Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Precision Biomagnetic Measurement System Based on Tunnel Magneto-Resistive Effect

This paper presents a novel low-noise and high-precision readout circuit for tunnelling magnetoresistive (TMR) array to evaluate the suitability of biomagnetic measurement platform for detection of weak biomagnetic fields. We propose a three operational-amplifier architecture with a high input impedance and an adjustable gain for the fabricated TMR sensor that is highly miniaturized and can be operated at room temperature. The proposed system was designed using standard 0.18 µm CMOS technology and achieved a good performance with regard to gain, linearity, power consumption, and noise by employing a chopper stabilization technique and common mode feedback. The gain can reach 80 dB through adjusting two 5-bit programmable resistors and the input-referred noise voltage only has 44.6 nV/√Hz with 10 nA input bias over a wide range of frequency. Moreover, the whole readout dissipates 58 µW of power with a 1.8 V supply voltage. Benefiting from the CMOS compatibility of the TMR sensor, it offers monolithic integration directly on a silicon substrate as a TMR-on-chip sensing system. This will enable a new scientific and engineering paradigm to revitalize the biomagnetism field as an alternative way to understand the underlying mechanism of the human body.
A High-Performance and Power-Efficient SIMD Convolution Engine for FPGAs

This paper presents a power-efficient hardware architecture designed to perform 3D convolution operations in modern high-performance FPGA-based video processors. The proposed design exploits a novel platform-independent Single-Instruction-Multiple-Data modality to process input data, thus allowing multiple levels of parallelism to be supported. For purposes of comparison with existing hardware accelerators, several implementations are characterized using different FPGA devices. When accommodated within low-end chips, such as the Xilinx XC7Z020, the proposed convolution engine exhibits a maximum clock frequency 22% higher than the competitors, without significantly increasing the dynamic energy consumption. When more advanced DSP slices are used, like those available in Xilinx Ultrascale FPGA devices the proposed convolution engine dissipates up to 40% less dynamic energy than existing counterparts, without significant speed performance penalties.
Numerical Analysis of Coupled Oscillators in Circuit Simulation Environment

The paper discusses the modification of Kuramoto model directed to exploitation in circuit simulators. The parameters of the modification of the Kuramoto oscillator model are obtained from the simulation of the oscillator circuit in free-running mode. The parameters of the couplings are set by the transfer functions of the interconnection linear models. The simulation examples of applying the parameterized Kuramoto model are presented. The numerical experiments illustrate analysis of impact of interconnect parameters on behavior of coupled oscillators.
A New Method for Creating a Wound Model with Micro-robot in Microfluidic Device

In this study, we describe a comparative two-dimensional and three-dimensional wound healing assay with wound generated via exclusively designed and magnetically manipulated micro-robot and the healing process is observed with a holographic imaging setup. Dynamic three-dimensional cell culture was created in the microfluidic channel which is produced by soft lithography. In this three-dimensional tissue model, a wound was created with a magnetically manipulated micro-robot. The wound model was monitored by holographic image tracking system for three-dimensional healing and cell migration every 12 hours. Particularly, three-dimensional cell culture created in the microchannel using micro-gel. After the microchannel is entirely coated with cells, the biocompatible micro-robot was placed inside the channel, moving towards the center of the channel and creating a uniform hollow wound by layering cells from the center to outlet. For the monitoring system, we use the phase-changing incline Mach-Zehnder interferometer technique. With this holographic system, the migration and healing of the cells were monitored in real time and in three-dimensional. HUVECs (Human Umbilical Vein Endothelial Cells), and HCT-116 (Colon Cancer) were chosen as the appropriate cancer cell line for the experiments. Upon the experiments conducted on the monitoring system and the different capabilities of the microrobot, the system shows several advantages as discussed further in the paper. Finally, we have determined that our novel three-dimensional monitoring system could provide new horizons for the study of wound healing and likewise cell processes.
Efficiency Intensification of a Solar Structure and Comparison of PI Controller Based Converter Topologies using MATLAB SIMULINK

Solar energy is one of the promising options for the current scenario in electricity market where there is substantial decrease in conventional sources of energy. Many other nonconventional and renewable sources are in use but due to numerous advantages of solar source, it is widely used over others. Not only to convert a power from solar to electricity is the main work but utilize that power to a higher level, for different applications, to regulate its output voltage and increase its use is the first reason to make it known. For the operation of a photovoltaic system in open loop mode is of course giving output but is that correct or not that will be always judged by a closed loop system. Similarly, in case of a converter topology with a solar operated load, various MPPT methods can be exercised as a closed loop system to get accurate and regulated output. This paper focuses on MATLAB SIMULINK based PI controller working with buck and boost converters and its comparative analysis with different types of semiconducting switches, insolation and heat levels. Also, it gives in brief, different control algorithms (MPPT techniques) for solar operated structure.
Numerical Simulation of Residual Plasma Evolution in DC Vacuum Circuit Breakers Based on Artificial Current Zero

