Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimum Functional Splits for Optimizing Energy Consumption in V-RAN

A virtualized radio access network (V-RAN) is considered one of the key research points in the development of 5G and the interception of machine learning algorithms in the Telecom industry. Recent technological advancements in Network Function Virtualization (NFV) and Software Defined Radio (SDR) are the main blocks towards V-RAN that have enabled the virtualization of dual-site processing instead of all BBU processing as in the traditional RAN. As a result, several types of research discussed the trade-off between power and bandwidth consumption in V-RAN. Processing at remote locations instead of BBU reduces mid-haul bandwidth at the expense of power consumption and vice versa. As a result, the integration of NFV and SDR in V-RAN facilitates dynamic power consumption and processing whenever relaxation is needed. This paper studies several functional splits proposed by ETSI in the NFV of the dual-site network. In addition, network performance is analyzed in terms of data rate, power consumption, and energy efficiency (EE) optimization. Furthermore, the combined optimization of power consumption and mid-haul bandwidth are investigated, and optimal operating parameters are recommended for similar network operators. Thus, regulators/operators can adjust their networks with these parameters to achieve the best performance. Additionally, the UEs switching scheme is introduced to sleep some RRHs in low-density traffic to lessen power consumption.
A New Semi-Supervised Deep Learning Approach for Intelligent Defects Recognition

Intelligent defect recognition (IDR) is one of the important technologies in production. Deep learning (DL) has drawn more and more attentions in IDR. Whereas, DL methods usually need large labelled training datasets, while the unlabeled is idle and not considered yet. In some cases, the requirement is difficult to satisfy. This is because labelling large datasets are costly, and the defect recognition might be delayed until getting enough labelled samples. To overcome this limitation, a semi-supervised DL approach for defect recognition, which uses the unlabeled samples to improve the accuracy, is introduced in this paper. This method uses a convolutional autoencoder to extract the common feature from both labelled and unlabeled samples, and only a few samples are required to finetune the network. The experimental results suggest that the proposed method achieves competitive results under limited labelled samples, and the accuracy outperforms the other approachs. Furthermore, the noise analysis also suggest this method performs robust for noisey samples.
Triple-Phase Shift Modulation and Tuning Technique to Improve the Efficiency of the Dual Active Bridge Converter

This paper proposes a triple phase shift modulation with an optimization technique that aims at maximizing the efficiency of the Dual Active Bridge (DAB) converter. The DAB converter finds application in smart dc power systems, including the electrification of the powertrain of vehicles and vehicles-togrid applications, where loss minimization is crucial. In this paper, favorable modulation parameters are found first, by aiming at minimum rms currents and zero voltage switching (ZVS) for the converter switches. Then, on the basis of the closed-form analytical description of the converter behavior over the determined favorable modulation patterns, efficiency improvement trajectories in the modulation planes are identified. It is shown that such trajectories lead to optimal efficiency operation, which can be exploited to implement fast closedloop efficiency optimization methods. The reported results are verified experimentally on a 1.5 kW prototype, showing that the proposed approach allows to achieve the best-efficiency operation of the converter.
Promising SPR Biosensors Applying 2D Materials with α-SnSe Allotrope for Sensing Applications

Two-dimensional (2D) materials, which are confined in one direction, have attracted great interest in surface plasmon resonance (SPR) biosensors owing to its excellent properties like high mobility, conductivity, and mechanical strength. Monolayers of alpha Tin Selenide (α-SnSe) having analogous structure as graphene and phosphorene, additionally exhibit amazing properties of optoelectronic that have taken a lot of attention as a 2D-material beyond former 2D material family members. In this paper, we present simple prism- based SPR biosensors with hybrid structures of Au-SnSe- graphene, Au-SnSe-Mos2, and Au-SnSe- WS2. The performance parameters of the raised biosensors are numerically studied in terms of sensitivity(S), quality factor (Q.F.) and detection accuracy (D.A.) through angular interrogation approach. Results reveal that the determined sensitivity is improved to 101.42 °/RIU, whereas D.A. and Q.F. are decreased a bit than the conventional SPR biosensors. Therefore, the proposed biosensors raise a new platform for its higher sensitive characteristics in many sensing applications.
Wireless Secure Signal Transmission for Distributed Intelligent Surface-Aided Millimeter Wave Systems

