventional clustering methods that is simple to use, quick to process, and ensures convergence is k-means (KM). Its weakness, which is defined by its susceptibility to the initial center's random initialization, is present. Because of this, the KM was optimized in this study using the white shark optimizer (WSO) to address this weak side of the KM. the KM would set the starting point for WSO's final position. Evaluation results of WSO-based KM were judged on a publicly available datasets in terms of several relevant evaluation methods and contrasted with outcomes of KM, fuzzy c-means (FCM), and other meta-heuristics when practiced to cluster the same datasets. the WSO-based KM clustering approach outperformed the standard KM and other competing algorithms-based KM.

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24th international conference on Information and Communications Security, ICICS 2022

24th International Conference on Information and Communicati...

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24th international conference on Information and Communications Security, ICICS 2022

ISBN: (纸本)9783031157769

the proceedings contain 34 papers. the special focus in this conference is on Information and Communications Security. the topics include: Maliciously Secure Multi-party PSI with Lower Bandwidth and Faster Computation;Conditional Cube Attacks on Full Members of KNOT-AEAD Family;Fast Fourier Orthogonalization over NTRU Lattices;secure Sketch and Fuzzy Extractor with Imperfect Randomness: An Information-theoretic Study;tight Analysis of Decryption Failure Probability of Kyber in Reality;preface;BS: Blockwise Sieve Algorithm for Finding Short Vectors from Sublattices;improving Deep Learning Based Password Guessing Models Using Pre-processing;exploring Phone-Based Authentication Vulnerabilities in Single Sign-On systems;FRACTAL: Single-Channel Multi-factor Transaction Authentication through a Compromised Terminal;lightweight and Practical Privacy-Preserving Image Masking in Smart Community;using Blockchains for Censorship-Resistant Bootstrapping in Anonymity Networks;Repetitive, Oblivious, and Unlinkable SkNN Over Encrypted-and-Updated Data on Cloud;privacy-Aware Split Learning Based Energy theft Detection for Smart Grids;query-Efficient Black-Box Adversarial Attack with Random Pattern Noises;autoencoder Assist: An Efficient Profiling Attack on High-Dimensional Datasets;TZ-IMA: Supporting Integrity Measurement for Applications with ARM TrustZone;calibrating Learning Parity with Noise Authentication for Low-Resource Devices;fuzzBoost: Reinforcement Compiler Fuzzing;secure Boolean Masking of Gimli: Optimization and Evaluation on the Cortex-M4;DeepC2: AI-Powered Covert Command and control on OSNs;ODDITY: An Ensemble Framework Leverages Contrastive Representation Learning for Superior Anomaly Detection;deep Learning Based Webshell Detection Coping with Long Text and Lexical Ambiguity;SimCGE: Simple Contrastive Learning of Graph Embeddings for Cross-Version Binary Code Similarity Detection;FN2: Fake News DetectioN Based on Textual and Contextual Features;Malware Detection wi

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