Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern Recognition in Myoelectric Signals Using Deep Learning, Features Engineering, and a Graphics Processing Unit

Intelligent robotic prostheses employ pattern recognition techniques in their construction and, for this, adopt several approaches of Artificial Intelligence (AI). The study created a system called BioPatRec-Py (inspired by BioPatRec) that implements the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in a parallel hardware, using a lightweight architecture. The introduced system employed a set of strategies to make the classification process homogeneous, reduce training time and variability. The methodology fed the algorithm with features instead of the raw signal, providing the network with information that describes the movement (level of muscle activation, magnitude, amplitude, power, among others). The research utilized an adaptive Kaufman filter to remove noise from the series of features and adopted a quantile normalization system to make the distribution uniform and facilitate the training process. It was possible to train a generic network capable of operating in the entire population analyzed. Collective training is the main contribution of the research, as it allows the prosthesis to function on various individuals and potentially under different conditions. The individually evaluated networks reached 97.44% average accuracy with 0.69 seconds of training. The global model achieved an accuracy of 97.83% with a training time of 4.01 seconds.
Analysis of Intersection Traffic Light Management System in Mandalay City

Traditional traffic signal controls at intersections are ineffective that causes traffic congestion, time wasting, and environmental problems. This paper proposes an intersection traffic light management system that incorporates machine learning technique with the traditional traffic light management system to solve the above challenges. To tackle traffic congestion and waiting time problems, Q learning algorithm is used as the reinforcement learning to choose new action. Action states include various traffic signal phases that are important in generating in realistic control mechanism. In this work, SUMO open source traffic simulator is used to construct realistic traffic intersection settings and the intersection Discretized Representation method is applied to get environment state and to calculate reward. Then, experience replay technique is used to enable reinforcement learning agent to memorize and reuse past experience. The results show that the proposed traffic system outperforms traditional traffic system in Mandalay city. The waiting time reduced 3 times compared with the traditional traffic control.
Design and Simulation of an All-Optical Plasmonic Fano Switch

The optical switch is a crucial component in optical communications that plays an important role in transmitting the optical signal within a photonic circuit. In this paper, an all-optical plasmonic switch composed of five nanodisks made from gold, planted inside SiOz substrate, based on Fano resonance, has been proposed. The simulation results disclose that the structure can work as an infrared optical switch by adjusting the pump light at 825 nm wavelength. The occurrence of the second Fano resonance (FR) due to the reduction in the distance between nanodisks was reviewed. The two-dimensional simulations using the finite-difference time-domain algorithm are utilized to reveal apparent optical bistability and notable switching mechanism of the signal light. The results show −4.00 dB and −36.84 dB transmissions when the pump is OFF and ON, respectively. As known, in highly integrated optical communication, ultra-small all-optical switches are of great importance. This switch has a nanoscale size of 450 nm×450 nm.
Optimal Energy Management of Residential Solar PV with Battery Storage: Effects of Fast Load and Generation Transients

An energy management algorithm to optimally control a rooftop solar photovoltaic device in combination with a battery storage and considering variable pricing is analyzed for single family houses. A discrete time dynamic programming controller is developed to minimize the electricity costs. It is shown in simulation, using real world measurement data, that considerable savings can be achieved with the optimal controller. However, we also show the limitations of such discrete time approaches in realistic scenarios when load and generation changes in-between the samples where the controller is active. This can lead to a performance deterioration making the optimal controller worse than a standard approach.
Human Activity Recognition using Smartphone Sensors and Beacon-based Indoor Localization for Ambient Assisted Living Systems

The aim of this paper is to present a novel indoor human activity recognition system designated for ambient assisted living environments. To overcome the drawbacks of current solutions in terms of equipment costs and computational complexity, we propose a simple yet robust activity recognition system that combines the output of an activity classification algorithm using smartphone sensors such as accelerometer and gyroscope with a Beacon-based indoor localization predictor to detect more complex activities based on a decision rule system. For the multiclass classification algorithm we employ a ConvLSTM algorithm, while for the positioning system we propose an ensemble based solution combining Multilayer Perceptron with Gradient Boosted Regression and k Nearest Neighbors. Our solution has been tested in a controlled home environment setup achieving an average localization error of 0.4m while for the final activity recognition system we report an accuracy of 91.0% concluding that the proposed solution is accurate enough to be integrated as a monitoring tool facility within ambient assisted living systems.
Internet Service of Vehicle Fleet Routing with Tabu Search and OSRM

