Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepHLS: A complete toolchain for automatic synthesis of deep neural networks to FPGA

Deep neural networks (DNN) have achieved quality results in various applications of computer vision, especially in image classification problems. DNNs are computational intensive, and nowadays, their acceleration on the FPGA has received much attention. Many methods to accelerate DNNs have been proposed. Despite their performance features like acceptable accuracy or low latency, their use is not widely accepted by software designers who usually do not have enough knowledge of the hardware details of the proposed accelerators. HLS tools are the major promising tools that can act as a bridge between software designers and hardware implementation. However, not only most HLS tools just support C and C++ descriptions as input, but also their result is very sensitive to the coding style. It makes it difficult for the software developers to adopt them, as DNNs are mostly described in high-level languages such as Tensorflow or Keras. In this paper, an integrated toolchain is presented that, in addition to converting the Keras DNN descriptions to a simple, flat, and synthesizable C output, provides other features such as accuracy verification, C level knobs to easily change the data types from floating-point to fixed-point with arbitrary bit width, and latency and area utilization adjustment using HLS knobs.
Transferring a facial depression model to estimate mood in a natural web browsing task

Because people are living in a stressful era, they are prone to common mental health problems, which cause them to experience low mood and loss of interest or pleasure. Although many suffer from depression/low mood, they hesitate to undergo clinical check-ups. Therefore, a systematic and efficient web-based system that automatically detects emotions is necessary. The purpose of this study was to design and develop a system that can automatically detect negative and positive mood states and to investigate the relationship between the depression and mood states of individuals. User’s facial expressions features are detected and analyzed in real-time and after the completion of using the system, the determined emotion is hence provided to the users. A facial depression model constructed from a dataset obtained in a human–agent interaction (HAI) experiment was applied to a general human-computer interaction (HCI) situation to classify negative and positive mood states. The model exhibits the highest accuracy rate for classifying mood states. These findings suggest that faces provide strong evidence of mood induction to depression and guidance to construct the automatic mental health care web-based system to know the preliminary mental state.
A Global System for Mobile Communication with Prevention to Reduce Higher Signal Strength in CRN

The spectrum allocation is the big problem in Cognitive Radio Network (CRN) and the reason is the unreliable deeds of Secondary Users (SU). The Secondary Users (SU) are reliable users and licensedusers in the network. These users are not responsible for malicious activities in CRN. The secondary users are using the free spectrum bands and also sometimes use the spectrum holes. The high strength of the attacker is also disturbing the actual performance PU as well as SU. This paper proposed a novel Global System for Mobile Communication with Prevention (GSMP) scheme to reduce the higher signal strength of malevolent nodes. The malevolent node transmits data from full of energy that passed over the actual receiver in the network. The mobile devices or nodes maintain the connection with others by sending control packets and after that exchanging information through established connections. The nodes are unknown from the attacker because it forwards the fake messages of the route in high strength. The attacker is detected by its strength i.e. it uses for forwarding of bogus packets that consumes the available bandwidth and by that not sends the data to the next neighbor node or sends up to the destination in the network. The proposed GSMP scheme is identified as the high signal strength at any intermediate node in the network. The higher signal strength again reaches normal by wedge its source i.e. attacker. The concert of propose GSMP is measured by different performance metrics.
Switched Reluctance Motor Drive for Electric Vehicle Applications Based on Speed Characteristics

Development in automobile sector is increasing day by day. Due to environmental pollution, conventional fuel vehicles are replaced by echo friendly advanced technologies. Due to simplicity and various advantages of SR motor, it can replace other motors. In In-Wheel motor electric vehicles, motors are installed on the wheels. The IW motor driven layout has number of benefits like fast motor response, precise torque generation, simplicity and ability to generate forward and reverse torques with no adverse effect on drive shaft stiffness. In this paper passive loading scheme is used for simulating IW SRM drive for EV.
Lateral gait analysis of a crawling robot by means of controlling the lengths of links and friction in the supports

The present paper describes three developed designs of multi-link crawling robots intended for movement on rough surfaces with obstacles. The characteristic feature of the first design is the ability to control the friction coefficients in the supports of the central link, the second design is supplemented by the ability to lift off the side links in order to "step over" obstacles. The third design is distinguished by the ability to control the friction coefficients in the supports of all links, as well as the lengths of the links in addition to the implementation of the function "stepping over" obstacles. A comparative description of the implementation of the lateral gait by the robots of all the designs is presented. The operation of the control system for the lengths of the robot’s links is described, which is based on a sequential algorithm for changing the length of the first central link and then the side links when the central one reaches its shortest length.
A Survey on Engagement and Emotion Analysis in Theatre using Thermal Imaging

