Human user study (N = 10) of wrist torque perception in various movement conditions

Coordinated Universal Time (UTC) is generated through transferring the time data of hundreds of atomic clocks to the Bureau international des poids et mesures (BIPM). Time transfer using the Global Navigation Satellite Systems (GNSS) is one of the most essential and widely used time transfer techniques. During the path from the transmitter to the receiver, the signals are subject to many phenomena that affect their characteristics. Multipath is one of these phenomena that degrade considerably the time transfer stability. Multipath errors are random errors that cannot be predicted. So, there are no systematic and standard method for compensating the multipath effect. In this paper the multipath phenomena were studied using a real time transfer data. It was proved that the elevation-azimuth profile of the satellite is affecting considerably multipath errors, and hence, a smart, systematic, and simple method was proposed for mitigating the effect of multipath on the time transfer stability.

Named entity recognition (NER) is an important task for clinical knowledge discovery from electronic health records (EHRs). However, to protect the patient privacy, we want to minimizethe annotation needs for NER system development. Therefore, advanced semi-supervised learning algorithms are required. In this paper, we reviewed the recent advance in semi-supervised learning for named entity recognition and discussed the possibility of application in the clinical NER system. We hope our discussions help the development or improvementfor the clinical NER applications for other researchers.

Even though the Taylor-Ulitovsky process for producing microwire has existed and been widely used in the past century, there are various challenges facing the microwire manufacturing process, such as inconsistent wire diameter, constant breaks of microwire during fabrication, and the difficulty of producing wires with a smaller diameter. These challenges can make the microwire fabri- cation process inefficient, and this research aims to understand how thermal im- ages from the fabrication process under various parameter settings can be used to assess and classify the quality of the microwire. Thermal videos and other process variables were collected from a microwire manufacturing lab, and the thermal image datasets from the video were trained using a pretrained Convolutional Neural Networks (CNN) in order to better understand how changing certain pa- rameters for the microwire manufacturing process can affect the microwire qual- ity. The features extracted from the thermal images using the proposed CNN- based machine learning algorithm is capable of classifying the microwire fabri- cation process into four stages, i.e., initialization stage, stable stage 1, stable stage 2, and ending stage. The stage classification accuracy reveals high repeatability and performance from the proposed CNN model. The results are promising since manufacturing process parameter settings can be adjusted and optimized by re- ferring to thermal image characteristics, and therefore the CNN model can im- prove microwire quality and predict failure of the microwire.

This paper proposes a FAst paRAllel and pipeliNE Q-learning accelerator (FARANE-Q) for a configurable Reinforcement Learning (RL) algorithm implemented in a System on Chip (SoC). The proposed work offers flexibility, configurability, and scalability while maintaining computation speed and accuracy to overcome the challenges of a dynamic environment and increasing complexity. The proposed method includes a Hardware/Software (HW/SW) design methodology for the SoC architecture to achieve flexibility. We also propose joint optimizations on the algorithm, architecture, and implementation to obtain optimum (high efficiency) performance, specifically in energy and area efficiency. Furthermore, we implemented the proposed design in a real-time Zynq Ultra96-V2 FPGA platform to evaluate the functionality with an actual use case of smart navigation. Experimental results confirm that the proposed accelerator FARANE-Q outperforms state-of-the-art works by achieving a throughput of up to 148.55 MSps. It corresponds to the energy efficiency of 1747.64 MSps/W per agent for 32-bit and 2424.33 MSps/W per agent for 16-bit FARANE-Q. Moreover, the proposed 16-bit FARANE-Q outperforms other related works by an improvement of at least 1.23× in energy efficiency. The designed system also maintains an error accuracy of less than 0.4% with optimized bit precision for more than eight fraction bits. The proposed FARANE-Q also offers a speed up of processing time up to 1795× compared to embedded SW computation executed on ARM Zynq processor and 280× of computation of full software executed on i7 processor. Hence, the proposed work has the potential to be used for smart navigation, robotic control, and predictive maintenance.

The data used our analysis is from an ID fan which is part of a coal powered plant that produces a target output of 117W. The fan is placed at the outlet of the boiler. The fan runs at an air flow rate of 5365 Am3/min , with a density of 0.85 kg/m3, diameter of 2440 mm , with a total pressure of 505 mmAq and a speed of 989 rpm. The fan impeller is driven by a 650 kW electric induction motor (6600 V 50 Hz 3 Phase) with a speed of 1000 rpm.

