Miracle-dz/UniFAR-FTUs huggingface dataset:
Approach in the development of lightweight microservice architecture for small data center monitoring system

In the past decade there is a significant trend of implementing IoT technologies and standards in different industries. This trend brings cost reductions to the companies and other benefits as well. One of the main benefits is real-time and uniform data collection. The data are transferred using diverse communication protocols, from the sensor nodes to the centralized application. So far, current approaches in developing applications are not proved itself to be efficient enough in scenarios when a significant amount of data needs to be stored and analyzed. The focus of this paper is on development of software architecture suitable for usage in Internet of Things (IoT) systems where the larger amount of data can be processed in real-time. The software architecture is developed in order to support the sensor network for monitoring the small data center and it is based on microservices. Besides the system and its architecture, this paper presents the method of analysis of system performances in real-time environment. The proposal for lightweight microservice architecture, presented in this paper, is developed with . NET Core and RabbitMQ, with the utilization of MongoDB and SQLite databases systems for storing data collected with IoT devices. In this paper, the system evaluation and research results in different stress scenarios are also presented. Because of its complexity, only the most significant segments of architecture will be presented in this paper. The proposed solution showed that proposed lightweight architecture based on microservices could deal with the larger amount of sensor data in the case of using MongoDB. On the other hand, the usage of SQLite database is not recommended due to the lower performances and test results.
Density-dependent quantized kernel least mean square with desired smoothing

Kernel least mean square algorithm is a simple and effective adaptive algorithm, but it is constrained by its infinite network size. Many schemes have been proposed to reduce the size of the network, but the distribution of input data and the desire output are seldomly considered together. In this paper, we present a density correlation quantization scheme in input space and a method of smoothing the desired output by using the average value of the neighbourhood in output, and call it the DQKLMS-S algorithm. The good performance of DQKLMS-S is verified by experiments.
Using SPSS for Solving Engineering Problems

The aim of this work is to help students to identify the role of statistical methods in the engineering problem-solving process. A structured methodology of applying different statistical tools in the software package SPSS is presented in details. The topics considered in the work include descriptive statistics, hypothesis testing, confidence intervals, linear regression and correlation analysis, as well as analysis of variance (ANOVA). An example from engineering practice is used to illustrate the methods. The methodology could be used in the practical classes for engineers in Applied Mathematics and Higher Mathematics.
Development of a Static Wireless Power Transfer System for Electric Bikes

This paper presents the design and development of a static wireless power transfer system for electric bikes. The system consists of two main parts: the power transmitter circuit and the power receiver circuit, in which the power source is a 9 Ah 36 V battery. The developed power transfer system was tested, and the condition at which the best power transmitter efficiency occurred was also found by taking consideration of two factors: frequency and distance between the power transmitter and the power receiver. The test results show that the wireless power transfer system being designed and developed can deliver power effectively. In addition, the highest power transmitter efficiency is 73.57 % at the resonance frequency of 36 kHz and the distance between the power transmitter and the power receiver of 4 cm.
Time-Series Information and Unsupervised Learning of Representations

Numerous control and learning problems face the situation where sequences of high-dimensional highly dependent data are available but no or little feedback is provided to the learner, which makes any inference rather challenging. To address this challenge, we formulate the following problem. Given a series of observations <inline-formula> <tex-math notation="LaTeX">$X_{0}, \ldots,X_{n}$ </tex-math></inline-formula> coming from a large (high-dimensional) space <inline-formula> <tex-math notation="LaTeX">$\mathcal X$ </tex-math></inline-formula>, find a representation function <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> mapping <inline-formula> <tex-math notation="LaTeX">$\mathcal X$ </tex-math></inline-formula> to a finite space <inline-formula> <tex-math notation="LaTeX">$\mathcal Y$ </tex-math></inline-formula> such that the series <inline-formula> <tex-math notation="LaTeX">$f(X_{0}), \ldots,f(X_{n})$ </tex-math></inline-formula> preserves as much information as possible about the original time-series dependence in <inline-formula> <tex-math notation="LaTeX">$X_{0}, \ldots,X_{n}$ </tex-math></inline-formula>. We show that, for stationary time series, the function <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> can be selected as the one maximizing a certain information criterion that we call time-series information. Some properties of this functions are investigated, including its uniqueness and consistency of its empirical estimates. Implications for the problem of optimal control are presented.
Low-Loss Millimeter-Wave SPDT Switch MMICs in a Metamorphic HEMT Technology

