Miracle-dz/UniFAR-FTUs huggingface dataset:
Real Time Implementation of Solar PV Energy System under Weak Grid Conditions

This paper presents a novel multi-magnitude integrator quadrature signal generator (MMI-QSG) based adaptive control to take care the grid abnormalities during the operation of voltage source converter (VSC) of a grid tied PV generating system (GTPGS). In proposed topology, the VSC performs as a PV array power DC-AC conversion supplying active power with the active power filter (APF) capabilities thus taking care the power quality (PQ) issues simultaneously; and a boost convertor is utilized here for implementation of P&O (Perturb and observe) based method for maximum power tracking (MPT) and MMI-QSG for the extraction of FC (Fundamental Component) of grid voltage and load current. The GTPGS is flexible as it continues to work satisfactorily under weak grid conditions and presence of nonlinear load connected at the PCC (Point of Common Coupling). The MMI consists of magnitude integrator, which has characteristic of accurate integration and retains a behavior of perfect FOS (First Order System) in terms of magnitude. The second order generalized integrator (SOGI) has simple and flexible structure, however, its dynamic response is affected by the damping gain and input and output parameters of the system. Accordingly, the design of the structure is complex. The MMI-QSG is configured to determine the fundamental component (FC) of current and voltage. The proposed MMI-QSG based control supports the PV array interfaced VSC to satisfy the grid voltage control requirements under several grid voltage distortion conditions. The effectiveness of the proposed structure of MMI-QSG method is evaluated through test results on the developed prototype in the laboratory.
Antenna Coupling and Out of Band Interference Effects on a High Precision GNSS Receiver

In this work we analyze the coupling between two patch antennas designed to be placed on a vehicle. One antenna transmits high power signals, while the other antenna is intended for receiving signals of global satellite navigation systems (GNSS). Although both antennas operate in different frequency bands, the out of band interference generated by the transmitting antenna can affect the performance of the GNSS receiver because of its high sensitivity. In addition, other intentional or unintentional out of band interferences coming from different sources represent a risk for the receiver. Simulation and measurement results show that due to the coupling between the antennas, the interference produced by the transmitter has a detrimental effect on the GNSS receiver. Several solutions to overcome this problem are presented and evaluated according to the requirements of the current application.
Recently Developed Reduced Switch Multilevel Inverter for Renewable Energy Integration and Drives Application: Topologies, Comprehensive Analysis and Comparative Evaluation

Recently, multilevel inverters (MLIs) have gained lots of interest in industry and academia, as they are changing into a viable technology for numerous applications, such as renewable power conversion system and drives. For these high power and high/medium voltage applications, MLIs are widely used as one of the advanced power converter topologies. To produce high-quality output without the need for a large number of switches, development of reduced switch MLI (RS MLI) topologies has been a major focus of current research. Therefore, this review paper focuses on a number of recently developed MLIs used in various applications. To assist with advanced current research in this field and in the selection of suitable inverter for various applications, significant understanding on these topologies is clearly summarized based on the three categories, i.e., symmetrical, asymmetrical, and modified topologies. This review paper also includes a comparison based on important performance parameters, detailed technical challenges, current focus, and future development trends. By a suitable combination of switches, the MLI produces a staircase output with low harmonic distortion. For a better understanding of the working principle, a single-phase RS MLI topology is experimentally illustrated for different level generation using both fundamental and high switching frequency techniques which will help the readers to gain the utmost knowledge for advance research.
A Random Iterative Algorithm for Community Detection

Research on community structure detection in complex networks has attracted a great deal of attention in recent years. In this paper we propose a random iterative algorithm to uncover meaningful communities. The algorithm starts with initial population creation. Each individual of the population is encoded with the community identifiers of the nodes in the network, so it is a potential solution of the community structure of the network considered. Nodes are randomly assigned into communities at the beginning of the algorithm. At each iteration some nodes are randomly selected, their community identifiers are reassigned according to the modularity function and the measure of information discrepancy based on the shortest path profiles of nodes in the network. In the end, a proper community structure can be detected by the identifiers encoded in the individual with the largest modularity. The algorithm does not need any prior knowledge about the number of communities and can give an appropriate number by maximizing the modularity function. The computational results of the method on real-world networks confirm its capability.
Improved Pulse-Width Modulation Technique for 11-level Series-Parallel Switched Hybrid Inverter

