Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyber-physical Control System for Autonomous Logistic Robot

The rapid development of intelligent control technology has affected the logistics industry and led to the implementation of new concepts of autonomous indoor logistic robots. These robots are operating inside warehouses and factories to move goods and packages around in the rooms. There is a high motivation to reduce the cost of these mobile robots as well as to create flexible control systems. Tallinn University of Technology in cooperation with a Kulinaaria production plant has developed a new small scale logistic robot to move boxes in rather tight spaces. New technology, including AI and machine vision, is applied to achieve the flexibility and cost-effectiveness of the newly developed mobile robot solution.
Privacy Protection and Secure Transmission of Smart Meter Data

Energy companies can collect users’ energy consumption information through smart meters, and provide users with various services based on these data information. However, the data collected includes sensitive personal information, which may lead to privacy leakage. Currently, existing schemes still do not solve the security, utility and computational efficiency of data privacy information well. Therefore, this paper proposes a system to protect the privacy and transmission of smart meter data, that is PSSM, to protect the privacy of user data and ensure the integrity of data transmission. In PSSM, we combine wavelet transform with Gaussian Distributed Differential Privacy (GDDP) to protect the confidentiality of data, and for the first time use Schnorr signature technology to verify the correctness of data. At the same time, the data aggregation module is used to collect the data of all users in a specific area, and then randomly send each user’s data to the Data Processing Units (DPUs) for data processing. Experimental simulation show that the scheme is superior to the existing scheme in terms of utility, security and computational efficiency.
Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter- and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.
A Novel Voltage Reconstruction Method for MRAS based Sensorless IPMSM Drives

Different advanced sensorless techniques have been proposed based on the model reference adaptive system (MRAS) principle to make the interior permanent magnet synchronous motor (IPMSM) drives mechanically more robust. However, in the special application such as high-power traction systems, the difference between the switching frequency and the calculation frequency is large, and the inconsistency between the command voltage and the actual voltage will affect the accuracy of the MARS estimator. To overcome such a drawback, this paper proposes a novel voltage reconstruction method to improve the accuracy of the MRAS estimator. Simulations and experiments are conducted to verify the effectiveness of the proposed method. Combining with the proposed algorithm, the problem of the inconsistency can be solved, and an accurate rotation estimation can be realized.
Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally.
A SPICE Compact Model for Ambipolar 2-D-Material FETs Aiming at Circuit Design

We report a charge-based analytic and explicit compact model for field-effect transistors (FETs) based on 2-D materials (2DMs), for the simulation of 2DM-based analog and digital circuits. The device electrostatics is handled by invoking 2-D density of states and Fermi–Dirac statistics that are later combined with the Lambert-W function and Halley’s correction to eventually obtain explicit expressions for the electron and hole charges, which are exploited in the calculation of drift-diffusion currents for both carriers. Furthermore, the charge model is extended to obtain characteristics of 2DM-based negative capacitance FETs. The model is benchmarked against experimental MoS2 FET measurements and experimental ambipolar characteristics of narrowband-gap materials, such as black phosphorus. Its soundness for SPICE circuit-level simulations is also demonstrated.
A Novel Single Switch Step Down Converter

A new step-down dc-dc converter with one active switch and two diodes is presented in this paper. Compared to other buck type topologies, the proposed converter exhibits smooth current at the input and it is shown to be better suited in applications where a small difference between the input and output voltage is required. This novel converter efficiency is excellent over a wide range of the duty cycle. A dc analysis is performed and main equations in steady state are provided, then device stresses are evaluated and compared to other buck topologies in many aspects showing better features. A prototype based on the design equations presented in this paper is simulated and then experimentally tested. The experimental results confirm the theoretical considerations and the simulations, proving the feasibility of the proposed topology.
Thermal Crosstalk Characterization Using Temperature Dependent Leakage Current Through Gate Stacks

