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IoT based Cold Supply Chain for Food Processing 3

IoT based Cold Supply Chain for Food Processing

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Abishek, R. Harish Chensudar, K. Lokesh, R. Jacob Paul, P. Balamurugan, Kavitha Kcg College of Technology Dept. of Electronics and Communication Engineering Chennai India Kcg College of Technology Dept. of Computer Science and Engineering Chennai India Kcg College of Technology Dept. of Ece Chennai India

ISBN: (纸本)9798350302288

Food is one of the essential needs for human survival. In the present era, there is a rapid increase in the population and emerging growth of technology in the developing countries around the globe. Due to the population increase, there is a demand for an abundant food supply. Most of the food products have a shorter shelf life. It is necessary to maintain food products at a certain temperature so that they can be preserved for a longer duration. There arises a necessity to develop a device that can monitor and maintain the food products. This research seeks to design a cold storage device that can monitor essential food storage specifications such as temperature, humidity, pressure, light and gas compositions like ammonia, methane, carbon dioxide and so on. The cold storage also alerts the user when a parameter passes through the threshold value. The cold storage device can be instated in a moving truck carrying food products as well as in a storage warehouse. It also has a Global Positioning System (GPS) module for the purpose of tracking the real time location of the transporting truck and it also provides a theft alert in the warehouse. Further the user can control the exceeded threshold value from any remote location. © 2023 IEEE.

关键词： Supply chains

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EMP-GAN: Encoder-Decoder Generative Adversarial Network for Mobility Prediction

EMP-GAN: Encoder-Decoder Generative Adversarial Network for ...

引用

2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bang, Sammy Yap Xiang Raza, Syed M. Yang, Huigyu Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665494274

Ultra-dense cell deployments in Beyond 5G and 6G result in extensive overlapping between cells. This makes current reactive handover mechanism inadequate due to availability of multiple strong signals at a position. Moreover, recently proposed predictive mobility management schemes are also not suitable as they may lead to unnecessary handovers. A predictive path-based mobility management scheme can solve these issues, but forecasting User Equipment (UE) paths with high accuracy is a challenging task. This paper proposes Encoder-Decoder Generative Adversarial Network (EMP-GAN) for forecasting multi-step ahead UE path. EMP-GAN architecture consists of generator and discriminator neural networks, where the generator predicts mobility (next multi-step target sequence) and the discriminator classifies between the predicted target sequence and the ground truth in adversarial learning. Besides adversarial learning, feature matching and fact forcing training methods are employed for fast convergence of GAN and performance improvement. EMP-GAN is evaluated on mobility dataset collected from the wireless network of Pangyo ICT Research Center, Korea, and results show that it outperforms state-of-the-art prediction models. In particular, EMP-GAN achieves 95.55%, 94.70%, 93.50%, and 92.39% accuracies for 3, 5, 7, and 9-step predictions, respectively. © 2023 IEEE.

关键词： Generative adversarial networks

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Feature Pool Exploitation for Disease Detection in Fundus Images 17

Feature Pool Exploitation for Disease Detection in Fundus Im...

引用

17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Xiang Bang, Sammy Yap Le, Kim-Ngoc Thi Le, Duc-Tai Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Retinal fundus diseases without immediate diagnoses and treatment may lead to serious consequences such as permanent visual impairment. Recently, many machine learning (ML) and deep learning (DL) models have been introduced for fundus image classification. However, those heavy models require high-end graphics processing units for training and testing, thus not suitable for real-case usage in fundus cameras with limited computation power. In this paper, we demonstrate the effectiveness of our proposed two-block model, Feature Exploitation Lightweight network (FEL-net) which consists of a Feature Exploitation Block (FEB) and Lightweight Classification Block (LCB) by comparing it with other DL models. The experiment was carried out on a dataset of 21, 697 fundus images and our model achieves 99% binary classification accuracy. The proposed robust FEB is used to generate a refined feature pool for fundus images to build an efficient ML classifier that can distinguish fundus images with disease from the normal case with high accuracy and low running time. © 2023 IEEE.

关键词： Feature extraction

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Development of a OTA with DFVF for a 50nm VLSI Technology Application 21

Development of a OTA with DFVF for a 50nm VLSI Technology Ap...

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21st IEEE India Council International Conference, INDICON 2024

作者： Manjunath, T.C. Kumar, Hareesh Rajani Rai, B. Narayanan, S. Krishna Padmavathi, M. Pavithra, G. Ninawe, Swapnil S. Rajarajeswari College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Rv Institute of Technology & Management Dept. of Electronics & Communication Engineering Karnataka Bangalore India Vivekananda College of Engineering and Technology Dept. of Electronics & Communication Engineering South Kanara Karnataka Puttur India Mvj College of Engineering Dept. of Computer Science & Engineering Karnataka Bangalore India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350391282

This research paper presents the design and simulation of Multiple Path Fully Operational Transconductance Amplifiers (OTAs) with Dual Flipped Voltage Follower, targeted for VLSI-based signal processing applications. Leveraging 50nm CMOS technology and Cadence tools, the proposed OTA architecture offers enhanced performance and versatility in signal processing tasks. The design incorporates multiple paths for signal processing, enabling improved bandwidth, gain, and linearity while minimizing common-mode noise. The inclusion of Dual Flipped Voltage Follower enhances the OTA's stability and dynamic range, ensuring reliable operation in high-speed VLSI systems. Through extensive simulation studies, the paper demonstrates the effectiveness of the proposed OTA design in meeting the stringent requirements of modern signal processing applications. This research contributes to the advancement of VLSI design methodologies, offering a promising solution for high-performance signal processing tasks in nanoscale CMOS technologies. The simulation results from the cadence tool shows the effectiveness of the methodology that is being presented in this research article. © 2024 IEEE.

