Miracle-dz/UniFAR-FTUs huggingface dataset:
DCT-based Hyperspectral Image Classification on Resource-Constrained Platforms

Deep learning based approaches to hyperspectral image analysis have attracted large attention and exhibited high performance in image classification tasks. However, deployment of deep learning based hyperspectral image analysis systems is challenging due to the computational complexity of deep learning and the large amount of data involved in hyperspectral images. To address this problem, this paper introduces a novel framework that integrates deep neural network (DNN) based image analysis by learning the network from discrete cosine transform (DCT) coefficients for hyperspectral image classification. The framework allows designers to derive diverse implementation configurations using a variable number of DCT coefficients for training. These configurations can be used to flexibly trade off classification accuracy and computational cost (e.g., based on specific characteristics of the device being used or based on time-varying operating requirements). Through experiments using a publicly available remote sensing dataset and a resource constrained Android platform, we demonstrate that our proposed approach enables the streamlined deployment of DNN-based hyperspectral image classification.
Detecting Pinholes in Coatings with Hyperspectral Imaging

Coatings are critical to maintaining the integrity of industrial and offshore equipment, but defects reduce their effectiveness. Pinholes, locations on a coated surface which remain uncoated, are one kind of defect. They difficult to detect because of their small size. Here we investigate the possibility of using Hyperspectral imaging to find pinholes. Seven painted steel panels are imaged with a short wave infrared hyperspectral camera. Two target detection algorithms are tested for finding pinholes using two spectral signatures: steel and an underlayer of epoxy paint. One pinhole that occupies multiple pixels is identified, and several possible subpixel pinholes are detected for each target signature. We expect that further improvements to the characterization of the target signal, a better understanding of false positives and noise and improvements to the validation process will allow the detection of subpixel pinholes.
Design of Ternary Logic based on Reram Crossbars

Implementing logic within ReRAM crossbar is an attractive approach to overcome the memory wall in conventional von Neumann architectures. Compared to binary logic, the ternary logic can reduce area and power overhead with enhanced computing speed. In this paper, a ternary logic design based on ReRAM crossbars is proposed. Experimental results show that the proposed ternary logic gates have better performance in terms of switching speed, area, and power as compared to the previously published IMPLY and MAGIC designs.
Parking preview based on Unity3D

In order to allow car owners to quickly and conve niently find parking spaces in unfamiliar parking lots and keep tr ack of the car conditions. This project uses Unity3D professional technology to design and develop “the application of Unity3D- based virtual reality technology in parking”, which combines virt ual reality technology and data visualization functions. The virtu al parking system will help improve parking difficulties and other problems, and aims to serve society.
Research on Adaptive Reclosing of Distribution Network based on Signal Injection

Without discriminating the nature of faults, the traditional three-phase reclosing performs after set time, so the failure of reclosing will result in a secondary impact on the system. In order to avoid the damage of secondary impact caused by reclosing onto a permanent fault and increase the successful rate of reclosing, the application of adaptive reclosing technology for transmission lines has been relatively common, and the development of adaptive reclosing technology for distribution lines has been relatively slow. The adaptive reclosing technology of distribution network based on signal injection is studied in this paper. Compared with the fault nature identification method based on the electrical characteristics of the fault itself, the scheme of adaptive reclosing technology based on additional injection of trial signal in the primary system has the advantage of not being limited by the structure and parameter characteristics of the distribution line itself, which can fundamentally overcome the difficulties faced by the fault property identification method based on the electrical characteristics or arc characteristics of the line fault recovery stage.
Implementation of Sparse Ramanujan Sequence (SRS) based transforms in FPGA

Sparse Ramanujan Sequences (SRS) are recently introduced sequences based on Ramanujan Sums (RS). These sequences are very efficient in computation and hardware implementation. In this paper, it is proposed to obtain the hardware realization of three different SRS based transforms. The implementation results are compared with that of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). For DWT, Daubechies wavelet 10 is chosen as the mother wavelet. All the transforms and their inverse transforms are implemented as matrix multiplication operations. The transforms are coded using Verilog HDL, synthesized in Vivado 2015.2 and the computational performance is compared.
Performance Evaluation of Host Scalability in Software Defined Networks

Software Defined Networks allow flexibility for the management of computer networks due to the separation of the data plan from the control plan. As the network supports an increasing number of hosts, it becomes relevant to understand how the network performs. This article presents an analysis on the performance of SDN simulator networks as the number of hosts scales. The Mininet simulation ran 12 topologies, each of which with an increasing number of devices, initially on the number of client hosts and then on the number of switches. The analysis metrics were collected through network parameters of the TCP and UDP protocols. The simulations revealed that balanced networks distribute more evenly packet traffic and achieve greater performance.
Micromachined Catalytic Combustible Hydrogen Gas Sensor Based on Nano-structured SnO 2

