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Registered-backoff-time (RBT) based channel access with grouping control for the trigger RAW mode of IEEE 802.11ah IoT Network

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computer Networks 2024年 242卷

作者： Huang, Chung-Ming Hsieh, Chia-Han Dept. of Computer Science Information Engineering National Cheng Kung University Tainan Taiwan

This work proposes the Trigger RAW -centric Registered Backoff Time (RBT) -based channel access with the Grouping Control (TRC-RBT-GC) method to tackle the load balance problem in the IEEE 802.11ah network for Internet of Things (IoTs). The main feature of the proposed method is twofold: (1) Each STA generates a RBT value registers it to AP such that AP can schedule STAs to access channel according to STAs’ RBT values. (2) Each RAW consists of a Claiming RAW a data RAW. Those STAs that only have the uplinked data can deliver their intentions of uplinking data to AP in their respective claiming slot of the Claiming RAW. AP then schedules the STAs that have successfully delivered their intentions of uplinking data based on their RBT values in their respective data slot of the data Raw. When a data slot of the data RAW has some remaining time after all of the AP-scheduled STAs have finished their channel access, if there are some STAs failed delivering their intentions of uplinking data to AP, then these STAs can try to access channel in the remaining time of their respective data slot using the regular CSMA/CA scheme based on their RBT values. The simulation results show that the proposed TRC-RBT-GC method can achieve load balance among RAW's slots the system performance can be improved a lot. © 2024 Elsevier B.V.

关键词： Internet of things

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SANFlow: semantic-aware normalizing flow for anomaly detection and localization 23

SANFlow: semantic-aware normalizing flow for anomaly detecti...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Daehyun Kim Sungyong Baik Tae Hyun Kim Dept. of Artificial Intelligence Hanyang University Dept. of Data Science Hanyang University Dept. of Computer Science Hanyang University

Visual anomaly detection, the task of detecting abnormal characteristics in images, is challenging due to the rarity and unpredictability of anomalies. In order to reliably model the distribution of normality and detect anomalies, a few works have attempted to exploit the density estimation ability of normalizing flow (NF). However, previous NF-based methods forcibly transform the distribution of all features into a single distribution (e.g., unit normal distribution), even when the features can have locally distinct semantic information and thus follow different distributions. We claim that forcibly learning to transform such diverse distributions to a single distribution with a single network will cause the learning difficulty, thereby limiting the capacity of a network to discriminate between normal and abnormal data. As such, we propose to transform the distribution of features at each location of a given input image to different distributions. Specifically, we train NF to map the feature distributions of normal data to different distributions at each location in the given image. Furthermore, to enhance the discriminability, we also train NF to map the distribution of abnormal data to a distribution significantly different from that of normal data. The experimental results highlight the efficacy of the proposed framework in improving the density modeling and thus anomaly detection performance.

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FPGA-based Optical Kerr Effect Emulator

FPGA-based Optical Kerr Effect Emulator

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Signal Processing in Photonic Communications, SPPCom 2022

作者： Liu, Keren Börjeson, Erik Häger, Christian Larsson-Edefor, Per Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Dept. of Electrical Engineering Chalmers University of Technology Gothenburg Sweden

We propose a digital emulator of the optical Kerr effect, suitable for FPGA implementation. In addition, we study a combined PMD and Kerr emulator implementation with respect to DSP hardware aspects such as fixed-poin... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical Kerr effect

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Frequency Response Deterioration due to Surface Ridge Abrasion of Tactile Sensors

Frequency Response Deterioration due to Surface Ridge Abrasi...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Muhammad Irwan Yanwari Shogo Okamoto Dept. Computer Engineering Technology Polines Dept. Computer Science Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Epidermal ridges are crucial for tactile sensing in both human fingers and synthetic sensors. These ridges are made of compliant materials and susceptible to abrasion damage after iterative use. This study focuses on examining the impact of sensor ridge damage on the acquired signal, which encompasses a broad range of frequency components. We compared the frequency responses of a tactile sensor scanning sandpaper with both intact and damaged sensors. The degradation of signal levels was evident across the 0–1,000 Hz range, with particularly prominent suppression of the peak frequency component. This study demonstrates the frequency-dependent influence of ridge abrasion.

