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Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants

Remote Sensing in Earth Systems Sciences 2025年第2期8卷 387-399页

作者： Kolikipogu, Ramakrishna Shivaputra Muniyandy, Elangovan Maroor, Jnaneshwar Pai Lakshmi, Gundala Venkata Rama Konduri, Bhagavan Naveenkumar, R. Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India

Web different IoT apps offer lot more options for every player in the meat supply chain to monitor, control, and optimize their manufacturing method. Beginning within late 2017, industrial IoF2020 program will concentrate toward massive deployments of IoT via three use cases in the meat supply chain: pig farm management, poultry chain tracking, and for livestock visibility with openness. This initiative will include eleven collaborators spanning 5 nations. Installing and integrating IoT sensors for environmental and livestock management would alleviate producers’ difficulty getting knowledge that data needed regarding continuously tracking how well they are doing. Alert detection is additionally being created, integrating multiple sources of data in order to give farmers helpful input along with communicating news with other partners. By carrying out this, preventative or remedial measures can be done for infections, swine taint, bird mortality, feed waste, the natural world, et cetera. Additionally, EPCIS-based traceability from farm to fork will be allowed to ensure customers may get precise data about the source and caliber of their livestock. Each of these three use scenarios’ existing stage in work along with future plans are expected to be discussed. IoF2020 seeks to support the digital revolution in smart farming by tackling various educational and commercial problems while also promoting its use throughout Europe. It maintains mechanical necessary degree and security against physical along with toxic contaminants during industrial butchering of chicken, a lot measures have been taken. Buyers’ worries about food safety and health are the result of eating chicken increasingly. Our authorities have begun many kinds of initiatives aimed at improving safeguards for food. Our goal is to create an apparatus that alerts the factory farms affecting potential threats for product hygiene of food. With such setup, the detector can be mounted on top of the poultry slaugh

关键词： Food safety Internet of Things Meat processing Monitoring Supply chain

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Acoustic Eavesdropping from Sound-Induced Vibrations with Multi-Antenna mmWave Radar

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Wenhao Spolaor, Riccardo Luo, Chuanwen Sun, Yuchao Chen, Huashan Zhang, Guoming Yang, Yanni Cheng, Xiuzhen Hu, Pengfei Shandong University School of Computer Science and Technology China Beijing Forestry University School of Information Science and Technology China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration China 3rd Research Institute of China Electronics Technology Group Corporation China Institute of Information Engineering Chinese Academy of Sciences China

Acoustic eavesdropping against private or confidential spaces is a significant threat in the realm of privacy protection. While the presence of soundproof material would weaken such an attack, current eavesdropping technology may be able to bypass these protections. Fortunately, existing studies either inadequately cover the full spectrum of human speech due to low-frequency responses or rely heavily on the prior knowledge used to train a model. To address these challenges, this paper introduces mmEcho, a new acoustic eavesdropping method that utilizes millimeter-wave signals to sense vibration induced by sound precisely. Through signal processing techniques such as the intra-chirp scheme and phase calibration algorithm, mmEcho achieves micrometer-level vibration extraction without requiring target-related data. To improve the range of eavesdropping attacks while reducing noise, we optimize radar signals by leveraging the widespread availability of multiple antennas on commercial off-the-shelf radars. We comprehensively evaluate the performance of mmEcho in different real-world settings. Experimental results demonstrate that, with the aid of multi-antenna technology, mmEcho can more effectively reconstruct the audio from the target at various distances, directions, sound insulators, reverberating objects, sound levels, and languages. Compared to existing methods, our approach provides better effectiveness without prior knowledge, such as the speech data from the target. © 2025 IEEE. All rights reserved.

关键词： Speech enhancement

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Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

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2nd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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A Sensor-Based Deep Learning Approach for Recognizing Daily and Work Activities in Open Environments for Sanitation Workers 9

A Sensor-Based Deep Learning Approach for Recognizing Daily ...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Sanitation workers play a crucial role in maintaining public hygiene and cleanliness. Understanding their daily routines and job responsibilities is essential for improving their work environment, task distribution, and overall supervision. However, tracking sanitation workers in open environments remains a persistent challenge. This paper introduces a deep learning model named CNN-BiGRU-CBAM, designed to recognize sanitation workers' daily and working activities using smartwatch sensors. The model leverages advanced convolutional neural networks to extract highly informative features from multi-modal sensor data, including acceleration information, collected from smartwatches. The dataset used in this study is carefully curated and collected from sanitation workers during their daily work routines, encompassing activities such as walking, running, sweeping, lifting, and driving vehicles. These activities are systematically categorized to cover both work-related tasks and non-work activities. Experimental results demonstrate that the proposed model consistently achieves an average F1-score of over 94.50% across seven activity groups, outperforming baseline deep learning methods by an impressive margin of 5.62%. The model excels in accurately identifying operations in diverse and dynamic work settings. This research highlights the potential of smartwatches and deep learning in continuously analyzing sanitation workers' duties and developing improved working regulations that consider their well-being and daily tasks. © 2024 IEEE.

关键词： Sanitation

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Enhancing Sensor-based Human Activity Recognition Using Hybrid Deep Learning and Data Augmentation 5

Enhancing Sensor-based Human Activity Recognition Using Hybr...

