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Aggregate 2022年第4期3卷 85-100页

作者： Pavana Siddhartha Kollipara Ritvik Mahendra Jingang Li Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTexasUSA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTexasUSA Material Science and Engineering Program Texas Materials InstituteThe University of Texas at AustinAustinTexasUSA

Developing on-chip functional devices requires reliable fabrication methods with high resolution for miniaturization,desired components for enhanced performance,and high throughput for fast prototyping and mass ***,laser-based bubble-pen lithography(BPL)has been developed to enable sub-micron linewidths,in situ synthesis of custom materials,and on-demand patterning for various functional components and *** exploits Marangoni convection induced by a laser-controlled microbubble to attract,accumulate,and immobilize particles,ions,and molecules onto different *** years have witnessed tremendous progress in theory,engineering,and application of BPL,which motivated us to write this ***,an overview of experimental demonstrations and theoretical understandings of BPL is ***,we discuss the advantages of BPL and its diverse applications in quantum dot displays,biological and chemical sensing,clinical diagnosis,nanoalloy synthesis,and *** conclude this review with our perspective on the challenges and future directions of BPL.

关键词： additive manufacturing capillary flow lithography marangoni convection microbubbles sensing

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Enhancing Energy Efficiency in Residential Buildings: A Comparative Analysis of Supervised and Unsupervised Learning Techniques

Enhancing Energy Efficiency in Residential Buildings: A Comp...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Reji, K. Resmi, R. Rohini, S. Padmavathi, S. Sreevidya, C. Vishwanathan, Rekha P. Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Electrical and Biomedical Engineering RMIT University 124La Trobe St MelbourneVIC3000 Australia

ISBN: (纸本)9798350325188

In this paper different machine learning algorithms are used to predict the energy performance of residential buildings. Prediction of energy consumption has a major role in energy conservation, management, and planning. The data is classified using a variety of supervised and unsupervised techniques. For generating predictive models, predictive modelling algorithms are employed for regression as well as classification. It is examined to determine how well the various predictive models perform and contrast with each other. A comparative analysis was implemented to assess the surmising performance of the model. From the results, we can infer that the Decision Tree Classifier performs the best in terms of classification and in clustering algorithms produced on the dataset, the AdaBoost algorithm performs better compared to the K-Means algorithm. While in Ensemble methods, a combination of Decision tree and Logistic regression exhibits higher performance Finally, this paper substructures the expediency of using machine learning algorithms to estimate the energy performance of buildings as a beneficial and authentic approach. © 2023 IEEE.

关键词： Classification Decision Tree Energy Performance Machine Learning Regression

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An 800 kS/s 1.83 fJ/conv. 12b ADC via Voltage Successive Approximation and Gated Cyclic Vernier Time Digitization

An 800 kS/s 1.83 fJ/conv. 12b ADC via Voltage Successive App...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Ian Perczak Fei Yuan Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

A 12b analog-to-digital converter (ADC) consisting of an 8b successive approximation register (SAR) ADC, a voltage-to-time converter (VTC), and a gated cyclic Vernier time-to-digital converter (TDC) is presented. A number of design techniques including a sub-threshold delay-locked loop (DLL) with pulsed outputs to generate the clock signals of the ADC, a power/area-efficient technique to eliminate the kickback-induced errors of the comparator, a gated cyclic Vernier TDC to eliminate resolution loss at both input injection and catch, a self-resetting arbiter, and a power/area-efficient synchronous SAR are proposed. The ADC is designed in a TSMC 130 nm CMOS technology with a reduced supply voltage of 0.8 V. The simulation results of the ADC with a 7.42 kHz 400 ∼ 800 V differential sinusoidal input voltage show that at 800 kS/s, the ADC offers 59.32 dB SNDR and 9.56 ENOB while consuming 3.44 µW, yielding 1.83 fJ/conv. FOM.

关键词： Circuits and systems Simulation Voltage Logic gates CMOS technology Registers Signal resolution

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Stacking multiple optimal transport policies to map functional connectomes 57

Stacking multiple optimal transport policies to map function...