The optimization of the secrecy rate of the distributed intelligent reflecting surfaces (IRSs)-assisted millimeter wave (mmWave) communication system is studied in this paper. In this system, the users are served by multiple IRSs, and by adjusting the switch state of each IRS and optimizing the phase shift of the IRS to maximize the secrecy rate of the system. The problem is described as a joint IRS control, transmit beamforming design problem. Our objective is to maximize the secrecy rate of the distributed IRSs-aided system under the transmit power constraint of the access point (AP). Since the problem is non-convex, one alternating optimization algorithm is proposed for solving this problem. We use the successive convex approximation (SCA) algorithm to solve it, and the solution that is obtained by the SCA algorithm guarantees to converge to a stable point that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Moreover, by using the dual method, we obtain the IRS switch. For the IRS phase shifts optimization problem, a low-complexity majorization-minimization (MM) algorithm is used to solve this problem. Numerical results show that the proposed scheme can achieve higher secrecy rate compared with the single IRS-aided scheme.
A new VR paradigm to measure mental rotation

In most of the studies focusing on mental rotation, researchers have used classic mental rotation paradigms. In a few more recent studies, new paradigms have emerged, but the effect sizes were small, sex-based performance measurements showed mixed results, and most paradigms were not standardized. In this paper, I describe the paradigm that I am willing to standardize and use on a large user base to investigate whether VR environments have any effect on the process and the performance measures of mental rotation. I expect subjects to have better performances when discriminating between stimuli in the presented VR environment.
On the Generation of Long Binary Sequences With Record-Breaking PSL Values

Binary sequences are widely used in various practical fields, such as telecommunications, radar technology, navigation, cryptography, measurement sciences, biology or industry. In this letter, a method to generate long binary sequences (LBS) with low peak sidelobe level (PSL) value is proposed. Having an LBS with length <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>, both the time and memory complexities of the proposed algorithm are <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(n)$</tex-math></inline-formula>. During our experiments, we repeatedly reach better PSL values than the currently known state of art constructions, such as Legendre sequences, with or without rotations, Rudin-Shapiro sequences or m-sequences, with or without rotations, by always reaching a record-breaking PSL values strictly less than <inline-formula><tex-math notation="LaTeX">$\sqrt{n}$</tex-math></inline-formula>. Furthermore, the efficiency and simplicity of the proposed method are particularly beneficial to the lightweightness of the implementation, which allowed us to reach record-breaking PSL values for less than a second.
Design and Simulation Study of a New Flared-U Shaped Springs Based MEMS Piezoelectric Vibration Energy Harvester

The paper presents a MEMS energy harvester based on new two symmetrical Flared-U shaped unimorph spring structures which is capable to harvest energy from low frequency-based vibration. It comprises a silicon substrate beam integrated with Lead Zirconate Titanate (PZT-5H) piezoelectric material and a silicon proof mass. This device is designed and optimized utilizing COMSOL multiphysics 5.4 (licensed version). First natural frequency of the 28.6 Hz is observed by COMSOL simulation. Finite element method (FEM) simulator COMSOL Multiphysics based simulations shows that the proposed structure is able to extract 10.738 nW power from low resonant frequency of 28.6 Hz and low acceleration of 0.07 g over $120\ \mathrm{k}{\Omega}$ load resistance.
Spatially Variant Apodization for Grating and Sidelobe Suppression in Near-Range MIMO Array Imaging