This survey revolves around state-of-the research works with the main focus on Thermal Face Emotion Recognition. The prime motivation of this survey is to identify research works that recognize the facial expressions of audience in dark, so giving preference to thermal imaging and then to generate a movie review based on the emotions. Many studies had been done in the Facial Expression Recognition domain but only a handful on Thermal Face Expressions. The main problem identified was there was not much work done in this domain, so to get better understanding this paper has surveyed the existing systems used for movie review and engagement determination that is sentiment analysis done over various social media platforms and some movie review websites. These systems were subjected to forgery and fake reviews which may contribute to a very different result than what is expected. So an unbiased review system based on emotions is needed.
Data Parallel Hypersweeps for in Situ Topological Analysis

The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.
Architecting Heterogeneous Memory Systems with DRAM Technology Only: A Case Study on Relational Database

This paper advocates a DRAM-only design strategy to architect high-performance low-cost heterogeneous memory systems in future computing systems, and demonstrates its potential in the context of relational database. In particular, we envision a heterogeneous DRAM fabric consisting of convenient but expensive byte-addressable DRAM and large-capacity low-cost DRAM with coarse access granularity (e.g., 1K-byte). Regardless of specific memory technology, one can reduce the manufacturing cost by sacrificing the memory raw reliability, and apply error correction code (ECC) to restore the data storage integrity. The efficiency of ECC significantly improves as the codeword length increases, which enlarges the memory access granularity. This leads to a fundamental trade-off between memory cost and access granularity. Following this principle, Intel 3DXP-based Optane memory DIMM internally operates with a 256-byte ECC codeword length (hence 256-byte access granularity), and Hynix recently demonstrated low-cost DRAM DIMM with a 64-byte access granularity. This paper presents a design approach that enables relational database to take full advantage of the envisioned low-cost heterogeneous DRAM fabric to improve performance with only minimal database source code modification. Using MySQL as a test vehicle, we implemented a prototyping system, on which we have demonstrated its effectiveness under TPC-C and Sysbench OLTP benchmarks.
Truth Discovery Algorithm on Big Data Social Media Tweets

Counterfeit news has become a significant area of research in an assortment of orders including semantics and software engineering. In this work, explanation of how the issue is moved closer from the perspective of basic language taking care of, with the target of building a system to perceive deception in news. The main challenge in this area is gathering quality data, i.e., events of fake and certifiable reports on a reasonable scattering of subjects. In this paper, a unique truth detection system with similar words concepts is added to the scalable and robust truth discovery system used earlier. By the use of similar words concepts, the manipulated fake news can be detected much easier and faster. The features add up same meaning words which are compared using Jaccard algorithm in the main algorithm to detect more number of fake news with reliability score. The reliability score is calculated by combining independent score, attitude score and uncertainty score. The implemented software is found to be having better accuracy and results compared to existing truth detection methods.
FPGA Implementation of PWM Based Sinusoidal Drive Generation for Stepper Motor

In control electronics the stepper motor is widely preferred for its ease in usage. Sinusoidal driving of motor using FPGA ensures the precise working and improved torque of the motor. FPGA based design is compatible with conversation to an ASIC (Application Specific Integrated Circuit). Using VHDL allows the control algorithm to be easily applied. This paper presents driving a stepper motor using sinusoidal input from PWM through FPGA. This driver can perform stable, precise control and improves the torque.
Detection and Classification of Diabetic Retinopathy using Raspberry PI

Automated detection of Diabetic Retinopathy (DR) could be a potential solution for the detection of DR at the early stages, thereby minimizing the requirement of expensive and complicated surgeries. Recent advancement in the field of artificial intelligence and computing devices has fueled the research in these areas. Large databases (KAGGLE) available resulted in the application of convolution neural networks (CNN) and its variants (ImageNet) for accurate DR detection. In this paper implementation of deep learning architecture in Raspberry Pi is proposed to classify DR. Proposed system classifies DR into five different categories - No DR, Mild, Moderate, Severe and Proliferative depending on the severity of the problem. 35126 images are used for training (90%) and validation (10%) of the model. The model gave high accuracy during the testing phase and system implementation. The proposed scheme using the Raspberry PI system to test the images acquired, thereby reducing the overall cost and this facilitates the detection of diabetic retinopathy by the deployment of the system in primary health centers.
Long-Range Underwater Acoustic Navigation and Communication System