This paper studies the development of an internet service to deal with a modification of the well-known Vehicle Routing Problem with Time Windows (VRPTW). Driven from an industrial application, the main focus is given on the effectiveness of simple metaheuristic approaches capable to provide high quality results in short computational times. Attention will be also given on environmental impacts and as such, the primary objective is to minimize the number of vehicles required to serve all customers and the secondary objective is to minimize the distance traveled. In an attempt to provide an efficient application for real-life problems, the target is to develop an internet service which automates the optimization procedure with python parsers. Routes are provided in a leaflet.js routing application, which utilised the Open Source Routing Machine (OSRM) routing engine, that enables schedulers to tackle VRPTW problems with real-life operational constraints.
A Review on the Study on MQTT Security Challenge

With the enhancement of mobile network access capabilities, the application of the Internet of Things has become increasingly widespread, and related research on the Internet of Things has received extensive attention from the academic and industrial fields. The security and efficiency of M2M in IoT are the core basic issues. Devices and systems in IoT are very complex, and the research on the communication protocols of each network layer is the cornerstone of the Internet of Things. And the MQTT is a widely used in data exchange as the application layer protocol of Internet of Things. The security problem of MQTT is of great significance. The paper discusses the hot topics of MQTT about security. This article focuses on the root causes of MQTT security, the main threats faced in MQTT, and corresponding offensive and defense strategies, including machine learning based MQTT security, replay attacks, man-in- the-middle attacks, anomaly detection, MQTT network mutual trust mechanism supported by blockchain, DoS attacks, encrypted transmission. Finally, the paper puts forward the key issues of MQTT security that should be touched in the future.
Artificial Neural Networks for Control of Three Phase Grid Connected T-Type Inverter

Multi-level inverters comparing two-level ones have many advantages. T-type is a new generation of multilevel inverters that comparing conventional converters like the NPC has fewer switches, less conductive losses, and noise. Using a proper control method for grid-connected T-type inverter can improve its performance in different working conditions. Conventional control methods in dynamic systems show limitations. By using an artificial neural network controller instead of PI controller, some problems such as overshoot, undershoot and settle time of output voltage in the face of turmoil like changing load decreases. In this paper, an artificial neural network-based controller is used to control the grid-connected T-type inverter. The neural vector controller modifies a good ability to quickly track reference values, tolerates system disorders and provides control needs for a faulty power system.
IP Geolocation Method Based on Neighbor IP Sequences

IP geolocation aims at determining the geographic location of an Internet host, which can improve the performance and security of the Internet application, and bring about novel services. Existing methods usually use measurement-based technology or data analysis-based technology, and less consider the relationship between IP addresses, this paper proposed an IP geolocation approach based on neighbor sequences. Considering the aggregation characteristics of IP addresses, the IP address location library and mobile traffic data are taken as the original data. The neighbor sequences of IP addresses are first calculated, then converted into corresponding latitude and longitude sequences, and then the model is built and solved. The experimental results based on IP addresses in Henan Province, China show that the geographic location of IP addresses can be determined by neighbor IP sequences with the mean error is between 20 kilometers and 30 kilometers. This method can also be combined with other methods to obtain better results.
Design, analysis and simulation of a biosensor Based on 2-D photonic crystal Ring Resonator

This paper presents the design of a photonic crystal ring resonator based bio MEMS sensor to detect divergent chemical and biochemical molecules such as RK3E, INOK, RK3E-ras, YD-10B, Cytosol and Nucleus. The output transmission, quality factor, detection limits and sensitivity variation of these molecules are sensed by the designed sensor, which is simulated by Finite-Difference Time-Domain (FDTD) method. A quality factor of 821 is obtained by using numerical calculations. The sensitivity of the proposed sensor is designed to be up to 881 nm/RIU (refractive index unit). As the biochemical molecules have exclusive refractive indices, the resonant wavelength, output transmission and other parameters are varied from one chemical to the other to detect the biochemical molecules.
Fault-tolerant Satellite Attitude Tracking by Modified Non-Singular Fast Terminal Sliding Mode

A modified non-singular fast terminal sliding mode as a fault tolerant control method is employed to track desired attitude of a satellite with three magnetorquers and one single reaction wheel subject to actuator faults and unknown external disturbances. Because satellites are a time-critical system, sliding variable is chosen so that the satellite attitude converges to a desired trajectory in a finite time. Furthermore, this sliding variable causes to avoid singularity. Numerical simulations are used to evaluate performance of the proposed controller for satellite attitude tracking control problem in the presence of given conditions. In addition, first and second order SMC methods are utilized to compare the performance of proposed controller.
One D-SDRE regulator for weakly nonlinear discrete statedependent coefficients control systems

