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A Lifetime Extension Framework for Wireless Sensor Networks

A Lifetime Extension Framework for Wireless Sensor Networks

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international conference on Telecommunications and Signal processing (TSP)

作者： Georgios Tsoumanis Nikolaos Giannakeas Alexandros T. Tzallas Evripidis Glavas Eleftherios Stergiou Konstantinos Oikonomou Constantinos T. Angelis Department of Informatics and Telecommunications University of Ioannina Arta Greece Department of Informatics Ionian University Corfu Greece

ISBN: (数字)9781728163765

ISBN: (纸本)9781728163772

Prolonging the lifetime of a wireless sensor network (WSN) is closely related to the energy hole problem where nodes close to the sink node consume more energy than the other nodes, due to their increased traffic load. For tackling the effects of the energy hole problem, a framework is proposed here consisting of two parts. In its first part, the sink node is selected in order to minimize the energy consumption of the most severely affected by the energy hole problem node. During the second part, a new algorithm proposed here is implemented and changes the path followed by traffic forwarded by nodes close to the sink node in order to achieve less energy consumption for the most energy consuming node of the network.

关键词： Energy consumption Wireless sensor networks data models Batteries Routing Load modeling Informatics

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Iris: Amortized, Resource Efficient Visualizations of Voluminous Spatiotemporal datasets

Iris: Amortized, Resource Efficient Visualizations of Volumi...

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IEEE/ACM international Symposium on Big data Computing (BDC)

作者： Kevin Bruhwiler Thilina Buddhika Shrideep Pallickara Sangmi Lee Pallickara Colorado State University Fort Collins Colorado 80521

ISBN: (数字)9780738123967

ISBN: (纸本)9780738130385

The growth in observational data volumes over the past decade has occurred alongside a need to make sense of the phenomena that underpin them. Visualization is a key component of the data wrangling process that precedes the analyses that informs these insights. The crux of this study is interactive visualizations of spatiotemporal phenomena from voluminous datasets. Spatiotemporal visualizations of voluminous datasets introduce challenges relating to interactivity, overlaying multiple datasets and dynamic feature selection, resource capacity constraints, and scaling. In this study we describe our methodology to address these challenges. We rely on a novel mix of algorithms and systems innovations working in concert to ensure effective apportioning and amortization of workloads and enable interactivity during visualizations. In particular our research prototype, Iris, leverages sketching algorithms, effective query predicate generation and evaluation, avoids performance hotspots, harnesses coprocessors for hardware acceleration, and convolutional neural network based encoders to render visualizations while preserving responsiveness and interactivity. We also report on several empirical benchmarks that demonstrate the suitability of our methodology to preserve interactivity while utilizing resources effectively to scale.

关键词： data visualization Spatiotemporal phenomena Iris Visualization Rendering (computer graphics) Servers Query processing

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Comparing the Effort Estimated By Different models

Comparing the Effort Estimated By Different Models

引用

international conference on Advanced Computing and Communication Systems (ICACCS)

作者： Mayank Jha Richa Jha Citicorp Services India Limited Pune India FIS Solutions (India) Pvt. Ltd. Pune India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

Management of project software starts with a collection of activities referred to as project planning procedure. A company's team must decide the work to be done, the resources to be reorganized and a time from beginning of the calculation until project starts. Following completion of these activities, the program team will set up a set of projects that will assign program development tasks, identify key milestones, identify responsibilities for each task and identify related dependencies among participants that may have a significant impact on progress. There is usually no full precise estimation process, but in this research we have tried to find the best programming methods to find the best estimate of programming. Effort estimation is one of greatest objection of STLC. It is platform for planning, estimating and preparing effort for project. This paper demonstrates model with a purpose of depicting bias variation and an accuracy of the technology of an enterprise test attempt estimates concluding the function of Cobb-Douglas (CDF), Neuro fuzzy approach, and Genetic methods. The purpose of this review is to present an analysis of principles to minimize software costs and to explain how these concepts are applied to general system divisions. We deliver simple algorithms namely-Cobb Douglas, Genetic algorithms, and Adaptive Neuro Fuzzy Approach to decide which algorithm is best suited to finding the best estimates as accurate as possible. The best outcomes they have been found in Neuro Fuzzy Approach. The Neuro Fuzzy has highest accuracy to be found, but the Genetic Algorithm was better than Fuzzy Logic, the worst compared to Cobb Douglas and Genetic algorithms.

