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arXiv 2024年

作者： Kelishami, Mahmood Saeedi Kelishami, Amin Saeidi Kelishami, Sajjad Saeedi Department of Mathematics Rasht Branch Islamic Azad University Iran Department of Computer Engineering Sharif University of Technology Iran Department of Architecture Rasht Branch Islamic Azad University Iran

This study introduces an innovative approach to classifying various types of Persian rice using image-based deep learning techniques, highlighting the practical application of everyday technology in food categorization. Recognizing the diversity of Persian rice and its culinary significance, we leveraged the capabilities of convolutional neural networks (CNNs), specifically by fine-tuning a ResNet model for accurate identification of different rice varieties and employing a U-Net architecture for precise segmentation of rice grains in bulk images. This dual-methodology framework allows for both individual grain classification and comprehensive analysis of bulk rice samples, addressing two crucial aspects of rice quality assessment. Utilizing images captured with consumer-grade cell phones reflects a realistic scenario in which individuals can leverage this technology for assistance with grocery shopping and meal preparation. The dataset, comprising various rice types photographed under natural conditions without professional lighting or equipment, presents a challenging yet practical classification problem. Our findings demonstrate the feasibility of using non-professional images for food classification and the potential of deep learning models, like ResNet and U-Net, to adapt to the nuances of everyday objects and textures. This study contributes to the field by providing insights into the applicability of image-based deep learning in daily life, specifically for enhancing consumer experiences and knowledge in food selection. Furthermore, it opens avenues for extending this approach to other food categories and practical applications, emphasizing the role of accessible technology in bridging the gap between sophisticated computational methods and everyday tasks. © 2024, CC BY.

关键词： Convolutional neural networks

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Ml-experiment: A Python framework for reproducible data science 4

Ml-experiment: A Python framework for reproducible data scie...

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4th International Conference on Applied Physics, Simulation and Computing, APSAC 2020

作者： Molner Domenech, Antonio Guillén, Alberto Computer Architecture and Technology Department University of Granada Spain

Nowadays, data science projects are usually developed in an unstructured way, which makes it difficult to reproduce. It is also hard to move from an experimental environment to production. Operational workflows such as containerization, continuous deployment, and cloud orchestration allow data science researchers to move a pipeline from a local environment to the cloud. Being aware of the difficulties of setting those workflows up, this paper presents a framework to ease experiment tracking and operationalizing machine learning by combining existent and well-supported technologies. These technologies include Docker, Mlflow, Ray, among others. The framework provides an opinionated workflow to design and execute experiments either on a local environment or the cloud. ml-experiment includes: an automatic tracking system for the most famous machine learning libraries: Tensorflow, Keras, Fastai, Xgboost and Lightgdm, first-class support for distributed training and hyperparameter optimization, and a Command Line Interface (CLI) for packaging and running projects inside containers. © Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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Practice research of the training mode of students' innovative and entrepreneurial talents based on computer courses 16

Practice research of the training mode of students' innovati...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Zhou, Yi Yu, Zhe Yu, Kang-Bo Wang, He Chen, Ling-Ling Changshu Institute of Technology College of Computer Science and Engineering SuZhou China Harbin Branch of United Network Telecommunications Corporation Harbin China Central China University Technology School of Architecture and Art ChangSha China Changshu Institute of Technology College of Computer Science and Technology SuZhou China

ISBN: (纸本)9781665414685

At present, the innovation and entrepreneurship education system of college students in colleges and universities in our country is faced with the problems of single resources, disconnect from majors and emphasis on theory and practice, which seriously restrict the effect and efficiency of college students' innovation and entrepreneurship education. This paper puts forward the use of service learning in the teaching of computer courses to carry out teaching activities, through the revision of talent training programs and curriculum, optimize innovative entrepreneurship education resources, build innovative entrepreneurship education information platform and other measures to strengthen the management and implementation of innovative entrepreneurship education for college students, improve the effectiveness and efficiency of innovative entrepreneurship education in colleges and universities. © 2021 IEEE.

关键词： Students

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge Computing

Energy-Efficient and Reliable Federated Learning in Heteroge...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yan Xu Liying Li Peijin Cong Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge Computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. Then, we design a highly reliable device selection scheme based on devices’ reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods.