This letter presents the design and performance of two single-pole double-throw (SPDT) switches operating in <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula>-band (50–75 GHz) and <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band (75–110 GHz). The millimeter-wave (mmW) integrated circuits (MMICs) are fabricated in a 50-nm gate-length metamorphic high-electron-mobility transistor technology. Special attention was paid to the reduction of the insertion loss (IL). Thus, both switch MMICs achieve an IL of 1–1.6 dB (average 1.2 dB), covering the entire <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula>-band and <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band, respectively. The isolation (ISO) of the switches is better than 31.6 and 28.5 dB, respectively. The input power for 1 dB of IL compression is at least 22 and 19 dBm, respectively. A wafer mapping of both circuits exhibits a high yield and low spread of IL and ISO. Based on the given results, the presented SPDT switch MMICs demonstrates state-of-the-art performance.
Optimal PWM for Three-Level Inverter fed High Speed Drives

Thanks to new field of applications, like high speed/high-pole drives with high rated fundamental frequency or multilevel converters powering medium or high voltage drive systems, optimized PWM techniques became again a hot topic of research interest. The current paper introduces an optimized PWM technique for three-level inverter, which can be applied to supply high speed drives at low pulse ratio. The optimization is done for the lowest loss-factor, which is proportional to the square of rms value of current harmonics. The performance of the optimal PWM technique is demonstrated by simulation and experimental tests by using a NPC type inverter.
A Ku-Band 6-Bit Vector-Sum Phase Shifter With Half-Quadrant Control Technique

This paper presents a 6-bit vector-sum phase shifter with half-quadrant control technique for Ku-band phased arrays. In this paper, the gain and phase symmetry in each quadrant is studied. Based on the gain and phase symmetry in vector-sum phase shifter, the half-quadrant control technique is proposed to simplify the complexity of phase control, phase measurement and phase calibration for large-scale phased arrays. Besides, a phase optimization method without extra phase settings is proposed to achieve low phase error. As results, the measured 6-bit RMS phase error is 1.6° ~ 2.3° across 13 ~ 17 GHz with only 64 different phase states, demonstrating the brevity and accuracy of phase control simultaneously. With the same phase optimization, six chips are measured and the maximum deviation of RMS phase error is < 0.3°. The measured peak gain is 3.8 dB at 14.5 GHz, and the measured 3-dB frequency band is 13.4 ~ 15.5 GHz. The measured RMS gain variation is 0.85 ~1 dB across 3-dB frequency band. The input-referred P1dB is > −11.4 dBm. The chip consumes 29.7 mW with 1.8 V supply and occupies $0.9\times1.45$ mm2. This work is fabricated in TSMC 180-nm CMOS technology.
DARS: An EDA Framework for Reliability and Functional Safety Management of System-on-Chips

Electronic design automation tools used for Design-For-Test infrastructure insertion have often relied on test standards (e.g. IEEE 1149.1, 1500 and 1687) as a structured methodology for IC test access, which consequently reduce the design cost for DFT. The IEEE 1687 standard introduces an efficient methodology for the off-chip access of the increasing number of embedded instruments that are used for test, debug and other purposes. A subset of these instruments is also used for Reliability and functional Safety (RaS) management, while accessing them via an on-chip manager. In this paper, we present a design automation framework for the RaS management of System-on-Chips using embedded instruments, by utilizing the IEEE 1687 standard as the key enabler of this automation. The framework enables the on-chip execution of cross-layer RaS procedures by the automatic generation of a dedicated design layer for the procedures execution. The framework utilizes the IEEE 1687-defined PDL language and the pattern retargeting process for enabling a programming model for developing the RaS procedures with no regard to the instruments access procedures and their physical locations, which consequently reduce the development time of RaS procedures and enable their scalability and reusability, and hence their automation.
Survey of Soft Error Mitigation Techniques Applied to LEON3 Soft Processors on SRAM-Based FPGAs

Soft-core processors implemented in SRAM-based FPGAs are an attractive option for applications to be employed in radiation environments due to their flexibility, relatively-low application development costs, and reconfigurability features enabling them to adapt to the evolving mission needs. Despite the advantages soft-core processors possess, they are seldom used in critical applications because they are more sensitive to radiation than their hard-core counterparts. For instance, both the logic and signal routing circuitry of a soft-core processor as well as its user memory are susceptible to radiation-induced faults. Therefore, soft-core processors must be appropriately hardened against ionizing-radiation to become a feasible design choice for harsh environments and thus to reap all their benefits. This survey henceforth discusses various techniques to protect the configuration and user memories of an LEON3 soft processor, which is one of the most widely used soft-core processors in radiation environments, as reported in the state-of-the-art literature, with the objective of facilitating the choice of right fault-mitigation solution for any given soft-core processor.
Predicting Music Popularity Using Music Charts