关键词： Degradation Fingers Tactile sensors Epidermis Frequency response Sensors Iterative methods Consumer electronics

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Predicting Stock Market Odds Across Time Variations using Deep Learning Algorithms

Predicting Stock Market Odds Across Time Variations using De...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Kishore, O.A.R. Krishna Prasad, P.E.S.N.K. Kumar, K. Parish Venkata Krishna, N. Vamsi Dokku, Srinivasa Rao SRR & CVR Government College Dept. of Commerce Andhra Pradesh Vijayawada India NIST University School of Computer Science and Engineering Odissa Brahmapur India V.R.Siddhartha Engineering College Dept. of Computer Applications Andhra Pradesh Vijayawada India V.R.Siddhartha Engineering College Dept. of Electrical and Electronics Engineering Andhra Pradesh Vijayawada India PVP Siddhartha Institute of Technology Dept of Business Administration Vijayawada India

ISBN: (纸本)9798350381887

This study conducts a comparative analysis of LS-SVM, LSTM, and ANN algorithms in predicting the NIFTY 50 index across various time frames, addressing the challenge of market unpredictability. Through a review of literature, the study explores the strengths and weaknesses of these algorithms in financial forecasting. Methodologically, it employs evaluation metrics such as Mean Squared Error (MSE) and R-squared (R2) to assess predictive accuracy. The findings reveal that LS-SVM demonstrates robust performance in capturing medium to long-term trends, while ANN excels in short-term predictions. LSTM exhibits competitive performance in short to medium-term forecasts. LS-SVM and ANN emerge as top performers across various time frames, showcasing strong predictive capabilities and explanatory power. This comparative analysis underscores the importance of leveraging the strengths of each model and integrating machine learning techniques with fundamental evaluation for informed decision-making in stock market forecasting. © 2024 IEEE.

关键词： Contrastive Learning

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Social Interaction & Service Robot for Hospital Wards

Social Interaction & Service Robot for Hospital Wards

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2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Bharath, G. Johnson, Nobin Monika, P. Prajwal, S. Aishwarya, S. Dayananda Sagar University Dept. of Mechanical Engineering Bengaluru India Dayananda Sagar University Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9781665483162

As the era of industrial revolution 5.0 has begun, most of the robots are developed to have cyber inter-physical functionalities which are deemed to replace human activities. However, robots are rarely being utilized in the health care sector. In a medical institution, countless activities and events are happening simultaneously. Most of these are very precise, lifesaving and are on a time constraint. Heavy machinery and equipment are required to execute such events which is time-consuming and inconvenient. The robot specified helps with regular processes occurring on a day-to-day basis in the institution such as taking vitals and sanitization as well as transporting products on the go intelligently and safely. This robot is good at mapping rooms using the internal GPS, the robot can effectively communicate and output simple messages with the patients via, a display screen. Human intervention plays a vital role in preventing the health care workers from coming in contact with the covid-19. © 2022 IEEE.

关键词： Internet of things

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An Evaluation of MFCC and ZCR Feature Extraction with CNN Model of Speaker Identification System 13

An Evaluation of MFCC and ZCR Feature Extraction with CNN Mo...

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13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Kaladharan, N. Arunkumar, R. Dept. of Computer Engineering Government Polytechnic College Theni India Department of Computer Science and Engineering FEAT Annamalai University India

ISBN: (纸本)9781713857938

The speech is the important medium to transfer our feeling to the neighbors. Speaker recognition is the process to recognize our speech in our owned database. Speaker recognition has divided in to speaker identification and speaker verification. Speaker identification is different mode which is text dependent, independent and prompted. Identifying the speaker in our database is the major task in our process. Identifying data or speech is the main aim to handle in ourdatabase. Speaker identification skills arebroadly useful in security and investigation, voice confirmation, electronic voice snooping and classify verification. Various features extraction ideas are used in identification as well as verification such as mfcc, zcr, pncc and so on. MFCC are cepstral coefficients resultant on a warped frequency scale centered on human hearing observation. In the computation of MFCC, the first thing is windowing the speech signal to split the speech signal into frames. The zero-crossing rate is the proportion at which the signal modifications from positive to zero to negative or from negative to zero to positive. Power Normalized Cepstral Coefficients is well identified for the high accuracy of automatic speech recognition systems even in high-noise *** paper presents the comparison of mfcc and zcr with the CNN modeling system. CNN is the top modeling idea in the recent decades. © Grenze Scientific Society, 2022.

