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Modular software for real-time quantum control systems 3

Modular software for real-time quantum control systems

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3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Riesebos, Leon Bondurant, Brad Whitlow, Jacob Kim, Junki Kuzyk, Mark Chen, Tianyi Phiri, Samuel Wang, Ye Fang, Chao Van Horn, Andrew Kim, Jungsang Brown, Kenneth R. Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Sungkyunkwan Univ SKKU Adv Inst Nanotechnol SAINT Suwon 16419 South Korea Sungkyunkwan Univ Dept Nanoengn Suwon 16419 South Korea Duke Univ Dept Phys Durham NC 27708 USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Real-time control software and hardware is essential for operating quantum computers. In particular, the software plays a crucial role in bridging the gap between quantum programs and the quantum system. Unfortunately, current control software is often optimized for a specific system at the cost of flexibility and portability. We propose a systematic design strategy for modular real-time quantum control software and demonstrate that modular control software can reduce the execution time overhead of kernels by 633% on average while not increasing the binary size. Our analysis shows that modular control software for two distinctly different systems can share between 49.8% and 91.0% of covered code statements. To demonstrate the modularity and portability of our software architecture, we run a portable randomized benchmarking experiment on two different ion-trap quantum systems.

关键词： real-time control systems modular software software portability quantum computing

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The Role of Social Psychology in Human-computer Interaction in Teaching Systems

The Role of Social Psychology in Human-computer Interaction ...

引用

Information Science and Education (ICISE-IE), international conference on

作者： Tao Sun Lan Luo Guang Chen School of Humanities Jiangxi University of Chinese Medicine Nanchang Jiangxi China School of Economics and Management Jiangxi University of Chinese Medicine Nanchang Jiangxi China

Under the trend of digitalization and informatization of teaching systems, the design and application of human-computer interaction technology reflects the psychology of human as a social being. The design of the human-computer interaction process in the informatization and digital teaching system collects information and data by establishing usage feedback, enhances the user experience in the human-computer interaction process through computer vision technology, visualizes the information with Focus+Context technology, and deeply integrates the user with the digital teaching system by establishing a model of the user’s demand for the teaching system. In addition, according to the user’s psychology when maintaining attention to the system, the network feature model of CSP+CBAM is constructed by using Yolov4-Tiny network, which integrates spatial attention and channel attention into the network of CBAM.

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Density vision: Crowd Classification using Deep Learning

Density Vision: Crowd Classification using Deep Learning

引用

Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Harsh Vardhan Singh S. Vidhya G Santosh Kumar Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Estimating crowd density and counting people are essential for crowd control, urban planning, and public safety. This research study utilizes a Multi-Column Convolutional Neural Network (MC-CNN) as a crowd counting technique trained on crowd datasets. The MC-CNN predicts crowd density maps from input images, providing precise crowd density estimates. Additionally, post-processing methods like clustering analyze the spatial crowd distribution. The proposed system incorporates object detection algorithms to generate individual coordinates, aiding in crowd analysis through clustering and then classifying them into three categories i.e. sparse, medium, and dense. Experimental findings showcase the method’s efficacy in accurately estimating crowd densities and discerning crowd patterns. The proposed system provides valuable insights for crowd monitoring, resource allocation, and decision-making in densely populated areas.

关键词： Urban planning Refining Neural networks Estimation Object detection Prediction algorithms Public security

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Comprehensive State-of-the-Art of Vehicle-To-Grid Technology

Comprehensive State-of-the-Art of Vehicle-To-Grid Technology

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Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Abdulgader Alsharif Abdussalam Ali Ahmed Mohamed Mohamed Khaleel Ahmed Salem Daw Alarga Omer. S. M. Jomah Ibrahim Imbayah Faculty of Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Malaysia Mechanical Engineering Department Bani Waleed University Bani Waleed Libya Department of Electrical-Electronics Engineering Karabuk University Karabuk Turkey Department of Computer Science Elmergib University Khums Libya Libyan Academy For Postgraduate Studies Tripoli Libya Department Energy Engineering College of Renewable Energy Tajoura Libya