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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Dadashkarimi, Javid Rosenblatt, Matthew Karbasi, Amin Scheinost, Dustin Yale University Department of Computer Science New Haven United States Yale University Department of Biomedical Engineering New Haven United States Yale University Department of Electrical Engineering Department of Computer Science Department of Statistics and Data Science New Haven United States Yale School of Medicine Department of Radiology and Biomedical Imaging New Haven United States

ISBN: (纸本)9781665451819

Connectomics is a popular approach for under-standing the brain with neuroimaging data. However, a con-nectome generated from one atlas is different in size, topology, and scale compared to a connectome generated from another. Consequently, connectomes generated from different atlases can-not be used in the same analysis. This limitation hinders efforts toward increasing sample size and demonstrating generalizability across datasets. Recently, we proposed Cross Atlas Remapping via Optimal Transport (CAROT) to find a spatial mapping between a pair of atlases based on a set of training data. The mapping transforms timeseries fMRI data parcellated with an atlas to form a connectome based on a different one. Crucially, CAROT does not need raw fMRI data and thus does not require re-processing, which can otherwise be time-consuming and expensive. The current CAROT implementation leverages information from several source atlases to create robust mappings for a target atlas. In this work, we extend CAROT to combine existing mappings between a source and target atlas for an arbitrary number of mappings. This extension (labeled Stacking CAROT) allows mappings between a pair of atlases to be created once and re-used with other pre-trained mappings to create new mappings as needed. Reconstructed connectomes from Stacking CAROT perform as well as those from CAROT in downstream analyses. Importantly, Stacking CAROT significantly reduces training time and storage requirements compared to CAROT. Overall, Stacking CAROT improves previous versions of CAROT. © 2023 IEEE.

关键词： Mapping

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Improved Vehicular Congestion Classification using Machine Learning for VANETs

Improved Vehicular Congestion Classification using Machine L...

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Annual IEEE Systems Conference

作者： Syed Ammad Ali Shah Xavier Fernando Rasha Kashef Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

Vehicular Ad-hoc Networks (VANETs) emerge as an inevitable element for autonomous driving, smart cities and intelligent transportation systems. The vehicular traffic density classification plays a crucial role in making important traffic routing and data transfer decisions between vehicles and surrounding infrastructure. However, vehicular density in a given area vastly varies depending on the environment (urban, rural, highway etc.), the day and the specific time of the day. There can also be unpredictable density variations due to traffic incidents or social events. Therefore, accurate classification of traffic density is essential to properly plan data communication in VANET. This paper studies a number of machine learning (ML) algorithms to accurately classify the traffic condition based on the data collected from intelligent sensors. First, the traffic flow and average speed data is collected for each vehicle. In the second step, vehicular density is estimated using speed and flow relationship in a given area. In the third step, traffic state is classified as "Free-Flow", "Dense", and "Congested" based on the congestion cost report by Victoria Transport Policy Institute. Finally, we utilized a range of ML approaches, including Decision Tree (DT), Na¨ıve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Classifier (SVC), Logistic Regression (LR), and Multilayer Perceptron (MLP) to categorize instances of traffic congestion. The results are studied based on classification accuracy, recall and precision metrics. The experimental results indicate that RF and subsequently Ensemble Soft Voting classifiers exhibit the best performance among all other classifiers, including the MLP model.

关键词： Training Accuracy Vehicular ad hoc networks Static VAr compensators Predictive models Vectors Classification algorithms

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Gated Ring Oscillator Time Amplifier with Pico-Second Sensitivity and Applications in All-Digital Variable-Gain Time Integrator

Gated Ring Oscillator Time Amplifier with Pico-Second Sensit...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Fei Yuan Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

A gated ring oscillator time amplifier (GROTA) featuring a well-defined programmable gain, the ability to amplify a pico-second input, an unlimited range of gain, and full compatibility with technology is proposed. The transfer characteristics of the GROTA and a time offset tuning mechanism enabling the amplification of a pico-second input are presented. The characteristics and impact of supply noise and device noise are studied. A variable-gain time integrator consisting of the proposed GROTA and a bi-directional gated ring oscillator (BD-GRO) time integrator is developed and its silicon implementation in TSMC 120 nm 1.2 V CMOS is provided with simulation results.