关键词： Software Estimation Mathematical model data models Software measurement Communication systems Computational modeling

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Federated Learning for Arrhythmia Detection of Non-IID ECG

Federated Learning for Arrhythmia Detection of Non-IID ECG

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international conference on Computer and Communications (ICCC)

作者： Mufeng Zhang Yining Wang Tao Luo Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In this paper, a distributed arrhythmia detection algorithm based on electrocardiogram (ECG) is proposed for auxiliary diagnosis and treatment. ECG that contains tremendous cardiac rhythm information plays an important role in clinical treatment. Machine learning (ML) algorithms can effectively build the relationship between ECG and the underlying arrhythmia in it. Due to the privacy sensitivity of the ECG, we introduced a federated learning (FL)-based distributed algorithm that enables each medical institution to cooperatively train a arrhythmia detection algorithm locally. Compared with the traditional centralized ML algorithms, the use of FL-based algorithm does not need to collect all the local ECG of each medical institution to an external platform to perform centralized learning, and hence preventing the privacy from leakage. However, ECG collected from different medical institution is non-independent and identically distributed (non-IID) in reality, which will lead to non convergence of the FL-based algorithm. To address this challenge, we optimize the FL-based algorithm using a sharing strategy for partial ECG data of each medical institution combined with elastic weight consolidation (EWC) algorithm. Here, the sharing strategy, which makes each medical institution share ECG data to the central server while not share to other clients, could help build an initial FL model and EWC algorithm make the accuracy of the model trained by each medical institution not decline, therefore the proposed FL algorithm can achieve a trade-off between the privacy and model performance. The experiment results show that, compared with baseline FedAvg algorithm and FedCurv algorithm, the optimized FL-based algorithm is faster in convergence for IID ECG and achieves signicant improvement in terms of both recall and precision for non-IID ECG.

关键词： Privacy data privacy Electrocardiography Prediction algorithms data models Detection algorithms Medical diagnostic imaging

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A Recursive Estimator of Spectral Noise Floor in the Presence of Signals

A Recursive Estimator of Spectral Noise Floor in the Presenc...

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international conference on Signal processing and Communication Systems, ICSPCS

作者： Songsri Sirianunpiboon Simon B. Faulkner Stephen D. Elton Cyber and Electronic Warfare Division Defence Science and Technology Group Australia

ISBN: (数字)9781728199726

ISBN: (纸本)9781728199733

In this paper we propose a recursive noise floor estimator, which operates on spectral data and in the presence of intermittent signals. We use a Bayesian approach where we treat data samples consisting of signals as outliers in a Gaussian mixture distribution. The signal is modelled with a Gaussian distribution having larger power with respect to that of pure noise samples. In addition to noise floor estimation, this technique also leads to a novel detection statistic for the detection of signals in noise. We demonstrate our approach in two scenarios. Firstly, we consider a noise only case and calculate the root mean square error for the estimated noise variance over the number of spectral samples used. In the second scenario, we simulate representative radio frequency (RF) signals in the presence of noise and evaluate the performance of our approach by estimating the noise and the detection of signals within the noise. We also compare our method to another classical technique, in the form of a median filter and show that our proposed approach performs better for certain signal scenarios of interest.

关键词： Noise measurement Entropy Probability distribution data models Estimation Random variables Probability density function

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Mizo Spoken Query System Enhanced with Prosodic Information

Mizo Spoken Query System Enhanced with Prosodic Information

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Oriental COCOSDA international conference on Speech database and Assessments

作者： Rupam Das Abhishek Dey Wendy Lalhminghlui Priyankoo Sarmah Samudra Vijaya Rohit Sinha Centre for Linguistic Science and Technology Gauhati University Institute of Science and Technology Gauhati University Guwahati

ISBN: (数字)9781728198965

ISBN: (纸本)9781728198972

We report the development of a Mizo spoken language interface to an online database of commodity prices in various agricultural markets of Mizoram province. The spoken query system is designed to be used over mobile phone networks. It is inspired by earlier such systems developed for languages such as Assamese and Marathi. However, considering the manifold increase in the performance of the current Mizo language spoken query system, we were motivated to report the whole system implementation in detail. The average word error rate of the DNN-HMM speech recognition system in a 5-fold cross validation experiment is 2.2%. The word error rate of the Mizo commodity name recognition system is 2.75 times smaller than that reported for a similar system for Assamese language. The reduction in error rate is attributed to data collection from diverse environmental situations with different noise levels and further processing of the data. The word error rate of the Mizo ASR system increases to 8.5% in field trials conducted with several users.

关键词： Hidden Markov models Speech recognition data collection Error analysis Computational modeling Training Speech processing

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Suitability of Single-channel Acoustic Myography for Classification of Individual Finger Movements

Suitability of Single-channel Acoustic Myography for Classif...

引用

international conference on Informatics, Electronics & Vision (ICIEV)

作者： Amirul Karim Tanim K M Talha Nahiyan M.A.R. Ahad University of Dhaka Dhaka Bangladesh

ISBN: (数字)9781728193311

ISBN: (纸本)9781728193328

Acoustic Myogram (AMG) is the vibration or sound signal produced during muscle contraction and relaxation. A simple system like a condenser microphone is enough to capture an AMG signal from muscles, unlike complex systems that are used in surface Electromyography (sEMG). Moreover, AMG signal is not highly sensitive to sensor placement like sEMG signal. Therefore, Acoustic Myography is a potential research area to find an alternative to complex and bulky sEMG system. This work focuses on verifying the suitability of single-channel AMG for the classification of individual five finger movements. AMG data were recorded from 14 subjects at two different sites on hand, namely forearm muscle and wrist. Temporal, Spectral, and Cepstral features were extracted from the collected data after required pre-processing. Two Machine learning algorithms: Support Vector Machine and K-Nearest Neighbors were applied to classify the features. From this analysis, three main outcomes were achieved: Independent five finger movements cannot be differentiated precisely using single-channel AMG data solely, whether they are from the forearm or wrist. Class reduction and grouping of some fingers increase the classification accuracy, which infers that vibrations due to different finger movements have very similar attributes. AMG from both forearm and wrist yielded similar classification accuracy, with no evidence of a site being significantly better. Contribution: This research reveals the competence of single-channel Acoustic Myography (AMG) in classifying individual hand finger movements.