A vehicle is a means of transportation, such as a car, truck, or train, that is capable of moving people or goods from one place to another. Vehicles can be classified based on various factors, such as the type of fuel they use (e.g. gasoline, diesel, electricity), the number of wheels they have (e.g. two, four, six), and their intended use (e.g. passenger transportation). Vehicles may have connectors, such as plug sockets or fuel ports, that allow them to be connected to other devices or systems to form Vehicle-to-Everything (V2X) technology. For example, an Electric Vehicle (EV) may have a charging port that allows it to be connected to an electric power source to recharge its batteries such Vehicle-to-Grid (V2G) as one of the V2X forms. One of the challenges in charging EVs is the availability of charging infrastructure. In many places, there are relatively few public charging stations, which can make it difficult for EV owners to find a place to charge their vehicles when they are away from home. Additionally, charging an electric vehicle can take significantly longer time than filling up a gasoline-powered vehicle, which can be inconvenient for some drivers. In this review, the various topologies of V2X, connectors, charging challenges, and EV impact types on the grid are conducted.

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Intrusion Detection using Deep Learning Techniques

Intrusion Detection using Deep Learning Techniques

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Innovations in computer Science & Software Engineering (ICONICS), international conference on

作者： Syeda Fareeha Batool Faisal Rehman Hanan Sharif Maheen Jaffer Anza Gul Sameen Butt Department of Computer Science & Information Technology Lahore Leads University Lahore Pakistan

All firms use intrusion detection systems as a fundamental element of their cyber security procedures. As more and more information become accessible in digital form on the internet, the demand for robust cyber security measures to protect against data breaches and malware assaults has grown. Automated intrusion detection systems are needed to keep pace with the ever-increasing number of assaults and new malware varieties. computer vision, natural language processing, and voice recognition are all examples of applications of Deep Learning methods that are currently being used in the realm of cyber security. An in-depth analysis of twenty-three papers using Deep Learning in intrusion detection systems is performed in this study.

关键词： Deep learning Recurrent neural networks Software algorithms Intrusion detection Speech recognition Malware Natural language processing

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Unsupervised Cell Segmentation in Fluorescence Microscopy Images via Self-supervised Learning 3rd

Unsupervised Cell Segmentation in Fluorescence Microscopy I...

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3rd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Krug, Carola Rohr, Karl Biomedical Computer Vision Group BioQuant IPMB Heidelberg University Heidelberg Germany

ISBN: (纸本)9783031090363

Cell segmentation in microscopy images is challenging particularly when only few or no annotations available. Existing unsupervised deep learning-based segmentation methods rely on large data sets to train large networks, use synthetic training data, pre-trained networks for domain adaptation, or exploit labels to further train pre-trained networks. We propose an unsupervised deep learning method which is trained from scratch by self-supervised learning without requiring any segmentation labels. Our deep neural network generates an attention map and performs the auxiliary task in one network. The segmentation result is directly obtained from the network, and model selection is performed unsupervised based on the behavior of the loss function during training. We applied our approach to two different fluorescence microscopy data sets and achieved generally better or similar results than classical unsupervised segmentation methods. Furthermore, we compared our method to a supervised method trained with a different number of labels, as well as a semi-supervised version of our method where we select the model based on few annotations. © 2022, Springer Nature Switzerland AG.

关键词： Fluorescence microscopy

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People Detecting and Counting System 3

People Detecting and Counting System

引用

IEEE 3rd Global conference on Life Sciences and Technologies (IEEE LifeTech)

作者： Myint, Ei Phyu Sein, Myint Myint Univ Comp Studies Yangon Myanmar

ISBN: (纸本)9781665418751

In present days, people detecting and counting is an important aspect in the video investigation and subjective demand in computer vision systems. For many applications, it is necessary to identify people and then accurately count the number of people in real time or near real time. To perform people counting, a robust system for people detection is needed. The system is designed to be able to calculate the number of people entering and exiting a room. The system is implemented using a raspberry pi camera to capture images, laptop to train a model for specific dataset, and raspberry pi 3 model B to apply the model, count detected person and send the number of people going in and out of room to the web server. Information from people counting support the retail shops to analyze customer visit patterns. In addition, it can also give useful information in the implementation of internet of things for smart rooms or smart buildings such as automation of room lights.