关键词： Extraction

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Enhancing Automatic Evaluation Systems for Descriptive Answers Using BERT: Addressing Context and Coherence Challenges

Enhancing Automatic Evaluation Systems for Descriptive Answe...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Sagarika Dash Dhawaleswara Rao CH Rudra Kalyan Nayak M.V.B.T. Santhi Dept. of Computer Science Engineering Centurion Univrsity of Technology and Management Odisha India Dept. of Computer Science Engineering VIT Bhopal Univrsity Sehore Madhya Pradesh India Dept. of Computer Science Engineering Koneru Lakshmalah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

This study explores the application of advanced Natural Language Processing (NLP) techniques, specifically leveraging BERT-based models, for evaluating semantic similarity between text inputs. The methodology involves preprocessing text through tokenization, stemming, and sentence chunking, followed by the generation of sentence embeddings using the ‘bert-base-nli-mean-tokens’ model. The model, fine-tuned on Natural Language Inference (NLI) tasks, captures deep contextual relationships, enhancing the ability to compute text similarity through cosine similarity of high-dimensional embeddings. The evaluation used datasets from Semantic Textual Similarity (STS) benchmarks, combined with educational text samples. Key metrics such as Pearson correlation, Spearman correlation, and Root Mean Squared Error (RMSE) were employed to assess the model's performance in predicting similarity scores between text pairs. Results demonstrate that the model achieves a Pearson correlation of 0.53, indicating moderate agreement with human-annotated scores, while the Spearman correlation score of 0.26 highlights a weaker monotonic relationship. An RMSE of 0.12 further suggests low prediction error, supporting the model's efficacy in identifying similarities between text pairs. These findings underscore the potential of BERT-based models in applications like automated essay grading, paraphrase detection, and content-based recommendation systems, where accurate assessment of text similarity is critical

关键词： Measurement Correlation Accuracy Computational modeling Semantics Predictive models Signal processing Transformers Tokenization Context modeling

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Smart Waste Bin: Mechanical and AI Based Waste Segregation 2

Smart Waste Bin: Mechanical and AI Based Waste Segregation

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Zubair, Mohammad Mathur, Yash Rathore, Himanshu Gupta, Param Banerjee, Snehashish Jk Lakshmipat University Dept. of Mechanical Engineering Jaipur India Jk Lakshmipat University Dept. of Computer Science Engineering Jaipur India

ISBN: (纸本)9781665400527

Increase in population in cities has resulted to inadequate waste management. It has resulted in the spreading of diseases, and increasing in cost of waste processing. In general, the waste management fails at the very initial stage i.e. at the source. There are no significant data that influence the pro per waste segregation at the source itself. This paper proposes an effective and smart solution for waste segregation at source by using image processing, artificial intelligence (AI), and mechanical sorting. A prototype is developed, consisting of mechanism to initial segregation, then image processing and AI is use to further segregate the waste. The prototype demonstrates the separation proposed in the lab environment. Which can be further implemented for commercial purpose of waste management. © 2022 IEEE.

关键词： Waste management

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Unveiling Insights: Vision-Based data Mining Analysis of Webpages in Transition from HTML 3 to HTML 5

Unveiling Insights: Vision-Based Data Mining Analysis of Web...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Meena Chaudhary Pooja Gunjan Chandwani Dept. of Computer Science and Technology Manav Rachna University Faridabad India Dept. of Computer Science and Engineering Galgotias University Greater Noida India

Deep web material is accessed with web database requests, and returned information is packaged into dynamically generated web pages known as deep Web pages. Due to their intricate structure, it is tedious task to retrieve organized data from deep web pages. A great many approaches to address this problem are yet to be proposed, but all of them have inherent drawbacks because they are based on the website and the programming language. The contents of Sites are always shown for users to search daily, like the common two-dimensional media. This motivates us to search for an alternative way to extract deep Web information, by using some interesting popular visual features from deep Webpages, to address the limitations of past works. A novel blog vision approach is proposed in this paper to overcome these issues. This technique uses visual features in deep web pages specifically to conduct deep web data extraction. This paper also recommends a new appraisal measure to capture the required human activity to make maximum mining. The proposed methodology is highly efficient in the deep network data extraction, according to our studies with a large set of web-based data bases.

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