In this letter, a new method based on the temperature dependence of gate leakage current for accurately characterizing thermal crosstalk is proposed and performed on the advanced silicon-on-insulator (SOI) MOSFETs. The developed technique enables to capture the pure thermal crosstalk effect as it excludes the self-heating impact during the measurement. Moreover, it is proven robust over a large range of gate voltages regardless of the difference of device types, which is demonstrated on a pair of devices with a shared port. The accuracy of the thermal crosstalk measurement benefits in determining the overall thermal performance of the nano-scaled circuits under the mutual heating impacts. It offers a possible hardware solution to thermal monitor and management.
A Self-Supervised CNN for Particle Inspection on Optical Element

In high-power laser instruments, optical elements play a significant role. Particles on the optical element degrade the system performance and even cause damage to the optical element. In this article, a particle inspection model based on self-supervised convolutional neural networks (CNNs) and transfer learning is proposed. The self-supervised network that is built on a rotation-flip-invariant pretext task is used to transform the image from grayscale feature to rotation-flip-invariant feature. Then, the learned feature is transferred to the central-pixel classification network that is fine-tuned on a small labeled dataset. The experiments show that the classification accuracy of our proposed method is 97.90%, which is higher than the other compared methods. For the whole image prediction, through feature reuse and pointwise convolution, the central-pixel classification network is adapted to the particle inspection network efficiently with minor changes. Since the method utilizes massive unlabeled data and is fine-tuned on a small number of labeled samples, it has the potential to be used in industrial production.
Cybernetics, Systems Biology, and the Phenomenological Gap [Historical Perspectives]

When I entered graduate school in 2014 with the goal of studying biological control systems, something I quickly noticed was that many of the most prominent people in the field seemed to have an incredible admiration for the ideas that grew out of Norbert Wiener’s cybernetics movement. Not knowing any of the history, the term cybernetics sounded more like something out of an early Isaac Asimov novel than a genuine research discipline. Though there was this clear admiration of cybernetics, Wiener’s work, for the most part, was not directly incorporated in our curriculum or textbooks in the way that the works of Bode, Shannon, and Nyquist were, aside from a few highly technical topics like Wiener filters and Wiener processes. What I took from this is that it must have been an interesting idea but not something that was a central part of the rest of the theoretical framework for analyzing feedback systems that made up my first years in the control systems graduate program.
Chaos Embedded Particle Swarm Optimization Technique for Solving Optimal Power Flow Problem

This paper proposes chaos embedded particle swarm optimization, (CEPSO) to achieve optimal settings of the optimal power flow problem's (OPF) control variables. Various objective functions such as fuel cost minimization, voltage profile improvement, voltage stability enhancement, active and reactive power losses, are achieved through using the proposed algorithm. All objective functions are processed as single objectives in each case. The performance of the algorithm is tested on the IEEE standard 30 Bus system. The CEPSO's and PSO's results are compared with each other. As objective functions the fuel cost minimization, voltage profile improvement, reactive power loss minimization were first time processed by the proposed algorithm. The obtained result of the fuel cost objective function is compared with other algorithms' results.
MPPT based on P&O control and FLC-Hill Climbing technique for a Photovoltaic Generator

This paper deals with a control of a photovoltaic generator using a maximum power point traking control which is an important function in all photovoltaic (PV) power systems through the conventional P&O method and the Hill-Climbing technique. It also presents an intelligent approach for the improvement and optimization of this control performances of a photovoltaic system, through the association of fuzzy logic control with the MPPT control. The simulation is developed under matlab/Simulink. different methods are used with a boost DC/DC converter. these results show us the efficiency and the robustness of a fuzzy logic based controller instead of the classical P&O controller.
Marine Current Energy Conversion System driven DFIG connected to dc-bus

This paper presents the Field Oriented Control (FOC) of a Doubly Fed Induction Generator (DFIG) linked to a DC -bus in Marine Current Energy Conversion (MCEC) System. The DFIG-DC system is an important configuration which used a diode rectifier to ensure the connection between the stator of the DFIG and the DC-bus, while the rotor is connected to the load and the inverter. The control method used for the MCEC system is the FOC which use the d-axis rotor current to regulate the stator frequency and the q-axis rotor current to keep the dc voltage constant. Simulation and experimental results confirm the performance of the proposed control strategy.
PGTLP: A Dataset for Tunisian License Plate Detection and Recognition