The plasma generated by a vacuum arc remains in the contact gap at current zero. This residual plasma at current zero is important to the later dielectric recovery process. Therefore, the evolution of plasma in the arcing phase is of great significance to obtain the characteristics of the residual plasma. During the arcing phase, the calculation of plasma is based on the rate equation and the Clausius-Clapeyron equation. Different kinds of particles, the fast ions, the fast atoms, the slow ions, and the slow atoms, are taken into consideration. Besides, the charge exchange and particle diffusion are involved. Then the calculation results of residual plasma are compared with the experimental results of DC current interruption. Finally, the effects of different factors on the simulation results of residual plasma are also studied.
Blind Phone Segmentation Using Contrast Function

Phone segmentation is a process of detecting the boundaries between phones in a spoken utterance. In this paper, the phone boundaries are detected without knowing contents of speech. Contrast, a concept in image processing, is investigated for phone segmentation. The speech signal is first transformed into frequency domain. Then band energy is extracted and considered as luminance in an image. A contrast function of a frequency band is defined on band energy. The peaks on the curve of contrast function present phone boundaries. The boundaries detected by eight bands are combined using probability mass function. Experiment is conducted on TIMIT corpus and results are promising. This method is also conducted on Vietnamese corpus yielding good results.
Power Factor Corrected Bridgeless Converter for Sensorless PMBLDC Motor of Ceiling Fan

In order to achieve low cost and high efficiency with improved power quality at AC mains, a single stage bridgeless flyback (SSBL-Flyback) converter is used to drive the sensorless brushless direct current (BLDC) motor for the ceiling fan. In this, the conduction losses are reduced due to bridgeless configuration of the power factor corrected (PFC) converter. It is consisting of a dual flyback configuration, which provides good power quality indices at the AC mains. The PFC converter is designed in the discontinuous mode (DCM). The high frequency transformer provides the flexibility to improve the gain and design with lower voltage stress across the switch. The main aim of this paper is to eliminate all sensors including the PFC converter output DC link voltage sensor and reduce the overall cost of the BLDC motor ceiling fan with improved power quality at the AC input. An auxiliary winding voltage is used to take care of the input voltage variation and it is also utilized to control the PFC converter output voltage (DC link voltage) and correspondingly, ceiling fan speed is controlled. The voltage source inverter (VSI) is switched at fundamental frequency to reduce the switching losses in VSI. The behaviour of the ceiling fan is simulated in the MATLAB and shown in results section to validate the design.
Blockchain Mobility Solution for Charging Transactions of Electrical Vehicles

Many countries in Europe are adopting a deregulated system where prosumers can subscribe with any energy supplier in an open market, independently of location. However, the mobility aspect of transactions in the existing system is not satisfactorily covered. For instance, if a person receives the service of charging an EV from a prosumer’s local outlet, he cannot pay to the prosumer directly without the presence of an intermediary system. This has led to a situation where the EV owners need to have a large number of subscriptions for EV charging providers and visitors cannot pay for the electricity used there. This study evaluates this mobility gap and proposes a solution for charging transactions using blockchain technology. Furthermore, we implement a proof of concept using the Hyperledger consortium platform for the technical feasibility of the proposed approach and evaluate the performance metrics such as transaction latency and throughput.
Large-scale Data Integration Using Graph Probabilistic Dependencies (GPDs)

The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.
Attention Models for PM2.5 Prediction

Air pollution is becoming a rising and serious environmental problem, mainly as a result from the migrations in urban areas. By employing effective air pollution monitoring systems, the pollution could be closely monitored, but this is not enough to make a significant impact in decreasing the pollution. The most effective value obtained from these systems is the amount of data that can be used to build pollution prediction models. To date, there are lot of different attempts to tackle the problem of air pollution prediction, but there is no evidence of their successful implementation in decreasing air pollution. In the last years, with the recent advances of deep learning techniques, and the increasing amount of data available, there are lot of proposed models for tackling the problem. In this research paper, we propose two different attention based models for air pollution prediction. Our models differ from all previous proposed models by introducing different attention factors for the previous timesteps when making a prediction. The model learns the attention factors, allowing it to learn the optimal amount that previous timesteps affect the current prediction. Using this approach we could better learn the patterns and dependencies in the data and in turn build better prediction models. We show that our models outperform two state-of-the-art models by employing our novel architecture.
Optical properties of core-shell Ag@Au and Au@Ag nanoparticles