Online streaming platforms have become the most important method of music consumption. Most streaming platforms provide tools to assess the popularity of a song by means of scores and rankings. In this paper, we present a methodology for predicting if a song will appear on Spotify's Top 50 Global ranking after a certain amount of time. Spotify is one of the biggest streaming services and if a song is popular on this platform, it is likely to ensure good financial return to the artists and their label. We approach the problem as a classification task and employ classifiers built on past information from the platform's Top 50 Global ranking, in addition to acoustic features from the songs. The Support Vector Machine classifier with RBF kernel reached the best results in our experiments with an AUC higher than 80% when predicting the popularity of songs two months in advance.
Cascade First and Second Order Sliding Mode Controller of a QuadRotor UAV based on Exponential Reaching Law and Modified Super-Twisting Algorithm

Unmanned aerial vehicles have become a disruptive technology, which has experienced exponential growth in several applications. The control of these vehicles is a fairly wide area and the cascade PID controller is the most used in practice. However, this latter structure doesn’t ensure high performances in the presence of unmodelled dynamics, uncertainties and external abrupt disturbances. To that end, this work proposes a new method that consists of a non-linear cascade configuration of the variable structure control between first order sliding mode based on exponential reaching law and modified super-twisting second order sliding mode algorithm. The developed method is tested on simulation on a quadrotor system, the results obtained demonstrate good performance for trajectory tracking and as well as other non-linear controller options, it is robust against unmodeled dynamics and disturbances.
State of Charge Based Droop Control for Coordinated Power Exchange in Low Voltage DC Nanogrids

Decentralized battery and solar photovoltaic (PV) system organized in the form of an autonomous low voltage DC nanogrid is a potentially low cost and scalable solution for electrifying rural areas without access to the national grid. Each DC nanogrid can be installed on a single home and used to supply basic lighting, charge mobile phones and power a television set. To provide enough power to meet productive energy uses such as irrigation, the DC nanogrid can be connected to neighboring DC nanogrids to form a cluster and exchange power. However, to achieve a coordinated power exchange in the cluster, new control strategies are required. In this paper, we propose a decentralized droop control method which uses a state of charge of the battery to coordinate the power exchange. The power exchange is achieved by scheduling a terminal voltage set point at each DC nanogrid based on the state of charge of the battery. The performance of the proposed method at achieving the power exchange is analyzed through simulations in Matlab/Simulink. The method does not require inter-unit communication. Therefore, the method is reliable, robust and scalable. Also, the method maintains low amounts of power flow in distribution lines during power exchange to reduce distribution line power losses.
Development of On-Demand Controller for Continuous Positive Airways Pressure

Continuous Positive Airway Pressure (CPAP) is one of the primary treatments for sleep apnea. In a conventional CPAP, the device produces a constant air pressure to maintain the opening of the respiratory tract. The common problem found in the conventional CPAP is discomfort due to long term air pressure during sleep. Further, in subjects who undergo training to strengthen upper airways muscles, the application of high air pressure during non-apnea period may reduce the muscular strength. In this work, we propose to develop an on-demand CPAP controller that follows the normal respiratory pressure during non-apnea event and produce a pre-set positive air pressure during the sleep apnea event. Not only improve the comfortability of the subjects, our method also useful for patients who plan to discontinue using CPAP in the future. Our proposed model was developed using LabView software. We simulated breathing signal to represent normal breathing and apnea condition. Then a proportional and integral control system was developed for regulating the air pressure. The results show that in 60 trials with several setting points, our model achieved the average rise time 0.75s, overshoot 3.79%, and settling time 1.72s. The total accuracy of the method was 100% in detection of sleep apnea events. Our method can be developed into a low-cost device for sleep apnea treatment in Indonesia.
Assisted Gait Phase Estimation Through an Embedded Depth Camera Using Modified Random Forest Algorithm Classification

The paper presents a novel method for the classification of gait phases for power gait orthosis users based on machine learning. The classification uses depth images collected from a Time of Flight camera embedded in the crutches employed for the assisted gait. The machine learning algorithm foresees an initial phase of data collection and processing, identifying the 3D points belonging to the foot and those belonging to the floor. From these, a feature set is computed analyzing the values of percentiles of distances of the foot from the floor, and passed to a modified version of Random Forest classifier, called Sigma-z Random Forest. The classifier considers the uncertainties associated to each feature set and provides both the classification of the gait phase (stance or swing) and an associated confidence value. In this work, we propose the use of the confidence value to improve the reliability of the gait phase classification, by applying an optimized threshold to the confidence value obtained for each new frame. The algorithm has been tested on different subjects and environments. An average classification accuracy of 87.3% has been obtained (+6.3% with respect to the standard random forest classifier), with a minor loss of unclassifiable frames. Results highlight that unclassifiable samples are usually associated to transitions between stance and swing.
A CSRR-Loaded Planar Sensor for Simultaneously Measuring Permittivity and Permeability