The whole point throughout this work is a publicly available and annotated images dataset for vehicle license plate (LP) detection and recognition. This contribution is driving by the fact that no Tunisian LP dataset has been provided up to this point in time. Pearl Guard Tunisian LP (PGTLP) dataset is fully annotated database that contains presently up to 3, 000 images captured by high definition quality camera mounted on a security mobile robot, called Pearl Guard and made accessible to the Academia. For benchmarking and ranking supply, we propose a unified, real-time (LP) detection module based on YOLOv4-tiny detector. We also provide baseline results for vehicle LP detection using the PGTLP dataset.
5G Channel Characterization at Millimeter-Wave for Baghdad City: An NYUSIM-based Approach

The massive bandwidth available at millimeter-wave (mmWave) spectrum can accommodate the increasing capacity demands in 5G systems and beyond. However, the channel noise is high at mmWave frequencies and is largely affected by atmospheric conditions, such as temperature and humidity, which vary for different geographical location and throughout the day. It is therefore indispensable to construct proper modeling for mmWave channels as the channel model has a profound impact on the overall system performance. Accordingly, in this paper, a custom model is first developed, via the generic NYUSIM channel simulator, that fits the properties of mmWave propagation in Iraq environment and also the effect of weather parameters, like temperature and humidity, on link quality is discussed. Then, novel answers are provided to key questions related to 5G system design which include identifying the optimal, i.e., power-efficient, transmission frequency, the typical base stations layout and the effect of various factors like transmission environment and antenna configuration. Extensive numerical results have shown that while the 28 GHz band is seen to be the most power-efficient, the 45 GHz band, on the other side, is more robust against the diurnal and seasonal atmospheric variations. It is also seen that a network of small base stations (SBSs) operated at 28 GHz is comparable in performance to macro base stations (MBSs) with 73 GHz of frequency, which means that the difference in transmission powers between MBSs and SBSs can be compensated by switching to a lower frequency band.
Improved Incremental conductance algorithm for MPPT in Photovoltaic System

This paper proposes a new approach to improve an incremental conductance maximum power point (MPPT) tracking algorithm with variable step size to achieve the maximum power point quickly in photovoltaic systems. Among the most used MPPT methods in photovoltaic systems, the INC algorithm is the most cited one. The MPP changes with multiple environmental factors, mainly temperature and irradiance. Conventional algorithms such as fixed step INC suffer from a trade-off between convergence speed and accuracy. To get around this problem, several variable step techniques are proposed. This algorithm is modified to quickly find the maximum power when there is a sudden change in solar irradiation. The proposed modification allows to vary the initial step gradually to approach the maximum power point quickly, once completed the step is fractionated after each “slope” performed. This technique reduces the oscillations and determines the PPM to the desired accuracy. A series of simulations involving variations in irradiance were performed using MATLAB/SIMULINK, and the speed of convergence and accuracy were compared with the traditional INC technique. Simulation results proved the effectiveness of the proposed modification technique to quickly reach the PPM with less oscillation in the case of a large and rapid change.
Surface Enhanced Raman Spectroscopy of a Au@Au Core–Shell Structure Containing a Spiky Shell

We have synthesized a novel Au@Au core–shell structure containing a spiky shell and characterized the surface enhanced Raman spectroscopy (SERS) of such a structure. The experimental and calculated SERS intensities for probe molecules residing between the core and shell are considerably higher in this structure containing a spiky shell, when compared to the smooth shell counterpart. Moreover, the SERS intensities for probe molecules residing on the outside of the spiky core–shell structure are comparable to those measured for the dye residing between the core and shell and similar to that of a gold nanostar. Finite-difference time-domain calculations in combination with hybridization theory are able to predict the extinction spectral features and show that the field enhancements are considerably larger than the smooth core–shell structure throughout the visible and infrared regions of the spectrum. This spiky Au@Au core–shell system shows excellent promise as a next generation SERS nanotag, in which encap.
Efficient Multiparty Interactive Coding—Part I: Oblivious Insertions, Deletions and Substitutions