The optical properties of spherical, bimetallic: (core-shell) plasmonic nanostructures made of gold and silver were studied. The simulations were performed using the boundary element method. The refractive index sensitivity (RIS) of the nanoparticles was calculated from the peak shift in the extinction spectrum due to the change of the surrounding medium's refractive index. The effect of the particle's material, the size of the core, and the shell on the extinction spectrum and the RIS were investigated and they were compared with the data of the pure spherical nanoparticles.
Pedestrian detection based on multi-layer feature fusion

This paper proposes a goal detection network of end-to-end multi-scale feature fusion because of the tiny pedestrian target and blocking in pedestrian detection. This algorithm is based on the YOLOv3 network, fully integrates multi-scale features, enhances the expression ability of small target features, improves the robustness of pedestrian detection in complex environments, and improves pedestrian detection accuracy based on guaranteeing real-time detection. In the experiment, the current mainstream pedestrian detection algorithm is compared. This algorithm effectively improves the detection accuracy in INRIA and KITTI data sets, and the average accuracy of Yolov3 in two different data sets is improved by 6% and 24.7%, respectively.
Agarose-biofunctionalized, dual-electrospun heteronanofiber mats: toward metal-ion chelating battery separator membranes

A facile and efficient way to impart compelling chemical functionality is the utilization of bio-related materials that are easily accessible from natural products. Here, inspired by anomalous physicochemical features and natural abundance of agarose, we demonstrate a new class of agarose-biofunctionalized, dual-electrospun heteronanofiber mats as a chemically active separator membrane for high-performance lithium-ion batteries. The agarose-enabled metal ion chelation effect of the separator membrane, in combination with its highly porous structure and superior electrolyte wettability, provides unprecedented improvement in cell performance far beyond those accessible with conventional battery separator membranes.
Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Flood disasters affect millions of people across the world by causing severe loss of life and colossal damage to property. Internet of Things (IoT) has been applied in areas such as flood prediction, flood monitoring, and flood detection. Although IoT technologies cannot stop the occurrence of flood disasters, they are an exceptionally valuable apparatus for conveyance of catastrophe readiness data. Advances have been made in flood prediction using artificial neural networks (ANN). Despite the various advancements in flood prediction systems, there has been less focus on the utilisation of edge computing for improved efficiency and reliability of such systems. In this paper, we present a system for short-term flood prediction that uses IoT and ANN, where the prediction computation is carried out on a low power edge device. The system monitors real-time rainfall and water level sensor data and uses the temporal correlative information for ahead-of-time prediction of flood water levels using long short-term memory. The system can be deployed on battery-powered IoT devices. The results of evaluating a prototype of the system indicate that our model is suitable for real-time flood prediction. Furthermore, we obtain a low response time for running the ANN prediction analysis on the low-power edge device. The application of ANN with edge computing will help improve the efficiency of real-time flood early warning systems by bringing the prediction computation close to where data is collected.
Frequency-Tunable Quasi-Elliptic Filter Using Liquid Metal

A fluidically-tuned substrate integrated waveguide (SIW) quasi-elliptic bandpass filter using reconfigurable TE101 mode cavity resonators is presented. A quadruplet filter with a pair of finite transmission zeros is presented using cross-coupled cavities to achieve a highly selective passband. The four SIW cavities have vias near their corners that are selectively filled with eutectic gallium indium (EGaIn) to tune the resonant frequency of the cavities. A symmetric via placement is implemented to maintain the par of finite transmission zeros equally spaced on either side of the passband. Measured results indicate that the filter’s center frequency can be discretely tuned between 7.96 GHz, 8.12 GHz, 8.24 GHz and 8.39 GHz, indicating a 400 MHz tuning range while maintaining a symmetric and sharp roll-off. The filter was designed and fabricated on a 1.54 mm thick Rogers 4003 substrate (ϵr = 3.55, tan δ = 0.0027).
Adaptive Fuzzy Output Feedback Simultaneous Posture Stabilization and Tracking Control of Wheeled Mobile Robots With Kinematic and Dynamic Disturbances