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Optimal Qubit Reuse for Near-Term Quantum computers

Optimal Qubit Reuse for Near-Term Quantum Computers

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Sebastian Brandhofer Ilia Polian Kevin Krsulich Institute of Computer Architecture and Computer Engineering and Center for Integrated Quantum Science and Technology University of Stuttgart Stuttgart Germany IBM Quantum IBM TJ Watson Research Center Yorktown Heights NY USA

Near-term quantum computations are limited by high error rates, the scarcity of qubits and low qubit connectivity. Increasing support for mid-circuit measurements and qubit reset in near-term quantum computers enables qubit reuse that may yield quantum computations with fewer qubits and lower errors. In this work, we introduce a formal model for qubit reuse optimization that delivers provably optimal solutions with respect to quantum circuit depth, number of qubits, or number of swap gates for the first time. This is in contrast to related work where qubit reuse is used heuristically or optimally but without consideration of the mapping effort. We further investigate reset errors on near-term quantum computers by performing reset error characterization experiments. Using the hereby obtained reset error characterization and calibration data of a near-term quantum computer, we then determine a qubit assignment that is optimal with respect to a given cost function. We define this cost function to include gate errors and decoherence as well as the individual reset error of each qubit. We found the reset fidelity to be state-dependent and to range, depending on the reset qubit, from 67.5% to 100% in a near-term quantum computer. We demonstrate the applicability of the developed method to a number of quantum circuits and show improvements in the number of qubits and swap gate insertions, estimated success probability, and Hellinger fidelity of the investigated quantum circuits.

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A Minimized Thickness of MIMO Antenna Based on Thin Film Substrates for UWB application

A Minimized Thickness of MIMO Antenna Based on Thin Film Sub...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Aroonmitr, Pongsathorn Rakluea, Chawalit Loedhammacakra, Wisit Rakluea, Paitoon Tangjitjetsada, Maleeya Mahatthanajatuphat, Chatree Akkaraekthalin, Prayoot Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi11000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand Siam Airport Ground Services Co. Ltd. Bangkok10210 Thailand

ISBN: (纸本)9798350332360

This paper presents a compact MIMO antenna for ultra-wideband (UWB) applications. It is created based on a thin film for an antenna substrate to minimize the antenna thickness, resulting in the antenna being utilized well on a curved surface. The film thickness is about 0.056 mm was used to design and cooperate with the rectangular slot antenna and rectangular stub. In addition, the antenna cover frequency range from 3.1 GHz to 10.6 GHz with the S11 and S21 below-10 dB and-15 dB, respectively. The antenna will be fed by 50 Ω coplanar waveguide for a transmission line. Moreover, an Envelope Correlation Coefficient (ECC) isn't more than 0.05 throughout the frequency range. The antenna radiation pattern is bidirectional all bands and the maximum gain is 4.6 dBi. The proposed antenna can be applied to the WBAN WPAN and WLAN, UWB communication systems as well. © 2023 IEEE.

关键词： Thin films

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Cloud-IoT Application for Scene Understanding in Assisted Living: Unleashing the Potential of Image Captioning and Large Language Model (ChatGPT)

Cloud-IoT Application for Scene Understanding in Assisted Li...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Deema Abdal Hafeth Gokul Lal Mohammed Al-Khafajiy Thar Baker Stefanos Kollias School of Computer Science University of Lincoln Lincoln UK School of Architecture Technology and Engineering University of Brighton Brighton UK School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

Vision is a vital sense that plays a pivotal role in our understanding of the world. The majority of our external information is acquired through our visual system, which significantly impacts various aspects of our lives, including mobility, cognitive abilities, access to information, and how we interact with both our surroundings and other individuals. Hence, individuals who need assisted living due to visual challenges are left behind and rely on human-driven image captioning services to make sense of their surroundings. In response to this challenge, we have developed a proof-of-concept system that integrates a large language model like ChatGPT to provide assistance to individuals with visual impairments in their daily lives through the utilisation of image captioning techniques. Our proposed model leverages the image captioning technique to describe the user’s environment. It is a fusion of concepts from Deep Learning and the Internet of Things, enabling it to provide more informative and enriched image captions. In this process, ChatGPT is stimulated to generate increasingly detailed and informative descriptions of images, allowing users to gain a deeper understanding of their surroundings. Our findings show that the proposed system generates captions that are contextually relevant to the visual content. These captions can assist individuals in various day-today activities, contributing to an improved quality of life.