In the field of interactive coding, two or more parties wish to carry out a distributed computation over a communication network that may be noisy. The ultimate goal is to develop efficient coding schemes that can tolerate a high level of noise while increasing the communication by only a constant factor (i.e., constant rate). In this work we consider synchronous communication networks over an arbitrary topology, in the powerful adversarial insertion-deletion noise model. Namely, the noisy channel may adversarially alter the content of any transmitted symbol, as well as completely remove a transmitted symbol or inject a new symbol into the channel. We provide an efficient, constant rate scheme that conducts any computation on any arbitrary network, and succeeds with high probability as long as an oblivious adversary corrupts at most <inline-formula> <tex-math notation="LaTeX">$\frac { \varepsilon }{m}$ </tex-math></inline-formula> fraction of the total communication, where <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is the number of links in the network and <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> is a small constant. In this work (the first part), our scheme assumes that the parties share a random string to which the adversarial noise is oblivious. While previous work considered the insertion-deletion noise model in the two-party setting, to the best of our knowledge, our scheme is the first multiparty scheme that is resilient to insertions and deletions. Furthermore, our scheme is the first <italic>computationally efficient</italic> scheme in the multiparty setting that is resilient to <italic>adversarial</italic> noise.
High Linear Power E-Band Traveling-Wave Tube for Communication Applications

A low-voltage and high linear power ${E}$ -band traveling-wave tube amplifier is designed to meet the demand of high-data-rate wireless communication. The integrated design, including the dispersion combination of folded waveguide slow wave structure, broadband diamond pillbox window, and AlN-SiC lumped attenuator, ensures the success of the prototype tube with satisfying performance. The test results show that the amplifier is capable of providing more than 45.2 W output power, 38.8 dB saturated gain, 14.3% total efficiency, and 45.6 dB small-signal gain in 71–76 GHz band with a low voltage of 12.88 kV and a current of 80 mA. The 1 dB compression point exceeding 20 W verifies the feasibility of dispersion combination technology for high linear power design.
Alternative computational methods for electromagnetic circuits of disk-type induction motors — comparison and verification

Due to specific shape of electromagnetic circuit in disk-type motors, electromagnetic calculations should make use of three-dimensional models. However, such calculations are very time-consuming and this poses practical problems. The answer might be to use other alternative methods (not based on 3D models), which would make it possible to obtain appropriately accurate results. Comparison of calculation results obtained with elaborated alternative computational methods for electromagnetic circuits of disk-type induction motors is given in this paper. The results have been compared with FEM 3D results and verified by lab test results (tests run with a physical motor model). When definite simplifications are adopted, the alternative computational methods make it possible to reduce computational time in a significant way. The achieved results are highly convergent with results acquired for real model.
Design and analysis of Multi-stage TDF/(1+TI) controller for Load-frequency control of A.C Multi-Islanded Microgrid system using Modified Sine cosine algorithm

This research work presenting an innovative approach to design AC off-grid Multi Microgrid system and proposing an analytical aspect of load frequency control using the Modified sine cosine algorithm based Multi-stage tilt TDF/(1+TI) controller. A Multi Micro-Grid system unites distinctive advantages identical to RES (Renewable energy sources), photovoltaic system model, wind turbine model, etc. The asymmetrical property of RES causes a contradiction between the load ultimatum and the source of the Multi Microgrid system. This asymmetrical property of renewable energy sources affects the system frequency adversely. So, nowadays there is a major challenge to stabilize the frequency oscillations in a Multi Microgrid system. Despite all these complications, this paper proposes an approach to stabilize the system frequency by a Modified sine cosine algorithm (MSCA) tuned Multi-stage tilt TDF/(1+TI) controller. To enhance the modern power system capability to meet the recent nonlinear load demand, Microgrid plays an important component to fulfill the critical loading effectively. To establish the efficacy of the proposed Multi-stage tilt TDF/(1+TI) controller, it has been analyzed by the dynamic responses of the classical PI & PID controller. The effectiveness of the MCSA has been validated by the comparative analysis of responses over the most popular responses obtained by the GA & PSO method.