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Forum on Specification, Verification and Design Languages, FDL

作者： Luigi Capogrosso Alessio Mascolini Federico Girella Geri Skenderi Sebastiano Gaiardelli Nicola Dall'Ora Francesco Ponzio Enrico Fraccaroli Santa Di Cataldo Sara Vinco Enrico Macii Franco Fummi Marco Cristani Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina at Chapel Hill USA

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh

A novel approach incorporating feature extraction followed b...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Debashis Kundu Md. Mushfiqur Rahaman Md. Nazmus Sakib Md. Faruk Abdullah Al Sohan Jannatul Fardaush Mithu Afzal Hossain Nishita Aktar Dept. Management Information Systems American International University- Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil Institute of IT Dhaka Bangladesh Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science American International University- Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering Army Institute of Business Administration (AIBA) Savar Dhaka Bangladesh

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation’s agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database.

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Enhancing Communication through Speech to Sign Language Converter

Enhancing Communication through Speech to Sign Language Conv...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Gayana M N Mohammed Rumaiz Shaz Rasheed Hussain Shravya A Shreenath Acharya Sana Parveen Dept. of Intelligent Computing & Business Systems St Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering St Joseph Engineering College Mangaluru India

There are millions of people suffering from speaking and hearing disabilities. Sign language is the only way to communicate for such people to convey their message. The advancement in technology has highly impacted this section of society, hence there is a need to deal with them. This paper describes the Application, "Speech to Sign Language Converter" for communicating with people having hearing and speaking impairments. The primary function of the Application is to convert input sentences into Indian Sign Language (ISL) actions in real time. It takes input in the form of speech or text and produces corresponding sign language using an Avatar which acts based on SiGML code. It can also be used to convert PDF based texts into sign language. In addition, the users can learn Indian Sign Language (ISL) using the tutorials provided within it. The application focuses mainly on converting local languages such as Kannada into ISL.

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Exploiting Information Theory for Intuitive Robot Programming of Manual Activities

arXiv

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arXiv 2024年

作者： Merlo, Elena Lagomarsino, Marta Lamon, Edoardo Ajoudani, Arash Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

Observational learning is a promising approach to enable people without expertise in programming to transfer skills to robots in a user-friendly manner, since it mirrors how humans learn new behaviors by observing others. Many existing methods focus on instructing robots to mimic human trajectories, but motion-level strategies often pose challenges in skills generalization across diverse environments. This paper proposes a novel framework that allows robots to achieve a higher-level understanding of human-demonstrated manual tasks recorded in RGB videos. By recognizing the task structure and goals, robots generalize what observed to unseen scenarios. We found our task representation on Shannon’s Information Theory (IT), which is applied for the first time to manual tasks. IT helps extract the active scene elements and quantify the information shared between hands and objects. We exploit scene graph properties to encode the extracted interaction features in a compact structure and segment the demonstration into blocks, streamlining the generation of Behavior Trees for robot replicas. Experiments validated the effectiveness of IT to automatically generate robot execution plans from a single human demonstration. Additionally, we provide HANDSOME, an open-source dataset of HAND Skills demOnstrated by Multi-subjEcts, to promote further research and evaluation in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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Method for Increasing the OpAmp’s Speed on the Basis of Complementary “Folded” Cascodes

Method for Increasing the OpAmp’s Speed on the Basis of Com...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Dmitriy V. Kleimenkin Ilya V. Pakhomov Darya Y. Denisenko dept. of Scientific Research dept. of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia dept. of Scientific Research dept. of Instrumentation and Biomedical Engineering Don State Technical University Rostov-on-Don Russia dept. of Scientific Research Don State Technical University Rostov-on-Don Russia dept. of Automatic Control Systems Southern Federal University Rostov-on-Don Russia