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A Quasi FSS Reflector with Dualband Cross Dipole for 5G Base Station Antenna Application

A Quasi FSS Reflector with Dualband Cross Dipole for 5G Base...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Loedhammacakra, Wisit Tangjitjetsada, Maleeya Rakluea, Chawalit Wongsin, Norakamon Jangjing, Thinnawat Chudpooti, Nonchanutt Mahatthanajatuphat, Chatree Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi11000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand Siam Airport Ground Services Co. Ltd. Bangkok10210 Thailand

ISBN: (纸本)9798350332360

This article describes the application of a quasi-frequency selective surface (FSS) reflector to a cross-dipole antenna for a 5G base station communication system. The quasi-FSS is composed of a square patch resonator with a resonance frequency of 1800 MHz and a perfect electric conductor (PEC) that reflects electromagnetic waves of all frequencies. Moreover, it can support and reflect two linear polarizations (±45°) of a dual-band cross-dipole antenna with dual operating frequencies of 700 MHz and 1800 MHz. At 700 MHz and 1800 MHz, the impedance characteristic of the antenna with reflector is approximately 26.83% and 34.86 % for Sll and S22 ≤-10 dB (±45°). The measured peak gain of the antenna is around 5.74 dBi at 775MHz and 11.88 dBi at 1850MHz. With a quasi-FSS reflector, the proposed antenna can also create a directional radiation pattern and facilitate the utilization of a 5G mobile communication base station. © 2023 IEEE.

关键词： Frequency selective surfaces

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A Non-contact Microwave Sensor System for Characterizing a Binary-liquid Mixed Concentration

A Non-contact Microwave Sensor System for Characterizing a B...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Seewattanapon, Somporn Namsang, Apirada Lerdwanittip, Reungyot Akkaraekthalin, Prayoot Rakluea, Chawalit Duangrit, Nattapong Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Nonthaburi Thailand Civil Aviation Training Center Aeronautical Engineering Division Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Rajamangala University of Technology Thanyaburi Department of Electronics and Telecommunication Engineering Pathumthani Thailand National Astronomical Research Instituite of Thailand Centre of Observatory Operation and Engineering Chiangmai Thailand

ISBN: (纸本)9798350332360

This research presents a design and analysis of an open-loop microstrip resonator which is integrated with a microwave sensor system. It is used for measuring a binary-liquid mixed concentration of sodium chloride (NaCl) and DI-water to detect salinity level. The open-loop resonator is designed to provide a band-stop bandwidth of 1 GHz, which has operated from 2 GHz to 3 GHz. The proposed microwave sensor system consists of a microwave synthesizer, a voltage detector, and an open-loop resonator that is designed to operate at 3 GHz to characterize the salinity level of the sample liquid mixture. A liquid mixture samples of NaCl and Distill-water with 5 different concentration levels of 0%, 2%, 5%, 10%, and 20% were used to assess the salinity level for the proposed microwave sensor system. © 2023 IEEE.

关键词： Sodium chloride

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Classification of Arrhythmia ECG Signals using Convolutional Neural Network

Classification of Arrhythmia ECG Signals using Convolutional...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Marija Habijan Irena Galić Aleksandra Pizurica Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia TELIN-GAIM Faculty of Engineering and Architecture Ghent University Ghent Belgium

The electrocardiogram (ECG) has been established as a reliable tool for monitoring cardiovascular health. Vast amount of ECG recordings can pose a challenge for its processing and analysis and seeking out experts to analyze such a large amount of ECG data can deplete valuable medical resources. Recently, there has been a growing interest in automatic methods for accurate heartbeat categorization. Our motivation behind this work is to improve efficiency and accuracy for arrhythmia classification based on convolutional neural networks (CNNs). We first pre-process the input signals by normalizing them using a z-score. Following the data segmentation, the under-represented classes are oversampled using random duplication. By randomly duplicating the samples of underrepresented categories and eliminating instances from the overrepresented categories, this approach minimizes the imbalance in the training data. We propose an efficient 12-layer 1D-CNN to classify cardiac arrhythmia into five classes. We demonstrate the oversampling technique’s impact on the proposed CNN’s performance by comparing its performance before and after its application.

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