A new type of micro catalytic combustible gas sensor system was designed and fabricated using micro-electro mechanical system (MEMS) technology. A chemical vapor deposition (CVD) method is used to coat porous nano-crystalline SnO2 catalyst layer. Tin chloride anhydrate vapor was used as the precursor, and it reacted with ammonium sulfide ((NH4)2S) to form tin disulfide (SnS2) nanoparticles. The tin disulfide was dried up, annealed in air, then it transformed into polycrystal- line SnO2 nanoparticles. The X-ray diffraction (XRD) measurement was used to investigate the structural properties of the SnO2 films. The morphology of the samples was investigated by field-emission scanning electron microscopy (FESEM). X-ray photoelectron spectroscopy (XPS) provided the information on chemical composition of the SnO2 films. The sensing elements and the reference elements were connected to a Wheatstone bridge circuit for the measurement of gas-sensing prop- erties. The catalytic combustion sensor exhibited relatively higher sensitivity (75.4 mV/1% H2) and good linearity (99.4%) to H2 from 0 to 4% V/V. The response and recovery times to 4% H2 were 0.65 s and 2.32 s, respectively. Finally, the sensor signal was very stable during a 200 d long term operation (accuracy>95%). It was noteworthy that the nano-structured SnO2 as catalyst film in a catalytic combustible gas sensor could considerably improve the performance of the gas sensor. It can be used in realizing portable sensing devices such as hydrogen analyzers and hydrogen leak monitors. Keywords nano tin oxide; catalytic combustible gas sensor; micro-electro mechanical system (MEMS); hydrogen detection
Design and Implementation of an Integrated Planar Transformer for High-Frequency LLC Resonant Converters

In this study, an LLC resonant converter equipped with an adjustable leakage inductance integrated transformer is proposed and applied to high-power adapters. To achieve high efficiency and power density, the magnetic circuit was increased by substituting the resonant inductor with an adjustable leakage inductance integrated transformer, and a novel core structure was designed to reduce copper loss. To further reduce the core loss and copper loss of the transformer, the core size was analyzed using a parametric technique. Finally, the total loss and physical footprint of the transformer were compared to select the most effective design point as the final design. The FEA 3D simulation was employed to verify the function of the transformer. An integrated transformer with adjustable leakage inductance replaced the resonant inductor in LLC resonant converters. Finally, a resonant converter was achieved with a switching frequency operating at 1 MHz, input voltage of 380 V, output voltage of 19 V, output power of 190 W, power density of 400 W/in3, and maximum efficiency of 94.3%.
the Carbonium Ion in the

,A general method for detailed study of enzymic reactions is presented. The method considers the complete enzyme-substrate complex together with the surrounding solvent and evaluates all the diRerent quantum mechanical and classical energy factors that can affect the reaction pathway. These factors include the quantum mechanical energies associated with bond cleavage and charge redistribution of the substrate and the classical energies of steric and electrostatic interactions between the substrate and the enzyme. The electrostatic polarization of the enzyme atoms and the orientation of the dipoles of the surrounding water molecules is simulated by a microscopic dielectric model. The solvation energy resulting from this polarization is considerable and must be included in any realistic calculation of chemical reactions involving anything more than an isolated molecule in vacua. Without it, acidic groups can never become ionized and the charge distribution on the substrate will not be reasonable. The same dielectric model can also be used to study the reaction of the substrate in solution. In this way the reaction in solution can be compared with the enzymic reaction. In this paper we study the stability of the carbonium ion intermediate formed in the cleavage of a glycosidic bond by lysozyme. It is found that electrostatic stabilization is an imp’ortant factor in increasing the rate of the reaction step that leads to the formation of the carbonium ion intermediate. Steric factors, such as the strain of the substrate on binding to lysozyme, do not seem to contribute significantly.
Analytical Design of Millimeter-Wave 100-nm GaN-on-Si MMIC Switches Using FET-Based Resonators and Coupling Matrix Method