关键词： Ring oscillators Sensitivity Circuits and systems Simulation Noise Bidirectional control Logic gates

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A 20mW 130-175GHz Phase Shifter with Meandered λ/2 TLINEs in BiCMOS 55nm

A 20mW 130-175GHz Phase Shifter with Meandered λ/2 TLINEs i...

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European Conference on Solid-State Circuits (ESSCIRC)

作者： Lorenzo Piotto Guglielmo De Filippi Andrea Bilato Andrea Mazzanti Department of Electrical Computer and Biomedical Engineering University of Pavia

This paper presents a compact and low-power D-Band four-quadrant active phase shifter using meandered λ/2 transmission-lines (TLINEs) for phase inversion. TLINE impairments (loss and phase deviation from 180°) are analyzed and their effects on the output vector space are discussed. A calibration technique is applied such that the phase shifter RMS amplitude and phase errors are kept under 1.2dB and 8°, respectively. Implemented in SiGe BiCMOS 55 nm, the proposed circuit covers 130- 175 GHz bandwidth with -4.5dB peak gain, $\mathrm{O}\mathrm{P}_{\mathrm{1}\mathrm{d}\mathrm{B}}$ greater than -3dBm and 20mW average power consumption.

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Hybrid Optimization Approach for Energy Savings in Cooling Tower using IoT and Gradient Boosting 8

Hybrid Optimization Approach for Energy Savings in Cooling T...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Selvakumar, R. Amarnath, Raveendra N. Pandey, Pramod Sakthisaravanan, B. Sasikala, K. Murugan, S. Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Mphasis MelissaTX United States Nagpur Campus Maharashtra Pune India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Improving operational efficiency and lowering environmental impact are made possible by optimizing energy usage in industrial cooling tower systems. This paper presents a new method for optimizing the management of industrial cooling towers, with a particular emphasis on energy savings, by combining Internet of Things (IoT) technologies with Gradient Boosting approaches. IoT sensors monitor the cooling system's temperature, flow rate, and humidity in real time. This data makes predictive analytics on system performance and energy usage possible by training Gradient Boosting models. It shows that the proposed approach successfully achieves substantial energy savings while keeping cooling tower performance at its optimum via extensive testing and analysis. The findings show that Gradient Boosting and IoT monitoring might help industrial settings with proactive energy management. By providing a workable framework for improving cooling tower operations' energy efficiency of sustainable industrial practices. The results highlight the significance of using data-driven strategies and advanced technology to solve energy problems in manufacturing, which will enhance efficiency and sustainability while reducing costs. Data analysis from the real world, performance measurements, and comparison findings show that the system is more efficient and system performance of assessment. © 2024 IEEE.

关键词： Energy management Energy savings Environmental impact Industrial IoT Sustainable practices

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Towards A Minimalistic Sensor-Based Smart Soccer Net With Machine Learning

Towards A Minimalistic Sensor-Based Smart Soccer Net With Ma...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Nguyen Ho Zaid Fafouri Austin Gregory Michael Okwori Department of Electrical Computer and Biomedical Engineering Union College Schenectady NY USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

The global appeal and competitive nature of soccer have led to the application of technology to monitor and enhance player training and performance. Effective and data-driven training can reduce injury related to over-exertion and boost player performance. Shooting is a crucial skill in soccer and can significantly influence the outcome of a game. This work addresses the lack of a smart and privacy-aware device to track and analyze players’ shot localization accuracy, particularly for amateur players. We investigate a smart soccer net that can localize the point of impact of the ball from the perturbations of small inertial measuring sensors attached to the net. We train a feedforward neural network with raw data and statistical-based features, and a convolutional neural network with time- and frequency-based features from continuous wavelet transformation (CWT). Also, we investigated one, two, three, and four sensors deployed in selected configurations on the net. Four sensors deployed in a rhombus configuration and a model trained with CWT-based features achieved the best classification accuracy of $94 \%$. A cost-effective option of using three sensors in an inverted triangle configuration and the model trained with statistical-based features achieved $90 \%$ accuracy. This work is a step towards a minimalistic system that can be used as a training aid to automatically track the positions of soccer player shots in the goal during training sessions.

关键词： Training Performance evaluation Accuracy Continuous wavelet transforms Tracking Computational modeling Sensor phenomena and characterization Data models Intelligent sensors Sports

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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