关键词： people detecting people counting raspberry pi 3 model B

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Selective Unlearning in Face Recognition: Forgetting Faces without Compromising Accuracy

Selective Unlearning in Face Recognition: Forgetting Faces w...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Adithyan M Nair Akshit Sudheer Kumar Devakrishna Sanil Kumar Anjali T Department of Computer Science Amrita School of Engineering Amritapuri India

Facial recognition technology is increasingly being used in current applications ranging from smartphone unlocking to security and surveillance systems. This extensive adoption, however, has generated severe concerns about individual privacy and data policies. In response to these concerns, this study introduces a novel methodology that gives people more control over their data in facial recognition systems. This study’s main contribution is the proposal of a strategy for selectively unlearning certain faces from trained facial recognition algorithms. An iterative model adaption approach is used to achieve this selective unlearning process. It becomes feasible to empower individuals to control the presence of their facial data in these systems by iteratively fine-tuning the model. This not only improves individual privacy but also conforms with ethical technology development standards. Furthermore, the methodology presented in this study is an important step towards improving privacy in the age of ubiquitous facial recognition. It tackles increasing concerns about surveillance, tracking, and unauthorized use of personal data by adapting and personalizing facial recognition algorithms to accommodate individual tastes. This study provides an innovative answer to the ethical and privacy concerns raised by facial recognition technologies. It empowers users to exercise greater control over their privacy and data in the face of increasingly prevalent facial recognition technologies by allowing them to selectively unlearn their data from training algorithms.

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Nesterov-Type Accelerated ADMM (N-ADMM) with Adaptive Penalty for Three-Phase Distributed OPF under Non-Ideal Data Transfer Scenarios

Nesterov-Type Accelerated ADMM (N-ADMM) with Adaptive Penalt...

引用

Smart Technologies for Power, Energy and control (STPEC), IEEE international conference on

作者： Subho Paul Biswarup Ganguly Soumya Chatterjee School of Electrical Engineering and Computer Science Washington State University Pullman WA USA Department of Electrical Engineering National Institute of Technology Silchar Silchar Assam India Electrical Engineering Department National Institute of Technology Durgapur Durgapur WB India

In the smart grid era, distributed algorithms are paving their way to solve optimal power flow (OPF) problems in lesser computation complexity. In distributed algorithms, the original centralized problem is decomposed into multiple subproblems and they are solved in parallel by enabling coordination among them. In this context, the alternating direction method of multiplier (ADMM) has proved itself superior over other methods and is used extensively for power networks. However, vanilla ADMM completes a large number of macro-iterations $(\approx 10^{2})$ before convergence. Therefore, to reduce the number of iterations and the solution time, in this article the concept of predictor-corrector acceleration, proposed by Nesterov, is merged with the vanilla ADMM for solving three phase OPF problem of unbalanced distribution networks. The convergence speed is further improved by utilizing the concept of adaptive penalty. The efficacy of the proposed Nesterov-type accelerated ADMM (N-ADMM) with adaptive penalty is established by implementing on IEEE 123 bus test system. Again, two non-ideal data transfer scenarios, viz. bad and noisy data transfer, are modeled to show their impacts on the N-ADMM algorithm.

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Development of a Real Time vision-Based Hand Gesture Recognition System for Human-computer Interaction

Development of a Real Time Vision-Based Hand Gesture Recogni...

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IEEE Applied Signal Processing conference (ASPCON)

作者： Arijit Das Kaulik Maitra Shayan Roy Biswarup Ganguly Meghna Sengupta Shreya Biswas Electrical Engineering Department Meghnad Saha Institute of Technology Kolkata India Electrical Engineering Department National Institute of Technology Silchar Assam India Clavictor Academy Private Limited National Institute of Technology Rourkela Odisha India

Speech impairment, a debility that affects an individual’s capacity to communicate via talking and hearing. Therefore, sign language is essential for facilitating communication with individuals who are deaf and mute. American Sign Language (ASL) is widespread in the application of sign language worldwide, with regional variations. Our proposed system is a vision-based hand gesture recognition software, where the camera and the software will observe the set of sign language or gestures and will convert it into normal text. So, with the help of this software, we can easily communicate with a mute person. In the proposed system we used ASL as the language dataset and YOLO-v5 as the main algorithm model. We also included a ‘BOT’ control with our gesture recognition system of hand for a better understanding of the power of the model and different application abilities of hand gesture recognition. Service bots have one-on-one conversations with users and discover "natural" and intuitive interfaces. The main highlighting point is we can use a custom dataset as per our flexibility, for example, we can move the bot forward by giving a sign like ‘1’ instead of sign language like ‘A’ or ‘B’. We captured nearly 700 pictures of sign language with different backgrounds and trained it where we got an overall 93% accuracy in the experiment.

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