This article presents an analytical design for millimeter-wave monolithic microwave integrated circuit (MMIC) switches. First, the field-effect-transistor-based (FET-based) resonator is proposed, whose resonant frequency and bandwidth can be controlled flexibly. As a result, the design flexibility of MMIC switches can be highly improved by using FET-based resonators. Second, a fast and flexible impedance matching technique is presented for MMIC switches based on the coupling matrix method. By selecting appropriate coupling parameters, the MMIC switches can be easily designed without designing multistage impedance matching circuits. In this way, low insertion loss (IL) and compact chip size can be accordingly obtained. For design validation, two single-pole single-throw (SPST) switch prototypes are designed and fabricated by using two or four FET-based resonators, respectively. Implemented in 100-nm GaN-on-Silicon HEMT process, high input 1-dB compression point (IP1 dB) of over 30 dBm is realized. The return losses are better than 15 dB, the minimum IL is only 1.1 dB at 30 GHz, and the isolations are higher than 45 dB. The above features indicate the validation of the proposed design method for MMIC switches.
Controlling Colloidal Stability using Highly Charged Nanoparticles

This dissertation focused on the potential use of highly charged nanoparticles to stabilize dispersions of weakly charged microparticles. The experimental components of the project centered on a model colloidal system containing silica microparticles at the isoelectric point where the suspensions are unstable and prone to flocculation. The stability of the silica suspensions was studied in the presence of highly charged nanoparticles. Initial experiments used polystyrene latex with either sulfate or amidine surface groups. Effective zeta potentials were measured with nanoparticle concentrations ranging from 0.001% to 0.5% vol. Adsorption levels were determined through direct SEM imaging of the silica microparticles, showing that the nanoparticles directly adsorbed to the microparticles (amidine more than sulfate), producing relatively large effective zeta potentials. However, stability experiments showed that the latex nanoparticles did not stabilize the silica but merely provided a reduction in overall flocculation rate. It was concluded that the zeta potential was an insufficient predictor of stability as there was still sufficient patchiness on the surface to allow for the silica surfaces to aggregate. Experiments using zirconia and alumina nanoparticles did achieve effective stabilization; both types stabilized the silica suspensions for longer than the observation period of approximately 15 hours. Stability was observed at concentrations of 10% to 1.0% (zirconia) and 10% vol. (alumina). These particles adsorbed directly to the microparticles (confirmed via SEM) and produced increasing effective zeta potentials with increasing nanoparticle concentrations. The adsorption resulted in significant electrostatic repulsion that was determined to be effectively irreversible using colloidal probe AFM. The improved stabilizing ability was attributed to the increased van der Waals attraction between the oxide nanoparticles (compared to polystyrene). Finally, an unexpected result of the CP-AFM force measurements showed that the repulsive forces between a nanoparticle-coated particle and plate lacked the normal dependence on the radius of the probe as predicted by the Derjaguin approximation. The forces observed in nanoparticle suspensions were virtually identical for 5 μm and 30 μm probes. Based on calculations of the shear rate in the gap, it was theorized that this phenomenon may have resulted from the shearing of adsorbed particles from the surfaces, which leads to similar interaction geometries for the two probe sizes.
Numerical Analysis of a Free Rare-Earth PMaSynRM for Light Electric Vehicle

In the paper is presented a numerical analysis of a free rare-earth permanent magnet assisted synchronous reluctance machine (PMaSynRM). It will be analyzed a geometrical configuration with six poles on the rotor, whose permanent magnets are made of SrO6Fe2O3. Usually, electric vehicles need reliable and resilient electric motors and today it is a worldwide trend that consists of rare-earth free electrical machines. The efficiency and torque density have to be investigated and optimized, so as a consequence the entire cost of the motor could be reduced. PMaSynRM machine is designed using finite element method (FEM) simulation. The proposed configuration presented in the paper, is analyzed, by taking into account the generator working conditions, as a first step in the new machine configuration design. The cogging torque is computed, using FEM simulations and the three phased electrical voltages are presented.
An ns3-based Energy Module of 5G NR User Equipments for Millimeter Wave Networks

This poster presents the design, development and test results of an energy consumption analysis module developed over ns3 Millimeter Wave (mmWave) communication for analyzing power consumption for 5G New Radio (NR) User Equipment (UE) during both continuous and discontinuous packet receptions. This module is important to analyze and explore the energy consumption behavior of the 5G communication protocols under the NR technology. The developed module includes the complete Radio Resource Control (RRC) state machine for 5G NR recommended by 3GPP Specification 38.840. To the best of our knowledge, the designed module is the first of its kind that provides a comprehensive energy analysis for the 5G NR UEs over mmWave communication.
Secure ACO-Based Wireless Sensor Network Routing Algorithm for IoT