Simulation for the fault current limiting operation of REBCO CORC® superconducting cables with different core materials

A switching cell based on Hybrid Switched Capacitor and Modular Multilevel Converters is presented. Automatic arms voltages clamping are achieved, while the current spikes and the efficiency dependence on duty cycle can be reduced by a proper design. A Buck type topology simulation and experimental results are presented and an example of cell application as a single-phase inverter is shown.

This research will focus on the differences and similarities between the various Hadoop distributions on-premises or in the cloud. We will also focus on cloud services and components and try to compare them. We will choose Cloudera as Hadoop distribution and AWS, Azure as cloud distribution services. Distributed cloud services have opened the door to easy use and integration of extensive IT services without much manual effort, which is possible with the help of open source communities. However, some enterprises do not see cloud services as a trusted or secure option for their data, so cloud service providers (CSP’s) are considering hybrid solutions as a trend. In general, we can see that these distributions are converging in recent years, and enterprises tend to choose the public cloud as a deployment model. This convergence makes any comparison a difficult task. In this study, we will attempt to develop a general comparison method for cloud services and Hadoop distributions that can serve as a basis for future comparison studies.”}]}This research will focus on the differences and similarities between the various  distributions on-premises or in the cloud. We will also focus on cloud services and components and try to compare them. We will choose  as  distribution and AWS, Azure as cloud distribution services. Distributed cloud services have opened the door to easy use and integration of extensive IT services without much manual effort, which is possible with the help of open source communities. However, some enterprises do not see cloud services as a trusted or secure option for their data, so cloud service providers (CSP’s) are considering hybrid solutions as a trend. In general, we can see that these distributions are converging in recent years, and enterprises tend to choose the public cloud as a deployment model. This convergence makes any comparison a difficult task. In this study, we will attempt to develop a general comparison method for cloud services and distributions that can serve as a basis for future comparison studies.

Computerized Numerical Control (CNC) machines are the building block of industrial manufacturing, while CNC machines (CNCM) are designed to work relentlessly, breakdown may occur due to ageing, wrong operation, malfunctioning of sensors/actuators etc. Breakdown of CNCM hampers the production, supply chain and occurs financial loses. To improve the availability of the CNCM, predictive maintenance is considered a gold standard which requires continuous monitoring of the state of CNCM. The state of the CNC machine is temporal and requires a continuous data stream (CDT) to represent the correct present state. To achieve CDT, we propose and evaluate the Self-Adaptive Digital Twin (SADT) model of the CNC Machine (CNCM) using open-source industry-standard OPCUA protocol in compliance with RAMI4.0 architecture. We tested our approach on actual CNC machines in an industrial setup in production environment. We found that the proposed SADT with data analytics can identify potential issues in the CNCM, various bottlenecks in production and provides real-time notifications for fault prediction, which leads to the optimization of spares and improved predictive maintenance. We presented a few applications of the proposed SADT and found that it helps in optimising the axis closed loop control, thereby reducing the vibrations and the following error by five times. It also predicts the runtime of the part program accurately in real-time by analysing the current feed rate. We observed a 15% increase in Mean Time Between Failure (MTBF), 11% decrease in Mean Time to Repair (MTTR), 21% reduction in the percentage of breakdowns and 14.2% reduction in the power consumption of the CNCM after the implementation of the proposed system.

A new class of vulnerabilities, called trojan source, has been recently discovered by Boucher and Anderson. They allow attackers to inject specific Unicode control characters in the source code of a software project, such that what text editors visualize differs from what compilers interpret. In the absence of specific protections, trojan source vulnerabilities can be exploited to launch effective supply chain attacks that appear invisible to developers. This work describes the state of the art of known trojan source attacks, illustrates two new attack variants involving configuration files and Java code, and describes practical preventive measures. The first variant affects Java platforms and may enable the execution of instructions injected within comments. Whereas the second variant exploits BiDi and homoglyph Unicode symbols within source code and configuration files. We also give several proofs of concept for many popular development platforms and provide a detection tool that can be easily integrated into the build process.