In this paper, improved Pulse-Width Modulation technique for an 11-level series-parallel switched Hybrid Inverter is presented. The series-parallel switching of the inverter bridge reduces the switching devices used for the same output voltage level as compared to traditional topologies. The hybrid modulation technique used is further separated as continuous and discontinuous. Results of both the modulation techniques are compared on the basis of their harmonic performance, which is measured in terms of total harmonic distortion and weighted total harmonic distortion. Discontinuous hybrid modulation is proved to be harmonically superior to its continuous counterpart. The validity of the proposed scheme is verified through simulated and experimental results.
Analysis on Temporal Dimension of Inputs for 3D Convolutional Neural Networks

3D ConvNets provide a dedicated spatiotemporal representation in order to incorporate motion patterns within video frames. However, compared to 2D convolutions, the 3D convolution kernels increase the number of parameters in the architecture and the floating point operations during inference time, which are of critical importance for real-time applications requiring faster runtime. In this paper, we show a sparse sampling and stacking strategy to span large time intervals for 3D ConvNet architectures that can attain multiple times less inference time by relinquishing little amount of classification accuracy. The proposed approach is validated on action and gesture recognition tasks using two recent video datasets: Jester and Something-Something datasets.
LC Tank Differential Inductor-Coupled Dual-Core 60 GHz Push-Push VCO in 45 nm RF-SOI CMOS Technology

This paper presents a 60 GHz dual-core push-push VCO in a 45 nm partially depleted (PD) RF Silicon-on-Insulator (SOI) CMOS technology. The cores are coupled inductively via differential inductors. The best measured phase noise at 1 MHz offset from a 63 GHz carrier is -$94.4 dBc/Hz. The wideband continuous frequency-tuning-range (FTR) is 16 %. The DC power dissipation is 76 mW including fundamental 30 GHz and second harmonic (H2) 60 GHz output buffers at 1 V power supply voltage. The measurement results of a reference single-core VCO design proves the relative phase noise improvement of the implemented core-coupling technique. The chip area excluding pads is 0.09 mm2.
Natural Genetics Adapted Control for an Autonomous Wind-Battery Based Microgrid

This paper proposes a natural genetics and evolution based control strategy for adjusting the gains of the proportional and integral (PI) controller meant for regulating the speed of the generator of an autonomous microgrid system using wind energy generation and a battery energy storage (BES). The variable wind speed operation leads to the dereliction of the fixed gain PI controller as it is sensitive to parameter variations and load disturbances. The genetic algorithm (GA) is implemented for adapting the gains of PI controller according to the changing operating conditions. It avoids the entrapments of the optimal solution in the local search space. The optimal solution is selected on the basis of best fit candidate. The synchronous generator (SG) reference speed is obtained using perturb and observe (P&O) maximum power extraction scheme. The flexible power flow control is obtained using a BES with a bidirectional converter. A prototype of the microgrid is developed in the laboratory and tested in accordance to the IEEE-519 standard under steady state and dynamic conditions including variable wind speeds and load removal.
Comparative Performance Evaluation of High-performance Data Transfer Tools

Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools’ performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively.
Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions

Multi-robot task allocation is one of the main problems to address in order to design a multi-robot system, very especially when robots form coalitions that must carry out tasks before a deadline. A lot of factors affect the performance of these systems and among them, this paper is focused on the physical interference effect, produced when two or more robots want to access the same point simultaneously. To our best knowledge, this paper presents the first formal description of multi-robot task allocation that includes a model of interference. Thanks to this description, the complexity of the allocation problem is analyzed. Moreover, the main contribution of this paper is to provide the conditions under which the optimal solution of the aforementioned allocation problem can be obtained solving an integer linear problem. The optimal results are compared to previous allocation algorithms already proposed by the first two authors of this paper and with a new method proposed in this paper. The results obtained show how the new task allocation algorithms reach up more than an 80% of the median of the optimal solution, outperforming previous auction algorithms with a huge reduction of the execution time.
Performance Evaluation of Conventional and Electric powertrain