The paper presents a promising method of increasing by more than an order of magnitude the maximum slew rate (SR) of integrated operational amplifiers (OpAmps) based on two-cycle “folded“ cascodes. This method is based on the introduction of special differentiating transient correction circuits into the classical circuits of OpAmps. Developed on the basis of the MH2XA031 array chip, the high-speed OpAmp is recommended for practical use in the subclass of so-called discrete-analog SC-filters on switched capacitors, for which (in some important cases) higher SR values are required. The effect of significantly improving the OpAmp’s speed is to provide higher output current levels of the two-cycle “folded” cascode that recharges the integrating correction capacitor during the transient edge. The OpAmp’s speed performance in the large-signal mode is increased from $248.7 \mathrm{~V} / \mu \mathrm{s}$ to $4148 \mathrm{~V} / \mu \mathrm{s}$ as a result of this.

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SimBusters: Bridging Simulation Gaps in Intelligent Vehicles Perception

SimBusters: Bridging Simulation Gaps in Intelligent Vehicles...

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IEEE Symposium on Intelligent Vehicle

作者： Alberto Justo Javier Araluce Javier Romera Mario Rodriguez-Arozamena Leonardo González Sergio Díaz TECNALIA Basque Research and Technology Alliance (BRTA) Derio Spain Dept. of Computer Systems Engineering Mondragon University Bilbao Spain

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Recent advances in automated vehicle technology rely heavily on simulated environments for training and testing. However, a significant challenge lies in bridging the gap between simulated and real-world scenarios, as discrepancies between these environments can affect the performance and reliability after that transition, especially in perception. Particularly, LiDAR sensors are highly affected in this matter due to disparities in pointcloud distribution and intensity. Therefore, this paper presents an innovative approach to bridge the gap between simulation and reality. For it, we test and validate a realistic LiDAR library, PCSim, within the CARLA simulator, providing an enhanced simulation environment. Our method involves integrating perception models, pre-trained on real-world datasets, in this environment. Then, we develop a Real2Sim domain adaptation method to transfer these models into the library, leveraging their performance. Finally, we evaluate the 3D object detection models in PCSim LiDARs to prove our *** have assessed this proposal in PCSim, obtaining promising results in mitigating the simulation-reality gap. Our evaluations provide a guidance for future effective transition from virtual environments to real-world applications.

关键词： Training Adaptation models Solid modeling Laser radar Three-dimensional displays Intelligent vehicles Virtual environments

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Predictive control and Production Process Forecasting Under Deterministic Chaos 23

Predictive Control and Production Process Forecasting Under ...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Manusov, Vadim Z. Orlov, Dmitry V. Karmanov, Vitaly S. Khusnutdinov, Alexander O. Kovalenko, Dmitry I. Ivanov, Gennady V. Novosibirsk State Technical University Dept. of Industrial Power Supply Systems Novosibirsk Russia Novosibirsk State Technical University Dept. of Theoretical and Applied Computer Science Novosibirsk Russia Surgut State University Dept. of Radio Electronics and Radio Engineering Surgut Russia

ISBN: (纸本)9781665498043

Electricity consumption forecasting is an integral part of the workflow in most industries. At the same time, daily load schedules of the mining industry are characterized by deterministic chaotic fluctuations in the electricity consumption curve caused by the technology of the production process. The paper proposes two mathematical forecasting models, one of which is based on the recurrent method, and the second one is based on the vector method. At the same time, the structure of the models allows taking into account the individual features of the operating cycle of the production process, as well as identifying and smoothing the 'noise' components of the rank parametric surface of this production process. The Singular Spectrum Analysis method was chosen as the algorithm for preprocessing and smoothing time series of electricity consumption. The method was applied for the first time for a multidimensional array of electricity consumption data. As a result, rank parametric surfaces were plotted, one of which is the initial surface, and the second one is the final surface. © 2022 IEEE.

关键词： Energy conservation

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A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data 57

A Decision Making Model Where the Cell Exhibits Maximum Dete...