Here the stabilizing regulator for quasilinear discrete state-dependent coefficients control systems is constructed based on the formal asymptotic approximation of an arbitrary order of the state-dependent Riccati equation solution. The stability of the corresponding closed-loop system is proved using the Lyapunov functions method and the series expansion of the system and criterion matrices.
2 x 3 Arrayed CMOS Capacitive Biosensors for Detection of microRNAs on a Microfluidic System

Breast cancer is one of the most important cancers among women worldwide. Since breast cancer is prone to metastasis and recurrence, mortality is relatively high if not diagnosed early. Therefore, it is important to develop a device that can quickly and accurately diagnose breast cancer. Recently, microRNAs such as microRNA-195 and microRNA-126 have been reported to act as biomarkers for breast cancer diagnosis. Therefore, this study reported an integrated microfluidic system capable of extracting microRNAs by microfluidic techniques, detecting and quantifying microRNAs by using complementary-metal-oxide-semiconductor (CMOS) capacitive biosensors for rapid and accurate diagnosis of breast cancer. Monolithic sensor with integrated-circuits reduces parasitic effects and submicron interdigitated microelectrodes as sensing interfaces significantly improves sensitivity. microRNA-195 detection for concentrations ranging from 1 fM to 10 pM could be successfully obtained, allowing detection in physiological conditions for cancer patients in the near future.
Model Reference Input Shaping Control of a Nonlinear Rotary Crane with Time-Varying Rope Length

In this paper, a model reference input shaping (MRIS) control will be formulated for a luffing dynamics of a rotary crane system with a time-varying rope length. The newly established technique is able to completely suppress the residual vibration for a highly nonlinear time-varying system. The basic idea of the proposed approach is to obtain a reference oscillation from a second-order time-invariant linear system, on which the standard input shaping control is applied. The MRIS control law is then designed to exactly match the real vibration of the system with the reference oscillation. By such a means, an exact zero vibration suppression can be achieved for the actual system. The reference (or unshaped) command is parameterized to cope with control objectives other than the vibration suppression. Simulation results are provided to verify the superiority of the MRIS controller over the standard robust input shaping control method.
Vascular control by infrarenal aortic cross‐clamping in placenta accreta spectrum disorders: description of technique

We describe a novel surgical technique in 31 women with histopathologically confirmed placenta accreta spectrum (PAS) disorders managed by a multidisciplinary team using a prophylactic infrarenal abdominal aortic cross‐clamping technique during caesarean hysterectomy. We conclude that this new surgical procedure is a relatively safe technique to potentially control operative blood loss. Our work may stimulate others to develop protocols assessing this innovative technique to improve the surgical outcome of PAS disorders.
Filtered Rayleigh scattering diagnostic for multi-parameter thermal-fluids measurements : LDRD final report.

Simulation-based life-cycle-engineering and the ASCI program have resulted in models of unprecedented size and fidelity. The validation of these models requires high-resolution, multi-parameter diagnostics. Within the thermal-fluids disciplines, the need for detailed, high-fidelity measurements exceeds the limits of current engineering sciences capabilities and severely tests the state of the art. The focus of this LDRD is the development and application of filtered Rayleigh scattering (FRS) for high-resolution, nonintrusive measurement of gas-phase velocity and temperature. With FRS, the flow is laser-illuminated and Rayleigh scattering from naturally occurring sources is detected through a molecular filter. The filtered transmission may be interpreted to yield point or planar measurements of three-component velocities and/or thermodynamic state. Different experimental configurations may be employed to obtain compromises between spatial resolution, time resolution, and the quantity of simultaneously measured flow variables. In this report, we present the results of a three-year LDRD-funded effort to develop FRS combustion thermometry and Aerosciences velocity measurement systems. The working principles and details of our FRS opto-electronic system are presented in detail. For combustion thermometry we present 2-D, spatially correlated FRS results from nonsooting premixed and diffusion flames and from a sooting premixed flame. The FRS-measured temperatures are accurate to within {+-}50 Kmore » (3%) in a premixed CH4-air flame and within {+-}100 K for a vortex-strained diluted CH4-air diffusion flame where the FRS technique is severely tested by large variation in scattering cross section. In the diffusion flame work, FRS has been combined with Raman imaging of the CH4 fuel molecule to correct for the local light scattering properties of the combustion gases. To our knowledge, this is the first extension of FRS to nonpremixed combustion and the first use of joint FRS-Raman imaging. FRS has been applied to a sooting C2H4-air flame and combined with LII to assess the upper sooting limit where FRS may be utilized. The results from this sooting flame show FRS temperatures has potential for quantitative temperature imaging for soot volume fractions of order 0.1 ppm. FRS velocity measurements have been performed in a Mach 3.7 overexpanded nitrogen jet. The FRS results are in good agreement with expected velocities as predicted by inviscid analysis of the jet flowfield. We have constructed a second FRS opto-electronic system for measurements at Sandia's hypersonic wind tunnel. The details of this second FRS system are provided here. This facility is currently being used for velocity characterization of these production hypersonic facilities. « less
Double-Framed Thin Elastomer Devices

