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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Badshah, Mohsin Ali Lee, Seong-Min Jin, Chengjun Byeon, Seongyoung Hussain, Tasadduq Ali, Muzahir Kim, Seok-Min University of California Dept. of Chemical and Biomolecular Engineering Irvine United States Chung-Ang University Dept. Computer Science and Engineering Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Glancing angle deposition (GLAD) is a physical vapor deposition process in which the substrate is placed to have a large incidence angle (>75°, angle between incoming flux and substrate normal). The GLAD process can be considered a promising method to fabricate metallic nanostructure on a large area at a low cost. GLAD Ag nanorods were fabricated and their fluorescence enhancement performances were examined. A practical antibody chip was realized using a 500 nm thick vertical Ag nanorods substrate. To overcome the limitations of GLAD Ag nanorod MEF substrate, Ag nanorods on micro post array substrate were also investigated. © 2022 IEEE.

关键词： Biochips

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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Automatic Fusion Network for Cold-start CVR Prediction with Explicit Multi-Level Representation 39

Automatic Fusion Network for Cold-start CVR Prediction with ...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Jin, Jipeng Lu, Guangben Li, Sijia Gao, Xiaofeng Tan, Ao Wang, Lifeng Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Dept. of Computer Science and Engineering China Corporate Development Group Tencent China

ISBN: (纸本)9798350322279

Estimating conversion rate (CVR) accurately has been one of the most central problems in online advertising. Existing methods in production focus on learning effective interactions among features to boost the model performance. Despite great success, these methods treat all the features equally without distinction. However, different features suffer differently from cold-start issues. Tail elements in those high-cardinality features, which we denote as fine-grained features, tend to have inadequate samples and thus fail to obtain semantically meaningful embeddings. Interacting with those features leads astray and impairs the accuracy of new ads in a cold-start scenario. In this paper, we propose Automatic Fusion Network (AutoFuse) to better tackle the challenge. AutoFuse explicitly separates features into groups based on their granularity and learns multiple levels of representation conditioned on different combinations of feature groups. Concretely, AutoFuse learns an ad-level representation to depict the unique individual character and a group-level representation to portray the collective information by discarding the fine-grained features. The final robust and general ad representation is obtained by integrating these two level representations adaptively. Such a combination encompasses a wider amount of information, and thereby mitigates the cold-start issue. Extensive experiments on two industrial-scale datasets and three public datasets show that AutoFuse significantly and consistently outperforms a spectrum of competitive methods including our currently deployed model. Meanwhile, the remarkable improvement on new ads validates the effectiveness of our method in cold-start scenarios. We design AutoFuse as a generic approach and thus it can be seamlessly transferred into other domains. Our method has been deployed online to serve billions of users and ads and has achieved significant GMV gain of 2.84%. © 2023 IEEE.

关键词： Marketing

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Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 24-31页

作者： Zivelonghi, Alessandro Giuseppi, Alessandro Dep. of Applied Mathematical Science IIS "Calabrese-Levi" VR San Pietro in Cariano Italy PowerConsulting Studio San Pietro in Cariano Verona Italy Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Via Ariosto 25 Rome00185 Italy

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO2 levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO2. The multi-room monitoring concept of indoor-CO2 levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes. © 2023 The Authors

关键词： Internet of things

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Developing a Self-Explanatory Transformer

Developing a Self-Explanatory Transformer

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Rasha Karakchi Ryan Karbowniczak Dept. of Computer Science and Engineering University of South Carolina Columbia USA

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

While IoT devices provide significant benefits, their rapid growth results in larger data volumes, increased complexity, and higher security risks. To manage these issues, techniques like encryption, compression, and mapping are used to process data efficiently and securely. General-purpose and AI platforms handle these tasks well, but mapping in natural language processing is often slowed by training times. This work explores a self-explanatory, training-free mapping transformer based on non-deterministic finite automata, designed for Field-Programmable Gate Arrays (FPGAs). Besides highlighting the advantages of this proposed approach in providing real-time, cost-effective processing and dataset-loading, we also address the challenges and considerations for enhancing the design in future iterations.

关键词： Training Automata Transformers Real-time systems Natural language processing Encryption Security Internet of Things Field programmable gate arrays Edge computing

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An Efficient Deep Learning Approach for Jute Pest Classification Using Transfer Learning

An Efficient Deep Learning Approach for Jute Pest Classifica...

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International Conference on electrical engineering and Information & Communication Technology (ICEEICT)

作者： Muhammad Tanvirul Islam Md. Sadekur Rahman Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Jute is considered as one of the most vital crops in the world. For some countries jute is the principal source of earnings and GDP. One of the primary elements influencing jute yield is jute pests. Accurate pest identification makes it possible to take prompt preventative action to minimize financial losses. Considering the fact, to classify jute pests, the study suggests different jute pest classification models, which are based on transfer learning. The best model offers high performance and resilience. A VCI-validated dataset comprising 7235 images has been utilized in the analysis. The dataset encompasses images classified into 17 distinct jute pest classes. The dataset is already divided into three categories train, test and validation. To increase the dataset size, data augmentation is applied to the training set. To improve performance, all the models were integrated with the transfer learning model. VGG 16, ResNetl0l, DenseNet201, InceptionV3, Xception, and MobileN etV2 were used to train the parameters on the publicly available ImageN et dataset followed by some customized dense layers. The models were assessed using different types of metrics, including confusion matrix, F1 score, precision, and recall. Compared to other models DenseNet201 outclassed other models, acquiring 97% accuracy. The fundamental information and technical support for jute pest classification are provided by this study.