This article proposes a simultaneous posture (i.e., position and heading direction angle) stabilization and tracking control for differential-drive wheeled mobile robots (WMRs) using an adaptive fuzzy output feedback tracking control (AFOFTC) method and a fuzzy basis function network (FBFN). Existing studies on simultaneous posture stabilization and tracking control of WMRs do not compensate for the uncertainties and disturbances caused by kinematics and dynamics (i.e., kinematic and dynamic disturbances). To inhibit the performance degradation caused by these disturbances, an FBFN-based adaptive fuzzy output feedback posture stabilization and tracking control of WMRs is proposed considering the unavailable velocities of the WMR. In this approach, i) kinematic error equations are derived in polar coordinates, ii) a nonlinear velocity observer for WMRs containing the kinematic and dynamic uncertainties is designed, iii) a backstepping-like feedback linearization controller is designed based on the kinematics in polar coordinates to generate a reference trajectory, and iv) an AFOFTC law is proposed based on the dynamics to ensure that the WMR follows the generated reference trajectory. The proposed method can maintain satisfactory posture stabilization and tracking control performance despite the kinematic and dynamic disturbances. A stability analysis as well as the simulation and experimental results are provided to validate the proposed scheme.
Unexpected Error Explosion in NAND Flash Memory: Observations and Prediction Scheme

Wear-out has been a critical reliability problem in NAND flash memory. As executing repeated program and erase operations on the NAND flash chips, the number of errors increases and ultimately exceeds the ECC capability. In previous work, error characteristics of flash wear-out are observed by endurance tests on a single type of NAND flash memory. We wonder if the experimental results cover the entire error characteristics of NAND flash memory. In this paper, we tested more than 20 types of NAND flash chips with different vendors and structures and presented an overlook of test results. Through the test results, we found an unexpected error-explosion phenomenon that errors of flash blocks first increase over several cycles and then reach a high value without warning. We analyzed the features of the error-explosion and explored its influence on operation time. And we propose an error-explosion prediction scheme to find the blocks that will occur an error-explosion in the next 1000 P/E cycles. The block identifying operation is realized by the machine-learning model. The performance of six machine-learning methods is compared. The results demonstrate that the Decision Trees and Bagged Classification Trees have the best accuracy.
Comparison of Metal and Dielectric Millimeter-wave Antennas in 3D Printed Complementary Structures

This paper makes a comparison of metal and dielectric millimeter-wave antennas, which are fabricated by 3D printing in complementary structure. These two antennas are designed based on the same tapered slot antenna prototype. It was found that the dielectric antenna excites with extra higher order modes, thus achieves narrower main lobe and lower cross-polarization level than that of the metal antenna. The measured results show that both antennas achieve wide impedance bandwidth of 28.6% over 24-32 GHz, and have similar peak gains of 11.3 dBic and 11.42 dBic respectively. Their fabrication prototypes demonstrate dielectric antenna has a 43% reduction in volume and a 90% reduction in weight, but might suffer from the degraded radiating performance due to the fabrication error.
Development and Test of a Data Framework for Prediction of Soldering Quality in Selective Wave Soldering Applying K-Nearest Neighbors

Machine Learning has been proven to be a powerful tool to model and predict complex applications. Selective wave soldering is a widely applied interconnection technology for THT components. It is mostly used if components are not substitutable by surface mount devices due to high thermal load or mechanical stress. Especially in power electronic circuit boards, large copper layers or high thermal mass components lead to critical soldering situations. This paper suggests a Machine Learning framework to identify thermally challenging solder joints. The hybrid approach consisting of an analytical thermal description of THT components and solder joints in the multilayer circuit board and the ML analysis allows the prediction of arbitrarily complex solder joint configurations. The data framework represents electronic components and solder joints. Utilizing solder joint, component and soldering process parameters as input, the K-Nearest Neighbors algorithm predicts the probable hole fill following IPC-A-610 with an overall accuracy of about 75%.
Tomographic Reconstruction of Dynamic Features with Streaming Sliding Subsets

As the sophistication and speed of today’s X-ray experiments grow, collecting the most informative data has become ever more relevant, necessitating the development of algorithms that can provide good quality reconstructions from tomographic data streams. However, almost all conventional reconstruction systems and algorithms work exclusively offline, requiring that complete datasets be collected and available before they can be processed. Further, these systems and algorithms provide limited consideration and support for challenging experiments, such as imaging samples with dynamic features, where both spatial and temporal properties of the features rapidly change.We describe here a high-performance runtime system for analyzing and reconstructing streaming tomography datasets using sliding subsets of projection images, and evaluate the reconstruction quality of dynamic features with respect to different runtime configuration parameters using phantom and real-world tomography datasets. Our system enables runtime system parameters to be adjusted dynamically over the course of experiment, providing opportunities for balancing the quality and computational demands of tasks, better observation of phenomena, and improving advanced experimental techniques such as autonomous experimental steering.