The paper presents a performance analysis of a digital underwater acoustic communication system for operating distances above 300 km in 300-500 Hz bandwidth. Estimating the system's performance relies on the underwater acoustic channel model involving a rapid transition from shelf zone to deep-sea consisting of ray patterns and channel impulse responses. Reverberation in such conditions of 200-600 ms imposes specific design requirements to an efficient digital system of commands and data transmission in such a narrow band. The bitrate of 20 bit/s was achieved for stated distances under severe multipathing.
On-board setup-hold time measurement using FPGA based adaptive methodology

The aggressive scaling of devices and increasing density of circuits at times does not allow inbuilt circuits for testing of timing parameters, which increases the emphasis on post-silicon validation for testing of these parameters. In this paper, a versatile on-board solution for testing setup and hold time of critical paths in digital designs is proposed using FPGA. Using buffers, the proposed setup provides flexibility to maneuver the step size of the test signal’s delay using coarse and fine delay for evaluating the setup/hold time, which is lucrative in testing devices with higher variations in timing parameters accurately. The adaptive nature of the design with bidirectional functionality allows robust testing of a comprehensive range of devices. The correct functioning of the system is validated by performing timing tests using Xilinx MPSoc Zynq Ultrascale FPGA.
Thermal conductivity enhancement of suspensions containing nanosized alumina particles

Various suspensions containing Al2O3 nanoparticles with specific surface areas in a range of 5–124 m2 g−1 have been prepared and their thermal conductivities have been investigated using a transient hot-wire method. Nanoparticle suspensions, containing a small amount of Al2O3, have substantially higher thermal conductivity than the base fluid, with the enhancement increasing with the volume fraction of Al2O3. The enhanced thermal conductivity increases with an increase in the difference between the pH value of aqueous suspension and the isoelectric point of Al2O3 particle. For the suspensions using the same base fluid, the thermal conductivity enhancements are highly dependent on specific surface area (SSA) of nanoparticle, with an optimal SSA for the highest thermal conductivity. For the suspensions containing the same nanoparticles, the enhanced thermal conductivity ratio is reduced with the increasing thermal conductivity of the base fluid. The crystalline phase of the nanoparticles appears to have no .
Confocal data acquisition for digital quantification using amplified single molecule detection

We have recently presented a method that enables single molecule enumeration by transforming specific molecular recognition events at nanometer dimensions to micrometer-sized DNA macromolecules. This transformation process is mediated by target specific padlock probe ligation, followed by rolling circle amplification (RCA) resulting in the creation of one rolling circle product (RCP) for each recognized target. The transformation makes optical detection and quantification possible by counting the number of generated RCPs using standard epi-fluorescence or confocal fluorescence microscopes. We have characterized the performance of the epi-fluorescence and the confocal readout formats. Both formats exhibit a linear response of the number of counted objects as a function of starting circles, and the dynamic range is three orders of magnitude employing epi-fluorescence readout and four when using confocal. In the epi-fluorescence format flow rate has to be below 1 μl/min and flow variations are likely to be the limiting factor for precision. If the flow rate is above 3 μl/min the precision of the confocal readout format is limited only by Poisson counting statistics, due to the accurate volume definition of the confocal optics. The limit of detection in the confocal format was reduced by a factor of three by increasing the data acquisition rate by a factor of ten.
A test setup for the characterization of Lorentz-force MEMS magnetometers

Lorentz-force Microelectromechanical Systems (MEMS) magnetometers have been proposed to replace magnetometers in current consumer electronics products. As a result, there exist numerous works that propose MEMS transducers and readout systems. However, when it comes to the characterization of MEMS devices, a wide variety of strategies and instruments are used, making it difficult to compare results from different works. In this article, a test setup for the characterization of Lorentz-force MEMS magnetometers is proposed. The solution in based in the use of an impedance analyser along with a simple and flexible circuit that provides the in-phase driving of the voltage and the current of the MEMS. The proposed solution has been successfully used to characterize MEMS magnetometers with very different characteristics.
Performance Comparison of Standalone AC Microgrid with Inner Loop Proportional Integral and Proportional Resonant Current Controllers