关键词： Muscles Wrist Feature extraction Vibrations Machine learning Diseases data acquisition

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Video-Based Coding Of Volumetric data

Video-Based Coding Of Volumetric Data

引用

IEEE international conference on Image processing

作者： Danillo B. Graziosi Bart Kroon Sony Corporation of America and Philips Research Eindhoven

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

New standards are emerging for the coding of volumetric 3D data such as immersive video and point clouds. Some of these volumetric encoders similarly utilize video codecs as the core of their compression approach, but apply different techniques to convert volumetric 3D data into 2D content for subsequent 2D video compression. Currently in MPEG there are two activities that follow this paradigm: ISO/IEC 23090-5 Video-based Point Cloud Compression (V-PCC) and ISO/IEC 23090-12 MPEG Immersive Video (MIV). In this article we propose for both standards to define 2D projection as common transmission format. We then describe a procedure based on camera projections that is applicable to both standards to convert 3D information into 2D images for efficient 2D compression. Results show that our approach successfully encodes both point clouds and immersive video content with the same performance as the current test models that MPEG experts developed separately for the respective standards. We conclude the article by discussing further integration steps and future directions.

关键词： Encoding Indexes Erbium Circuits and systems Presses Graphics

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Group-specific models of healthcare workers’ well-being using iterative participant clustering

Group-specific models of healthcare workers’ well-being usi...

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First international conference on Transdisciplinary AI (TransAI)

作者： Vinesh Ravuri Projna Paromita Karel Mundnich Amrutha Nadarajan Brandon M. Booth Shrikanth S. Narayanan Theodora Chaspari Texas A&M University College Station TX University of Southern California Los Angeles CA

ISBN: (数字)9781728186993

ISBN: (纸本)9781728187006

Healthcare workers often experience stress and burnout due to the demanding job responsibilities and long work hours. Ambulatory monitoring devices, such as wearable and environmental sensors, combined with machine learning algorithms can afford us a better understanding of the naturalistic onset and evolution of stress and emotional reactivity in real-life with valuable implications in behavioral interventions. However, the typically large degree of inter-subject variability, due to individual differences in responses and behaviors, makes it difficult for machine learning models to robustly learn behavioral signal patterns and adequately generalize to unseen individuals. In this study, we design group-specific models of well-being (i.e., stress, sleep, positive affect, negative affect) and contextual outcomes (i.e., type of activity) based on real-life multimodal longitudinal data collected in situ from healthcare workers in a hospital environment. Group-specific models are constructed by learning an initial model based on all individuals and subsequently refining the model for a specific group of participants. Participants are originally grouped based on the feature space constructed by the multimodal data, while the original grouping is iteratively refined using the learned multimodal representations of the group-specific models. The results from this study indicate that in the majority of cases the proposed group-specific models, learned through iterative participant clustering, outperform the baseline systems, which involve general models learned based on all participants, as well as group-specific models without iterative participant clustering. This study provides promising results for predicting psychological and behavioral factors that affect the well-being of healthcare workers and lays the foundation toward ambulatory real-life assessment and interventions.

关键词： Refining Psychology Machine learning data models Stress Wearable sensors Monitoring

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Stock Price Prediction Based on Artificial Neural Network

Stock Price Prediction Based on Artificial Neural Network

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international conference on Machine Learning, Big data and Business Intelligence (MLBDBI)

作者： Xuefei Kan Miao Miao Liangcheng Cao Tianhao Xu Yanqing Li Jiyue Jiang College of Business Administration Southwestern University of Finance and Economics Chengdu China Management School The University of Sheffield Sheffield United Kingdom School of Economics & Management Anhui Agricultural University Hefei China Faculty of Information Technology Macau University of Science and Technology China School of Economics Southwest Minzu University Chengdu China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

At present, China's economy is undergoing continuous development and progress. With the emergence of new technologies, domestic emerging industries are showing a state of vigorous development, leading to strong volatility in the entire stock market, which brings complex challenges to stock price prediction. Therefore, this paper realizes the judgment of stock price based on artificial neural network and obtains more accurate prediction results. The results of the experiment show that the model proposed in this paper can accurately judge the future price trend of stocks, and the model runs faster, which fundamentally solves the defects of the traditional forecasting methods of slow operation efficiency and low forecast accuracy.

关键词： Technological innovation Artificial neural networks Information processing Predictive models data models Forecasting Stock markets

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