This letter proposes a microwave planar sensor for the characterization of magnetodielectric materials. The sensor is designed by loading microstrip lines with complementary split-ring resonators (CSRRs). Two patches are connected to the microstrip lines to couple with the CSRRs, and a pair of 50-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> resistors are in series with the lines to provide a notch. The designed sensor can extract the permittivity and permeability of the magnetodielectric materials simultaneously. A prototype of the designed sensor is fabricated and measured for validation of characterizing the magnetodielectric materials.
Using Smartphones to Enable Low-Cost Secure Consumer IoT Devices

This paper proposes a solution for low-cost consumer IoT devices to employ end-to-end security without requiring additional hardware. Manufacturers of consumer IoT devices often sacrifice security in favour of features, user-friendliness, time to market or cost, in order to stay ahead of their competitors. However, this is unwise, as demonstrated by recent hacks on consumer IoT devices. Low-cost embedded devices struggle to create suitable entropy for key generation; on the other hand, smartphones are both abundant and have multiple sources of entropy for strong key generation. The proposed architecture takes advantage of these properties and offloads key generation and transfer to the user’s smartphone, removing the need for constrained IoT devices to perform public key infrastructure and generate symmetric keys. The authors implemented the design on a $1 general-purpose microcontroller and then analysed the performance. The design allows all communication to and from the device to be encrypted while being simple to setup, low-cost and responsive without any additional manufacturing cost. The architecture presents a general solution, which could be implemented on any microcontroller. Since the architecture does not require any additional hardware, it can be retroactively applied to deployed devices through a firmware update.
Using flexible ferrite sheet to design PCB inductor for 1MHz GaN converter taking into account thermal issues

Inductors are required in switching power converters for energy storage. They take a large volume compared with the other components of converters because of their small energy density. Nowadays, the operating frequency of power converters increases to MHz range because of the fast switching time of the new power GaN transistors. This allows to reduce the volume of the required inductors. In this paper, a design method for PCB inductor for GaN converter is proposed. The design approach is based on the optimization of the inductor volume taking into account the thermal issues of the ferrite sheets used to enhance the inductance value. The required inductance value for the GaN converter is chosen from a Losses-Volume curve. Also, an experimental method based on S-parameters is used to characterize the inductor saturation.
Low-Power Classification using FPGA—An Approach based on Cellular Automata, Neural Networks, and Hyperdimensional Computing

Field-Programmable Gate Arrays (FPGA) are hardware components that hold several desirable properties for wearable and Internet of Things (IoT) devices. They offer hardware implementations of algorithms using parallel computing, which can be used to increase battery life or achieve short response-times. Further, they are re-programmable and can be made small, power-efficient and inexpensive. In this paper we propose a classifier targeted specifically for implementation on FPGAs by using principles from hyperdimensional computing and cellular automata. The proposed algorithm is shown to perform on par with Naive Bayes for two benchmark datasets while also being robust to noise. It is also synthesized to a commercially available off-the-shelf FPGA reaching over 57.1 million classifications per second for a 3-class problem using 40 input features of 8 bits each. The results in this paper show that the proposed classifier could be a viable option for applications demanding low power-consumption, fast real-time responses, or a robustness against post-training noise.
THEASIS System for Early Detection of Wildfires in Greece: Preliminary Results from its Laboratory and Small Scale Tests

This paper presents the preliminary results of the THEASIS system which is under development for early detection of wildfires in Greece. Firstly, the proposed system in terms of conceptual design, the early fire detection methodology as well as the main software modules are presented. Laboratory and small-scale experiments made in the University of Patras campus as well as in the Strofylia forest are presented. The concluding remarks are promising towards the development of an efficient system for early detection of wildfires.
A Comparative Study Between Wired and Wireless Virtual Reality Setups

Virtual Reality (VR) through head-mounted displays (HMDs) can be delivered via multiple setups such as smartphones, standalone VR or VR Workstations. The VR Workstation setup delivers the best performance of them all; however, as a drawback up until recently, it required cables to power up the VR equipment. The introduction of wireless solutions for VR Workstations came to solve one of the disadvantages of this setup. However, the impact of the wireless solution versus the HMD cables was not yet properly investigated. In this paper, we study the impact of using a wired vs wireless HMD on Presence, Cybersickness, and Game Experience. We conducted a quasi-experimental between-subjects study with 68 participants assigned to the following three groups that were balanced regarding gender and sample size: Cable (participants used a wired HMD), Cable + Help (a researcher assisted the participants with the HMD cable during the experience to reduce the participants’ awareness of it), and Wireless (untethered HMD). Results showed no significant differences in presence and cybersickness. The game experience was significantly different, with Cable + Help performing better than Cable and Wireless. We conclude that sense of presence using wireless solutions is equated to wired HMD solutions.