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5th research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Faculty of Applied Science Department of Applied Statistics Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798331505998

Human Activity Recognition (HAR) is essential in various applications, including wellness tracking, automated residences, and fitness monitoring. In the past few decades, sensor-based HAR has become increasingly popular due to advancements in technologies for sensors. Nevertheless, HAR networks' effectiveness dramatically depends on the caliber and volume of the training data, which is frequently restricted and unevenly distributed. This research introduces a novel deep learning method called Multihead-CNN-BiGRU, which integrates one-dimensional convolutional neural networks with bidirectional gated recurrent units (BiGRU) to improve the accuracy of sensor-based HAR. To tackle the problem of insufficient a nd uneven training data, we utilize the synthetic minority over-sampling technique (SMOTE) to augment the data. The suggested model is assessed using the publicly accessible WISDM dataset, which comprises sensor data from diverse human actions. The ID-CNN is employed for extracting localized characteristics from the sensor data, while the BiGRU gathers temporal dependencies and contextual information. The hybrid architecture allows the model to acquire spatial and temporal patterns efficiently. In addition, the SMOTE technique is utilized to create artificial samples of the underrepresented classes, thus equalizing the distribution of classes and enhancing the model's capacity to generalize. The experimental findings show that our hybrid strategy provides exceptional outcomes when paired with SMOTE data augmentation. It obtains the most excellent accuracy of 99.51 % and the highest F1-score of 99.49% compared to the most advanced approaches. The suggested framework provides a reliable and precise solution for sensor-based HAR, which opens up opportunities for improved applications in other fields. © 2024 IEEE.

关键词： Spatio-temporal data

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Design oligoporous-core based multimode fiber for mode division multiplexing applications

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Optical and Quantum Electronics 2025年第5期57卷 1-24页

作者： Mitu, Sumaiya Akhtar Abdulrazak, Lway Faisal Ibrahim, Sobhy M. Tahhan, Shaymaa R. Hossain, Md Bellal Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering University of Asia Pacific 74/A Green Rd Dhaka1205 Bangladesh Department of Information and Communication Technology Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Laser and Optoelectronics Engineering College of Engineering Al-Nahrain University Jadriya Baghdad64074 Iraq School of Electrical and Information Engineering The University of Sydney SydneyNSW Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

A polarization-maintaining oligoporous-core-based multi-mode fiber is proposed. By tuning the air hole, as well as the core number, shape, size, and position up to 28 distinct linearly polarized (LP) modes are obtained. The Finite Element Method (FEM) is used to perform the numerical investigations. In addition, various materials combinations are used as a doping with silica which is highly helpful to increase or decrease the refractive index of the core material. The multimode fiber is identified by the normalized frequency or V parameter. Besides, the high birefringence value, low loss value, minimum crosstalk with high sensitivity response of 1.46×10-2, 2×10-11dB/m, 41.80 dB and 88,280.46 nm/RIU, respectively, are achieved from the numerical investigations over the wavelength range from 1.55 µm to1.65 µm for the different LP modes. Moreover, good responses also obtain for the numerical aperture, V number, coupling length and other parameters. In the end, every value reveals both an easy-to-fabricate structure design as well as adequate performance analysis. The suggested fiber structure can support many modes and might be applicable in the field of optical communications and spatial multiplexing based on the user demand. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fiber to the x

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Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wearable Activity Recognition 9

Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wear...

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ISBN: (纸本)9798350318241

The use of widespread sensors for automatic activity recognition has become a dynamic area of research due to its broad applications in healthcare, fitness monitoring, and assisted living. To efficiently identify daily and sports activities from sensor data, this study suggests a hybrid deep learning model that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and convolutional block attention modules (CBAM). The model starts by extracting spatial features from the sensor data using CNN layers. These spatial features are then employed to offer temporal context information to the BiLSTM network. To conclude, the CBAM attention mechanism is applied to guide the model's focus to the most informative segments of the BiLSTM feature maps. This approach enables the accurate recognition of patterns in both complex and daily sports activities. The effectiveness of the proposed hybrid model is evaluated using the UCI-DSA, a publicly available benchmark dataset that includes sports and daily activities collected through wearable motion sensors. In contrast to existing approaches, the outcomes reveal that a unified framework involving CNN, BiLSTM, and CBAM achieves outstanding performance in activity recognition, exceeding 99 % in both accuracy and F1-score while maintaining computational efficiency. © 2024 IEEE.

关键词： Wearable sensors

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Detecting Face-Touching Gestures with Smartwatches and Deep Learning Networks 47

Detecting Face-Touching Gestures with Smartwatches and Deep ...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

ISBN: (纸本)9798350365597

Everyday actions like scratching one's nose or resting the chin on one's hand may facilitate the spread of germs and diseases. Detecting these gestures holds the potential for innovative health monitoring and disease prevention applications. However, the identification of face-touching poses challenges due to the variety of human gestures and the limitations in accuracy associated with wearable sensors. This study introduces an original deep-learning system to identify face-touching gestures using standard smartwatches' inertial measurement unit sensors. The system on the smartwatch captures and pre-processes multi-channel time-series data from the accelerometer to generate robust input features. We utilize a benchmark, the Face Touching dataset, to evaluate the recognition performance of deep learning networks, including our proposed network. Our approach proposes a hybrid deep residual network architecture tailored for sequence-based gesture classification using signals from the smartwatch sensors. In our experiments, the developed deep learning framework achieves an impressive F1-score of 97.53% in detecting face-touch gestures. The suggested system takes a step forward in advancing the practical applications of face-touch gesture detection to smartwatch-based health sensing and disease prevention. © 2024 IEEE.

关键词： Network architecture

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