Due to the depletion of conventional energy sources and to enrich the increasing energy demand results the new concept of microgrid. Microgrid includes renewable energy sources (RES), Energy storage System (ESS), Distributed Generation (DG's) and loads in microgrid power electronic converters (PEC). PEC plays a key role in the integration of RES and ESS for energy distributed system. It assists in controlling, converting, and consumed or stored energy. Microgrid enhances the uninterruptable supply at good quality of power, reliable, stability and load sharing methodologies. Conventionally, for standalone system, the outer voltage control loop is followed by inner current control loop. The comparison of inner loop Proportional Resonant (PR) current controller using stationary reference frame and the inner loop Proportional Integral (PI) current controller using rotating reference frame is presented. Analysis of inner loop PR current controller over the inner loop PI current controller is implemented for standalone AC microgrid. It also deals with study of employment of inner loop PR current controller compared with conventional inner loop PI current controller. The inner loop PR current controller has the following advantages- steady-state error zero for sinusoidal waveforms having the same resonant frequency, lower order harmonic compensation (HC) and acts like a resonant filter. Different resonant frequencies with multiple inner loop PR current controller can operate in parallel without interfering with each other. Analysis of both the inner loop current controllers done by using MATLAB/Simulink.
A D-Band Active Single Pole Double Throw Switch in 60-nm GaN/SiC Technology

This paper presents an active single pole double throw switch (SPDT), with Gallium Nitride on Silicon Carbide (GaN/SiC) millimeter wave process, working in the D-Band (110-170 GHz) frequency range. A single branch of the switch consists of the combination of a shunt transistor and a common-source transistor, achieving low insertion loss in the transmission state as well as good isolation in the isolation state. The simulation results of the monolithic microwave integrated circuits (MMICs) SPDT switch demonstrate a minimum insertion loss of 0.64 dB with 3 dB bandwidths of 35 GHz. Within the 3 dB bandwidth the isolation of the switch is higher than 19.7 dB.
Throughput Performance Analysis of Cooperative Spectrum Sensing Network with Improved Energy Detectors in Hoyt Fading Environment

In this work, a cooptative spectrum sensing network (CSSN) for improved energy detection (IED) based CR nodes is proposed. All the CR nodes are equipped with number of antennas and perform themselves selection diversity (SC) techniques. Each CR node senses a PU through erroneous channels (sensing) and reports its sensing data (one-bit decision) to fusion center (FC) through erroneous channels. In FC, the global decision on the status of PU is made with the help of k-out-of-N fusion scheme. The expression for detection probability over noise plus Hoyt fading channels are derived and the mathematical framework for analysis of throughput of proposed network is developed. The simulation results is also presented to validate the analytically obtained performance characteristics. For several network parameters values, the investigation on comparison between IED and traditional energy detection method for different decision fusion rules is discussed. Further, performance curves are analytically illustrated for throughput over noise plus Hoyt fading channel. The impact of diversity technique and effect of fading severity parameters on the throughput is also investigated. The effect of channel error on total error performance for both proposed and conventional networks is shown. Finally, optimal network parameters values correspond to maximum throughput are also identified.
Improving UAV Personalized-Tracking Services by Fusing Visual and Radio Data

This work presents a high precision unmanned aerial vehicle (UAV) communications to detect, track and locate people during moving using real-time dataset. We use a fusion of visual and radio data collected using a UAV, where the UAV is used to provide radio signals to mobile users and to collect visual and wireless datasets simultaneously. During data collection, the UAV is connected to smartphones and laptops, and the UAV is connected to a controller and an edge server through a wireless network. You only look once version4 (YOLOv4), Kalman filter, and long-short term memory (LSTM) algorithms are combined to evaluate the proposed system. YOLOv4-Kalman filter uses video inputs to detect and track people. The detail information of the bounding boxes of the detected persons is integrated with wireless data to locate people in motion. We use a variety of optimization methods, such as batch normalization, error smoothing and DROPOUT to optimize the performances of our proposed system. The proposed model improves the conventional approach by 10 % during localization.
DC-Link Voltage Balancing with Fuzzy Logic Controller for Shunt Active Power Filter of More Electrical Aircraft

Demand for electric power is increasing in the more electric aircraft system leads to use more power electronics devices and hence power quality problems are increasing day by day. A three-phase two-level active power filter (APF) is proposed for mitigation of harmonics generated in the source side of aircraft electrical power system having variable source frequency range 360 Hz to 800 Hz due to nonlinear load present in the system. In this paper, DC link voltage of voltage source converter capacitor is maintained constant by the fuzzy logic control (FLC) technique. Fryze conductance algorithm is used for reference current tracking. Hysteresis current controller is used to produce gate pulses for switching of IGBTs. MATLAB simulation was carried out for frequency 400 Hz, 600 Hz, and 800 Hz with a variable and unbalanced non-linear load. It is observed that the performance is satisfactory in all operating states. Total harmonic distortion (THD) is well within prescribed limits as per IEEE std. 519 and MIL-STD-704F standards.