In this article, a grating and sidelobe suppression method for near-range multiple-input multiple-output (MIMO) array imaging is proposed, which achieves grating and sidelobe suppression by reducing the sidelobes of the transmitting and receiving array. The method first utilizes the generalized matched filtering (GMF) for imaging to obtain the imaging results which are separable along the transmitting and receiving array. Then, an image domain phase compensation term is derived to eliminate the spatially variant characteristic of the wavenumber spectrum, and a spatially variant weighting scheme with the adaptive weighting range is proposed to reduce the sidelobes of the transmitting and receiving array, respectively. The proposed method can effectively suppress the grating and sidelobes while maintaining the width of the mainlobe. Both simulated and experimental data verify its effectiveness.
Simultaneous Switching Loss Reduction and Neutral-Point Voltage Balance Scheme for Single-Phase Three-Level T-Type Inverter

Single-phase three-level T-type inverter (3LT$^2$I) has revealed superior performances in many applications. However, it inevitably faces the challenge of high switching loss and imbalanced neutral-point voltage with conventional carrier based pulsewidth modulation (CBPWM). To address these issues, an improved CBPWM strategy with maximum offset voltage injection is proposed to reduce the switching loss and balance the neutral voltage simultaneously. Neutral-point voltage balance is maintained by selecting P-type or N-type switching sequence, which depends on the sign of offset voltage. Also, switching loss is mitigated by reducing the number of total switching commutation. First, the restriction of injected offset voltage is investigated and when the offset voltage is maximum, the switching commutation is reduced. Then, to further reduce the number of switching commutation, a new set of carrier signals are proposed to realize zero switching commutation between P-type and N-type switching sequences. After the analysis, the proposed modulation method also has better common-mode voltage restriction and faster response of neutral-point voltage balance. Simulation and experiment have been conducted to verify the theoretical analysis.
Pothole and Speed Breaker Detection Using Smartphone Cameras and Convolutional Neural Networks

Poor road conditions are one of the major causes for road accidents. Developing countries in particular are witnessing increased accident rates due to these poor road conditions. Potholes, deep ridges, missing pitches, improper speed breakers, poorly constructed manhole covers and slabs all combine to greatly increase the probability of serious accidents thus transforming roads into obstacle courses. In this study we have developed a model to detect unwanted potholes, deep ridges and speed breakers using computer vision and machine learning tools. We have developed a customized dataset (called Bumpy) that we use to train our machine learning algorithms. In this paper we propose a method where we use the Tensorflow pre-trained model to detect the potholes, deep ridges and speed breakers. Our experimental results demonstrate high accuracy although there are many obstacles on the road.
Structural, optical and magnetic properties of B8, FeB8, CoB8, NiB8 nanoclusters: A DFT study

A first principle investigation is made on the structural, optical and magnetic properties of the pristine and several other transition metal (TM), Fe, Co, Ni adsorbed B8 structures. The adsorption of TM atoms alters the bond length of the B-B bonds in B8 structure. Furthermore, due to the adsorption, no significant deformation and the imaginary frequency in the IR spectroscopy was observed. From the analysis of thermodynamical parameters, it was observed that the TM atoms adsorption process on B8 was spontaneous, exothermic and ordered. Among our investigated structures, the most stable structure was NiB8 structure with an adsorption energy of -8.12 eV. The order of stability of adsorbed structures further was obtained as NiB8>FeB8>CoB8. TM adsorption made structures to absorb more energetic lights and widen the optical bandgap. Finally, only CoB8 structure exhibits magnetic properties and behave as a ferromagnetic material with a total and spin magnetic moment of 6.33 and 0.11 respectively.
Harmonics Reduction of Adjustable Speed Drive Using Transistor Clamped H-Bridge Inverter Based DVR With Enhanced Capacitor Voltage Balancing