The problem of developing effective and secure routing protocols has drawn more interest in network research with the popularization of IoT-connected devices. The perception of ensuring security is expensive in terms of computation, power and user-interface components because of low-power IoT objects or sensor nodes. Secret keys distribution schemes are computationally expensive and require more resources. Rather, bio-inspired mechanisms are considered more robust as they offer optimal and inexpensive solutions for designing secure routing algorithms for their inherent adaptable and scalable features. Moreover, trusted neighbor discovery is a crucial task. That is why a complementary security measure, trust evaluation system, is adopted here. In this paper, a secure bio-inspired WSN (Wireless Sensor Network) routing protocol based on ant colony optimization (ACO) algorithm for IoT has been proposed and analyzed to find secure and optimal path that is energy-efficient as well as aiming at providing trust in IoT environment. The performance of the proposed routing algorithm is evaluated utilizing MATLAB. The assessment results indicate that it can find the forwarding path with comparatively low cost in the premise of ensuring security and has minimized the average energy consumption by nearly 50% even as the number of nodes has increased, when compared with the traditional ACO algorithm, an existing ant colony based routing algorithm and a current routing protocol for IoT.
The Working Principles of ESP32 and Analytical Comparison of using Low-Cost Microcontroller Modules in Embedded Systems Design

With the emergence of many low-cost development platforms, designing an embedded system has become more flexible using different microcontroller platforms. However, it is necessary to consider various factors before selecting the brain of embedded systems, the microcontroller. This paper compares two commonly used development boards in designing embedded systems with one of the latest boards manufactured for high-performance, low-cost embedded systems design called ESP32. The comparison is based on hardware specifications, as well as programming tools. A Wi-Fi analyzer system was designed and implemented as an example of using the ESP32 module in low-cost, low-energy embedded systems design. In addition to the ESP32, a Graphical LCD is used in the design as an output device. However, there is a lack of literature on establishing serial communication between the ESP32 module and the Graphical LCD. Therefore, this work presents a clear explanation of connecting the Graphical LCD to the ESP32 module using only three GPIO pins.
Online Detection of Soft Internal Short Circuits in Lithium-Ion Battery Packs by Data-Driven Cell Voltage Monitoring

Besides the performance and range requirements, the breakthrough of electromobility depends crucially on the safety of battery systems. Cell faults that can lead to thermal runaway of the energy storage reduce customer acceptance. Thermal runaways are often preceded by an Internal Short Circuit (ISC). Thus, there is a necessity for a method that is able to detect the formation of ISCs, before a significant temperature rise is observed and the risk of thermal propagation arises. To achieve this, a data-driven method that enables early detection of soft ISCs is presented. The unsupervised machine-learning method is based on linear Principal Component Analysis (PCA) and nonlinear Kernel PCA (KPCA). Since the method only requires fault-free voltage measurements for training, it is directly applicable in conventional battery systems. The nonlinear KPCA is thoroughly compared with the linear PCA using experimental data. The data originates from a module consisting of twelve automotive cells. While the linear method has advantages in computational complexity, the nonlinear method detects ISCs earlier due to its high sensitivity and specificity.
Anomaly detection in Internet of Things using feature selection and classification based on Logistic Regression and Artificial Neural Network on N-BaIoT dataset

According to the paradigm of the Internet of Things (IoT), physical devices are connected to each other and to the Internet such that they operate automatically. One of the major challenges in the IoT is to detect and prevent intruders into the network and devices, a challenge that traditional solutions of Intrusion Detection Systems (IDS) are not responsive for it or at least not very efficient to use in IoT. In this article, we address the problem of using machine learning methods for anomaly detection and two methods for feature extraction and classification are proposed. The first method is feature extraction and classification using Logistic Regression (LR) and the second method is to use an Artificial Neural Network (ANN) for classification. To evaluate the performance of the proposed method, the N_BaIoT dataset, which consists of data samples related to nine devices IoT and several attacks is used according to a number of criteria for evaluating the performance of the proposed methods. Simulation results in comparison with other four deep learning methods in terms of F1-score and precision show that using logistic regression, is more efficient and the highest classification accuracy (equivalent to 99.98%) can be achieved.
A Novel Signal Separation Method for Micro-Motion of Helicopter Target

Micro-Doppler is the unique micro-motion feature of the target, which can be used as an important basis for helicopter target recognition. In this paper, the clutter component, fuselage component and micro-motion component of helicopter echo is analyzed. Based on the scattering point model, the micro-Doppler echo model under clutter background is deduced and established. Secondly, pseudo hyperbolic tangent function filter is constructed in the frequency domain. One of filter stopband notch is placed at zero frequency, and the other notch is searched with the contrast index of time-frequency diagram. The double filter stopband notch design is realized, and the separation of clutter component and fuselage component in helicopter echo is effectively completed. Finally, performance comparison with MTI filter and Kalmus filter shows the effectiveness of the proposed method.