关键词： Array Cadence Flip OperationalAmplifier Signal Simulation Transconductance VLSI Voltage

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Action Localization and Recognition through Unsupervised I3D and TSN 3

Action Localization and Recognition through Unsupervised I3D...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Umran, Munzir Muchtar, Kahlil Abidin, Taufik Fuadi Arnia, Fitri Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia

ISBN: (纸本)9798350321487

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data. © 2023 IEEE.

关键词： Forecasting

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir University of Oslo Dept. of Informatics Oslo Norway University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom Illinois Institute of Technology Dept. of Electrical and Computer Engineering Chicago United States Northwest Univesity School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350351255

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024 IEEE.

关键词： Quality of service

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Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery Systems 19

Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Yeongmin Syahran, Raihan Muhammad Spangenberger, Mael Yang, Huigyu Oh, Jiyong Choo, Hyunseung Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Informatics Télécom SudParis Évry France

ISBN: (纸本)9798331507817

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services. © 2025 IEEE.

关键词： Deep reinforcement learning

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Fine-Tuning Prophet model for application in Agro-Industry to forecast Pesticide consumption 1

Fine-Tuning Prophet model for application in Agro-Industry t...

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1st IEEE International Conference on Green Industrial electronics and Sustainable Technologies, GIEST 2024

作者： Dey, Snehasis De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Samant, Tapaswini Banerjee, Shobhan College of Engineering Electronics and Telecommunication Engineering Bhubaneswar India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Indian Institute of Information Technology Dept. of Computer Science & Engr. Ranchi India

ISBN: (纸本)9798331540142

Forecasting plays a key role in several industries to predict the probable requirement of a certain product or service over a period of time. This may or may not be dependent on certain external factors depending upon which, a suitable method needs to be chosen to perform the forecasting task. For a given data distribution, the best-fit model needs to be figured out after fine-tuning its hyperparameters to acquire the most optimal forecasts. In this paper, we have used the Prophet model of Facebook, an additive regression model to forecast the requirement of pesticides to be used for agricultural needs, and presented an overview of how fine-tuning the parameters leads to optimal curve fits, hence resulting in better-quality forecasts. We have also studied the impact of the size of the train data on the overall quality of predictions. Hence after combining both the data size and parameter values, the curve fits and predictions have been visually illustrated. © 2024 IEEE.

关键词： Pesticides

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Effectiveness of Recurrent Neural Network with Biomimic Components 3

Effectiveness of Recurrent Neural Network with Biomimic Comp...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Singh, Paras Nath Sakhare, Anupama Sarada, T. Cmr Institute of Technology Dept. of Computer Science and Engineering Bangalore560037 India Rtm Nagpur University Dept. of Electronics and Computer Science Nagpur440033 India

ISBN: (纸本)9798350343274

In order to improve the energy efficiency and computational capacity of our data processing systems, bioinspired, or neuromorphic, computing draws inspiration from how the brain computes. Emulation of natural systems, patterns, and components is known as biomimetics or biomimicry, and it is done to address challenging challenges in society. The term 'spintronics' refers to the application of a fundamental property of particles (the spin of an electron) known as the spin to information processing. Hopfield was the first to establish a direct connection between physical models and neural networks. To learn any of these models, the neural network must be able to adjust the coupling between each pair of spins. To implement multi-layer perceptron (neural network) structures, current technologies, however, have necessitated the extensive usage of complementary metal-oxide-semiconductor (CMOS) devices, which raises the perceptron's complexity of manufacture, space need, and power consumption. In this paper, it is suggested that the first trainable CMOS-free recurrent neural network with biomimetic components can be built using biomimetic neurons made of domain wall-magnetic tunnel junctions. This analysis shows how well this system performs computationally on benchmark tasks and how much more efficiently it performs computationally than alternative recurrent neural network systems. © 2023 IEEE.

关键词： Biomimetics

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Flood Evacuation Route Recommendation Using Particle Swarm Optimization Algorithm

Flood Evacuation Route Recommendation Using Particle Swarm O...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Mahendra, Ricky Haedir, Teuku Muh. Nur, M. Adnan Nur, Nur Khaerat Hazriani Handayani University of Makassar Dept. of Informatics Makassar Indonesia Fajar University Dept. of Civil Engineering Makassar Indonesia Handayani University of Makassar Dept. of Computer System Makassar Indonesia

ISBN: (纸本)9798350359633

Equipping the society with an evacuation route assistant is important as a preventive action in order to minimize anxiety as well as number of victims when disaster strike. Therefore, research on best route determination to the shelter is a noble task. Evacuation routes mean routes to pass in order to reach the gathering point or shelter safely. This study aims to implement the particle swarm optimization (PSO) algorithm in finding the optimal path to the designated shelter. PSO is a metaheuristic algorithm inspired by the behaviour of flocks of birds or fish to optimize the search for the best route. Taking into account the details of the PSO implementation, including initialization of the random particle position and velocity, selection of the best particle (Pbest) and Global Best (Gbest), and updating the speed and position of the particle using the specified formula. The objective function is recalculated based on the total distance of the particle route as well as the condition weight of the affected areas. The results showed that out of ten experiments with 100 iterations, six valid routes were found with the highest fitness value of 1,935, which indicates that PSO can provide optimal solutions in the context of finding flood evacuation routes. © 2023 IEEE.

关键词： Floods

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