Harmonics generated by nonlinear critical loads are major anxiety for the industries. A control technique for dynamic voltage restorer (DVR) is described to limit voltage harmonics introduced by adjustable speed drive (ASD) in the utility. Moreover, this control technique maintains constant dc-link voltage of ASD during any disturbances occurring at point of common coupling. Mathematical description to limit ASD voltage harmonics and to generate reference dc-link voltage of ASD is illustrated in the dq0 frame. Transistor clamped H-bridge (TCHB) multilevel inverter (MLI) is used as a voltage source inverter of DVR. An enhanced capacitor voltage balancing (ECVB) method for TCHB MLI for the fast dynamic performance of DVR is also discussed. The proposed control technique of DVR to limit ASD voltage harmonics is simulated using MATLAB software. Furthermore, a simulation for DVR protecting ASD during source harmonics, unbalance, sag, and swell is carried out and results of ASD performances are demonstrated. The working of the proposed controller of DVR with ECVB strategy based TCHB is reverified using experimental results. ASD voltage harmonics mitigation and fast dynamic response of ECVB based TCHB are the highlights of this article. The performance of the proposed strategy shows satisfactory results with a less complex control system.
Active Power Factor Correction for Automation of Electrical Vehicle Charging Station Using myRIO

This paper proposes a control strategy for an Autonomous electric vehicle charging station using an Active power factor correction technique. The charging stations are complementary for the electric vehicles. Charging stations supply the DC voltage for electrical vehicles. A full-bridge rectifier with a boost converter accompanies voltage stability problems due to switching devices. To mitigate these effects an algorithm is developed which has two control loops in a cascade manner constituting the Active power factor correction controller (APFC). The parameters of PI controllers are obtained by tuning through frequency response estimation using the Linear Analysis tool in the MATLAB Simulink. Tuned parameters of PI controllers have a substantive role in enhancing the performance of the system by ameliorating the power factor to unity and reducing THD to 2.67% (Total Harmonic distortion) and steady-state error of ±0.5%. The control program is developed in LabViewsoftware and is deployed in NI myRIO which generates PWM switching pulses for the MOSFET switch in the Boost converter. Automation of the entire charging station is possible using the myRIO controller by applying IoT technique. The algorithm developed for the power factor correction can be used to generate accurate payment bills based on the reactive power consumed for charging the vehicle. It helps in imposing the power factor tariff to consumers. The fault detection system for the charging station can also be developed using myRIO, voltage sensor and current sensor.
Prediction of Epileptic Seizures using Support Vector Machine and Regularization

Epilepsy is a neurological disorder that causes abnormal behavior and recurrent seizures due to unusual brain activity. This study has attempted to predict seizures in epileptic patients through the process of feature extraction from EEG signals during preictal/ictal and interictal periods, classification and regularization. EEG signals from various parts of the brain from 10 epileptic patients are considered. Fast Fourier Transform (FFT) is used to determine the three features-the phase angle, the amplitude and the power spectral density of the signals. To classify the signals, these features are then used along with Support Vector Machine (SVM) as the classifier. Furthermore, regularization is used to make better predictions i.e. increase prediction accuracy and decrease the rate of false alarm. Finally, the proposed approach is tested on CHB-MIT Scalp EEG data set and it is able to predict epileptic seizures 25 minutes on average before the onset of the seizure with 100% accuracy and a low false-alarm rate of 0.46 per hour. This study intends to contribute to the development of better and advanced seizure predicting devices in the medical field.
Resource Prediction Using Hybrid Double Seasonal Decomposition in Fog Integrated Cloud

Time series prediction has many real-world applications such as weather forecasting, electric utility forecasting, load forecasting in the cloud and many more applications. In this paper, we propose a hybrid model to predict the workload in fog integrated cloud architecture. The prediction of workload is divided into two domains: the long term and the other short term. The underlying system models and time-series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. The underlying motivation for using CTREE and CART is the ability to accurately forecast time series data when the process requests of the types are nonlinear, non-stationary and not well-defined a-priori, however, ARIMA methodology also we have used to compare the results and used to forecast the trend part. In this paper, we propose different sets of prediction models, including CART and CTREE and compare the result with other statistical-based predictions. We have used the Wikimedia data set for the prediction model and compared it with other states of the art. CTREE and CART have also been proven to outperform other linear techniques including AR, MA, and ARIMA prediction techniques. Simulation results in our proposed model showed good accuracy, leading to efficiency and dynamic resource utilization with good quality of service(QoS).
A Database Implementation for the Study of Crystalline Silicon PV Modules Behavior