Elastomers and, in particular, polydimethylsiloxane (PDMS) are widely adopted as biocompatible mechanically compliant substrates for soft and flexible micro-nanosystems in medicine, biology, and engineering. However, several applications require such low thicknesses (e.g., <100 μm) that make peeling-off critical because very thin elastomers become delicate and tend to exhibit strong adhesion with carriers. Moreover, microfabrication techniques such as photolithography use solvents which swell PDMS, introducing complexity and possible contamination, thus limiting industrial scalability and preventing many biomedical applications. Here, we combine low-adhesion and rectangular carrier substrates, adhesive Kapton frames, micromilling-defined shadow masks, and adhesive-neutralizing paper frames for enabling fast, easy, green, contaminant-free, and scalable manufacturing of thin elastomer devices, with both simplified peeling and handling. The accurate alignment between the frame and shadow masks can be further facilitated by micromilled marking lines on the back side of the low-adhesion carrier. As a proof of concept, we show epidermal sensors on a 50 μm-thick PDMS substrate for measuring strain, the skin bioimpedance and the heart rate. The proposed approach paves the way to a straightforward, green, and scalable fabrication of contaminant-free thin devices on elastomers for a wide variety of applications.
Earning the Trust of African American Communities to Increase Representation in Dementia Research

Abstract Black/African American populations are underrepresented as participants in dementia research. A major barrier to participation of African American older adults in dementia research is a tendency to distrust research institutions owing to a legacy of racism. Building on the Ford framework, the objective of our study was to examine factors that influence participation in dementia research among African American older adults and caregivers, with an emphasis on understanding factors related to trust. Data were collected from 10 focus groups with African American older adults (n=91), 5 focus groups with caregivers (n=44), and interviews with administrators of community-based organizations (n=11), and meetings with our Community Advisory Board. Inductive/deductive content analysis was used to identify themes. The results identified an overall tension between distrust of researchers and a compelling desire to engage in dementia research. This overarching theme was supported by six themes that provided insights about the multiple layers of distrust, as well as expectations about the appropriate conduct of researchers and academic institutions. Strong commitment to the community was identified as a priority. The findings suggest that a paradigm shift is needed to increase the representation of African Americans in dementia research. In this new paradigm, earning the trust of African American communities becomes a systemic endeavor, with academic, state and national institutions deeply committed to earning the trust of African American communities and guiding researchers in this endeavor. The findings also generated actionable recommendations to help improve representation of African American older adults in dementia research.
AFGL atmospheric constituent profiles (0-120km)

Abstract : An atmospheric data base consisting of volume mixing ratios (o0 to 12okm) for twenty eight (28) minor and trace gases has been assembled for use with spectral radiance transmittance models. Six references atmospheres, each defining temperature, pressure and density as a function of altitude (selected from the U.S. Standard Supplements, 1966 and the U.S. Standard Atmosphere, 1976) provide a range of climatological choices. Analogous zonal-mean descriptions for 2O, O3, N2O, CO, and CH4 have been subsequently adapted from satellite data and/or dynamical-photochemical analyses. The remaining species are defined by single profiles, usually appropriate for U.S. Standard conditions. Because the entire profile set is preferentially based on available measurements, explicit photochemical consistency between the different species has not been maintained. Keywords: ATMOSPHERIC CONSTITUENTS; TEMPERATURE PROFILES; MODEL ATMOSPHERES.
A retrospective survey of the seroprevalence of severe fever with thrombocytopenia syndrome virus in wild animals in Japan

Abstract Severe fever with thrombocytopenia syndrome (SFTS) has a high fatality rate and is caused by SFTS virus (SFTSV). Currently, SFTS is endemic to some areas in western Japan, and wild animals are considered to play important roles in the circulation of SFTSV in the environment. Previous retrospective surveys using samples mainly obtained between 2006 and 2015 revealed serological evidence of SFTSV infection in wild animals; however, seroprevalence before 2006 remains unclear. In this study, we investigated the presence of anti‐SFTSV antibodies in a total of 521 serum samples from nine wild animal species collected from 11 prefectures in central and eastern Japan between 1980 and 2000. All samples yielded negative results for antibodies to SFTSV, suggesting that there had been few or no SFTSV infections before 2000 in the sampled areas.