A bimodal video imaging platform combining RGB and 371-band hyperspectral imaging systems was used to collect time-series data of the Lake Ontario shoreline at Hamlin Beach State Park in Rochester, New York, USA. We predicted the hyperspectral image frames of dynamic natural water scenes at previous and later points in time using a paired relationship between the time-series hyperspectral imagery and RGB video. The time-series hyperspectral image data was collected using our Headwall Hyperspec micro-HE line-scanning imaging spectrometer integrated into a General Dynamics pan-tilt unit. RGB video data was collected with a low-cost consumer GoPro Hero 8 Black. We detail our data collection methods and characterize the predictions using distributions of absolute and normalized residuals in reflectance spaces. Within visible wavelengths, 95% of the scene is predicted to within 2% absolute reflectance. The normalized error percentage of these residuals translates to approximately 30% of signal level for water spectra. In the near-infrared regime, the normalized error percentage of the residuals sharply increases to approximately 90% for 95% of the scene due to lack of band information from the RGB video imagery of our shallow water scene.

In this paper, we investigate the optimal power allocation at the MAC layer in both the asymptotic and the finite blocklength regimes in a two-user NOMA uplink network, under statistical delay constraints captured through the link-layer rate. Using the link-layer effective rate analytic expressions, we provide closed-form expressions of the optimal power coefficients in low and high signal-to-ratio (SNR) regimes. These are validated by an extensive set of simulations, showing that that the proposed power allocation policy optimizes the performance of NOMA compared to OMA at Layer 2.

An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.

The paper studies the method for online teaching using Multi-Track Modular Teaching (MT2). MT2 is an advanced teaching-learning method for better training of students with deep skills.  Covid19 pandemic and the associated lockdown periods, coupled with the requirements of social distancing, have played a major disruption in the academic processes across all the world’s universities. Due to the inability to conduct physical classes and lectures, most universities have opted for online classes using video conference tools. One way to make the computer networking classes, during lock-down periods, more effective is to use the Multi-Track Modular Teaching (MT2) technique which organizes a course into multiple tracks and modules. This paper presents the mod?ifications carried out to MT2-based computer networking course and experiences from teaching it online during the lock-down period. While the online classes offered an alter?native to the completely disrupted teaching and learning process, observations on their effectiveness reveal certain important considerations in order to make them more useful. Results from a feedback survey of the students who under?took the course is also provided in this paper, giving insights to certain difficulties and issues in the conduct of online classes in developing countries.

The increasing interest in the residential renewable energy sources leveraged the development of the Battery Energy Storage Systems (BESS) ecosystems that allow the losses mitigation caused by the lack of intelligent storage of unused energy. BESS’s have reduced user’s costs by storing the overproduced energy by the renewable sources and using it when the demand was bigger than the production. Despite that, cost minimizing can yet be improved by applying a Energy Management System (EMS) which takes in consideration several factors and draws a policy to reduce costs. Model-based approaches have been thought as a solution but the increasing complexity of the models turned the researchers into a model-free direction using Reinforcement Learning. This paper creates an innovative agent, trained by Deep Reinforcement Learning algorithms, capable of reducing the costs in a building. A literature review of other approaches, a solution formulation to solve the problem, and evaluation using synthetic data and data supplied by an energy company are contained. Furthermore, evaluation scenarios and techniques that can lead to agent’ instability are mentioned. Results indicate that the agent has better performance in terms of revenue when compared with the same BESS operation without the agent.

Cloud computing has become a crucial component in order for an organization to store and manage its data effectively from its computers on a single platform. It is crucial for  a firm to implement cloud technology because so many organizations today incorporate the  use of various mobile devices into their everyday operations. Thanks to the development of  cloud computing, workers may easily collaborate on their devices wherever they are as long  as there is internet connectivity. By facilitating simple collaboration and securing data backup  for any unanticipated recovery, it gives great purpose to a company. The goal of this review  article is to conduct peer-review on cloud computing applications.

Uncertain renewable power generation and on-site demand would complicate battery operation and affect profitability. In this paper, a novel battery scheduling model is proposed for maximizing arbitrage benefits while meeting the peak shaving requirements. The proposed model features accurate expectations and is essentially a stochastic optimization accounting for uncertainty. The convexity of the proposed model is strictly proved, which ensures that the global optimal solution is obtainable and computationally attractive. Based on the firstorder optimality condition, we further derive the analytical form of the optimal solution, which explicitly demonstrates the impact of uncertainty on the optimal dispatch of battery storage. Both the convexity and the solution stability of the proposed model are verified through extensive case studies involving various time periods and multiple algorithms for convex optimization. In addition, the change in the trade-off price threshold caused by uncertainty is also illustrated in simulations.