The work proposed here aims at providing scientists and decision-makers with a mean which will help in the performance modeling of PV modules. Based on one of the existing models of solar cells, we developed a behavioral database enriched by the information that can be gathered from the manufacturers' datasheets of the modules. By considering the extraction of the 5 parameters of the module as an optimization non-constraint problem at Standard Test Conditions (STC), analytical expressions without any simplification, derived from the main current-voltage equation are used. By choosing a wise initial guess value we evaluated those 5 unknows more accurately. Datasheets of 10 different types of solar modules that are installed in photovoltaic plants in Senegal were considered. Hence the methodology adopted and applied on them in the simulation process allowed us to replicate first their current-voltage and power-voltage characteristics in STC, then in changing environmental conditions. In the end, this work focused on the prediction of a twelfth part of the output power produced on the DC side before an Inverter Transformer Station (ITS) of one photovoltaic plant. The measurement values compared with the simulation results showed a good match for the same day over two years.
Development of Blocked Route Signboard Detection Algorithm for Autonomous Mobile Robot Combining Omnidirectional Camera and 3D-LiDAR

This paper describes the development of an algorithm for detecting blocked route signboard in an autonomous mobile robot using a combination of omnidirectional camera and 3D-LiDAR. A signboard recognition technique is used to detect a blocked route signboard. An omnidirectional camera and 3D-LiDAR are used for stable signboard detection. The omnidirectional camera is mainly used for stable color detection regardless of outdoor sunlight position, while the 3D-LiDAR is used for the retroreflective painted areas used in the signboard and for detecting the shape of the known signboard dimension by point cloud extraction. The validity of the proposed algorithm was confirmed by detecting blocked route signboard with the same dimensions as those defined in the Tsukuba Challenge rule.
Three Stage Class AB Power Amplifier in 90 nm CMOS Process for IoT Applications

A highly efficient three-stage 2.8 GHz fully-integrated Si power amplifier (PA) using 90 nm CMOS process for IoT applications is reported in this work. The designed PA exhibits a simulated output power of 20.33 dBm, gain of 20.27 dB and 32.74% power added efficiency (PAE). This fully integrated PA achieves feasible frequency performance for critical IoT range. This paper addresses the potential for the incorporation of not only linear but also highly efficient PA architectures at GHz frequencies into nanometer CMOS technologies. The performances of the proposed design are then compared with other referred power amplifier circuits. It can be noticed that this proposed design exhibits outstanding performance incase of PAE and power gain. Three-stage PA is proposed targeting the 802.11n WLAN protocol that operates in the 2.8 GHz frequency band with strict linearity specifications.
Dynamic Multi-agent OpenADR Virtual Nodes for Distributed Demand Response Schemes

Demand Response (DR) has been described as one of the few solutions available for accessing the untapped energy potential of small/medium customers and especially of prosumers, towards supporting grid and DR reliability to a smart distribution grid. Investing on that notion, this manuscript employs virtual power plant (VPP) principles to deliver a novel, distributed, multi-agent virtual node scheme that aggregates consumers and prosumers and handles automatically OpenADR compliant DR requests. The introduced virtual node can act both as a virtual end node (VEN) receiving signals from a higher level (i.e., aggregator or utility) and/or as a virtual top node (VTN), dispatching optimal DR requests to customers. The proposed framework increases the DR results by not only optimally exploiting aggregated flexibility, but also through a dynamic bi-directional DR matchmaking process that can either recover monitored deviations within the virtual node (intra), or in-between nodes (inter) in real-time operation. The proposed design aims to elevate DR effectiveness, while enabling the participation of small to medium customers to energy markets.