关键词： Training Measurement Deep learning Economic indicators Transfer learning Crops Data augmentation

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Higher-dimensional HOM effect and quantum state routing with directionally-unbiased linear-optical devices

Higher-dimensional HOM effect and quantum state routing with...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Schwarze, Christopher R. Simon, David S. Osawa, Shuto Sergienko, Alexander V. Dept. of Electrical and Computer Engineering Photonics Center BostonMA02215 United States Dept. of Physics and Astronomy Stonehill College 320 Washington Street EastonMA02357 United States Dept. of Physics Boston University 590 Commonwealth Avenue BostonMA02215 United States

A four-port beam-splitter only maps between two input and output modes. We introduce a more general four-port scatterer that supports a broader class of state transformations, enabling a higher-dimensional HOM effect ... 详细信息

ISBN: (纸本)9781957171395

关键词： Electrooptical devices

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A comparative study of different scheduling approaches for splittable latency sensitive tasks in Fog-Cloud environment

A comparative study of different scheduling approaches for s...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Kone Sai Sandeep Challa Aditya Koundinya Adapa Venkata Prabhas Chinmaya Kumar Swain Sambit Kumar Mishra Dept. of Computer Science Engineering SRM University

IoT has revolutionized the way we live and the work we do by connecting different devices through the Internet. In the present scenario, the number of IoT devices are increasing rapidly due to the increase in technology and the increase in the comforts of life. Nowadays we can see that many of them are using IoT devices regularly, it's estimated that by the end of 2030, there will be 30 billion users who will be using IoT applications. These devices send data to the cloud for processing. Due to the distance of the cloud from the IoT devices, the application requests get delayed service responses. So to handle the latency sensitive applications we require the micro cloud service like fog servers deployed near to the data generation points. The fog layer lies between the IoT devices and Cloud which acts as an intermediate layer. This helps in reducing latency of the tasks and provide better performance. As the number of IoT applications keeps on increasing, the resources available with the fog nodes may not handle the upcoming demands. So to overcome these demands, we are using splittable methods to allocate the tasks to Fog/ Cloud nodes more compactly. If a task can be splitted before the deadline into different modules, then we split the given task and allocate those tasks to different fog nodes/ servers and then collecting back the data from the fog nodes/ servers and merging them into a single unit. With the help of this method, we can increase the performance of the system.

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Photon-Pair Generation in a 45 nm CMOS Microring Cavity: Impact of Spontaneous Raman Scattering

Photon-Pair Generation in a 45 nm CMOS Microring Cavity: Imp...

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Quantum 2.0, QUANTUM 2022

作者： Ramesh, Anirudh Wang, Imbert Kramnik, Danielius Fargas Cabanillas, Josep M. Stojanović, Vladimir Popović, Miloš A. Kumar, Prem Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States

ISBN: (纸本)9781957171111

We characterize the impact of spontaneous Raman scattering (SRS) on photon-pair generation via spontaneous four-wave mixing (SFWM) in a CMOS microring cavity by analyzing the single counts in each channel to separate out the contribution of SRS from SFWM. We find the contribution of SRS to the photon counts to be low compared to that from SFWM. © 2022 The Author (s)

关键词： Photons

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Accessing Visual Information for Divyangjan

Accessing Visual Information for Divyangjan

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： M S Divya Rani Hrishikesh Vivekanand Ghodke M Manikandan R Manaswini Shivani K Sri Sai Venkata Gayathri Dhara Shreya Dept. of Electronics and Communication Engineering Presidency University Bengaluru Dept. of Computer Science Engineering Presidency University Bengaluru

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Approximately 1.3 billion people worldwide suffer from a visual impairment. Typically, they must use Braille to read printed materials. However, when the content is not printed in Braille, these individuals have difficulties. Even though there are a lot of electronic reading aids available, the costs remain prohibitive. Therefore, our work suggests a low-cost document reader that uses a webcam to take pictures of printed materials. The Optical Character Recognition (OCR) framework's image-to-text conversion is then used to turn the acquired image into text. Finally, the Text to Speech (TTS) framework's text-to-speech conversion will be used to read the text aloud in speech format. Consequently, a person with a visual impairment can use hearing rather than touch to comprehend printed materials that are not written in Braille. Users may view documents printed in five different languages. The choice of different document sizes is this work's greatest novelty. The system is equipped with a Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, a web camera and speaker. The Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, webcam, and speaker are all part of the system.

关键词： Braille Visualization Costs Webcams Optical character recognition Visual impairment Government Motors Text to speech NEMA

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