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57th Annual Conference on Information Sciences and systems, CISS 2023

作者： Emadi, Ali Lipniacki, Tomasz Levchenko, Andre Abdi, Ali New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland Yale University Yale Systems Biology Institute Dept. of Biomedical Eng. New HavenCT United States New Jersey Institute of Technology Dept. of Elect. Comput. Eng. Dept. of Biological Sciences NewarkNJ United States

ISBN: (纸本)9781665451819

Molecular noise and signaling abnormalities in biochemical signaling systems in cells affect signaling events and consequently may alter cellular decision making results. Since unexpected and altered cellular decisions may contribute to the development of many pathological conditions and diseases, it is of interest to develop proper models to characterize and measure molecular signal detection parameters and cellular decisions. In this paper and using the Neyman-Pearson signal detection theorem, we propose a signal detection model in which the cell maximizes its signal detection probability in the presence of noise. To evaluate the usefulness of the proposed model, we use measured molecular experimental data of the important TNF-NF-κB cell signaling system. Our results demonstrate that the proposed model provides biologically relevant findings. The introduced Neyman-Pearson-based molecular signal detection framework allows to systematically model and quantify the signal detection behavior and failure of molecular signaling systems, and compute their key decision making parameters such as detection and false alarm probabilities. With regard to the specific TNF-NF-κB system case study in this paper and given the high involvement of the transcription factor NF-κB in cell survival, programmed cell death, immune signaling and stress response, the developed signal detection framework can serve as a useful tool to model the associated cell decision making processes. © 2023 IEEE.

关键词： Signal detection

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Facial Shape-Based Eyeglass Recommendation Using Convolutional Neural Networks

Facial Shape-Based Eyeglass Recommendation Using Convolution...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Rakib Hossain Rifat Sunzida Siddique Laxmi Rani Das Mohd Ariful Haque Dept. of Computer Science and Engineering BRAC University Mohakhali Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Sonapur Bangladesh Dept. of Cyber-Physical Systems Clark Atlanta University Atlanta Georgia

Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape; to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear.

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Cascaded Deep CNN Network for Breast Cancer Screening from Ultrasound Images Using Differential Evolution Algorithm

Cascaded Deep CNN Network for Breast Cancer Screening from U...

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Digital Society and Intelligent systems (DSInS), International Conference on

作者： Wahidur Rahman Md Shadman Sakib Md Solaiman Hosen Mir Mohammad Azad Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan Asadullah Shaikh Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh Department of Computer Science Iowa State University USA Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science College of Computer Science and Information Systems Najran University Saudi Arabia Emerging Technologies Research Lab (ETRL) College of Computer Science and Information Systems Najran University Saudi Arabia Department of Information Systems College of Computer Science and Information Systems Najran University Saudi Arabia

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Because of poor blood flow and susceptibility to infections, breast cancer can be challenging to heal and, if not treated, might cause major problems, including limb amputation and a lowered quality of life. Though several systems exist to identify breast cancer, few combine machine learning (ML), deep learning (DL), and optimization strategies. This work presents a novel method leveraging complex algorithms to precisely identify breast cancer from ultrasound images. The study utilizes a dataset from a secondary source, categorizing it into three identical classes: normal (Class 0), benign (Class 1), and malignant (Class 2). It extracts features using pre-trained convolutional neural networks (CNNs), optimizes these features using the Differential Evolution Algorithm (DEA), and classifies images using standard machine learning algorithms. To show how well it detects breast cancer, the approach combines seven conventional ML classifiers with ten pre-trained CNN models. Using DenseNet121 + DEA + Xtreme Gradient Boosting (XGB) Classifier, DEA chooses important features derived by CNNs with a high accuracy of 97.76%. However, the proposed method can be very effective for real-time breast cancer screening using ultrasound images.

关键词： Limbs Ultrasonic imaging Machine learning algorithms Feature extraction Breast cancer Real-time systems Classification algorithms Convolutional neural networks Standards Optimization

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