To curb rising environmental pollution, energy issues and depletion of petroleum sources, electric vehicle (EV) is a cleaner, reliable and efficient transport system. Out of all electric motors employed in electric powertrain, permanent magnet synchronous motor (PMSM) is the most economical one because of its high power and torque density. This paper presents the evaluative study of driving performance of powertrain propelled by two different systems. The characteristics of conventional generic engine are analyzed to extract the essential requirements that are necessary to implement the required model of the electric powertrain using the appropriate properties of electric motor. The electric motor is controlled via three phase voltage source inverter (VSI) in which the space vector pulse width modulation (SVPWM) technique is employed for switching control of VSI. The electric powertrain modeling is necessary to understand the dynamics of vehicle under different running conditions.
Whole Genome Sequencing and Metabolomic Study of Cave Streptomyces Isolates ICC1 and ICC4

The terrestrial subsurface microbiome has gained considerable amount of interests in the recent years because of its rich potential resource for biomining novel genes coding for metabolites possessing antimicrobial activities. In our previous study, we identified two Streptomyces isolates, designated as ICC1 and ICC4, from the Iron Curtain Cave, Chilliwack, Canada that exhibited antagonistic activities against the multidrug resistant strains of Escherichia coli. In this study, the genomes of these two isolates were sequenced by Illumina MiSeq, assembled and annotated. The genes associated with secondary metabolite production were identified and annotated using the bioinformatics platforms antiSMASH and BAGEL. ICC1 and ICC4 were then cultivated and ICC1 metabolome characterized by UHPLC-ESI-HRMS. The Global Natural Products Social Molecular Networking was used to identify metabolites based on the MS/MS spectral data. ICC1 and ICC4 showed a high level of sequence identity with the terrestrial bacteria Streptomyces lavendulae; however, they possess a greater secondary metabolite potential as estimated by the total number of identified biosynthetic gene clusters (BGCs). In particular, ICC1 and ICC4 had a greater number of polyketide and non-ribosomal peptide BGCs. The most frequently detected BGCs were those predicted to generate terpenes, small and low complexity dipeptides and lipids. Spectral analysis clearly identified a number of diketopiperazine products through matched reference spectra for cyclo (Leu-Pro), cyclo (Pro-Val) and cyclo [(4-hydroxyPro)-Leu]. One of the terpenes gene clusters predicted by antiSMASH possesses a seven-gene pathway consistent with diazepinomicin biosynthesis. This molecule contains a very rare core structure and its BGC, to date, has only been identified from a single bacterial genome. The tetrapeptide siderophore coelichelin BGC was unambiguously identified in the genome, however, the metabolite could not be identified from the culture extracts. Two type III polyketides, 2′, 5′ – dimethoxyflavone and nordentatin, were identified from the UHPLC-HRMS data of the aqueous and n-butanolic fractions of Streptomyces sp. ICC1, respectively. A BGC likely encoding these metabolites was predicted in both genomes. The predicted similarities in molecule production and genome shared by these two strains could be an indicative of a cooperative mode of living in extreme habitats instead of a competitive one. This secondary metabolite potential may contribute to the fitness of ICC1 and ICC4 in the Iron Curtain Cave.
A design partitioning algorithm for Three Dimensional Integrated Circuits

Three Dimensional Integrated Circuits (3D ICs) are designed to have better performance and yield. Devices of a circuit are placed on different layers. Through-Silicon Vias (TSV) is used to connect a signal that crosses adjacent layers. In this paper we propose an algorithm to partition an integrated circuit with the objective to minimize the number of TSV and chip area. The advantages of lowering the number of TSV are not only reducing chip delay but also decreasing the area. Experimental results show that the program can partition a circuit with small number of TSVs in a short time. For the case of the largest test circuit with 85013 cells, after partitioning in to 5 layers, the number of TSV is only 142 and the average percentage of area overhead is only 0.1%. We achieve the best result, among all teams that participated in the 2009 IC/CAD contest in Taiwan.
Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications

Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited for applications such as computer vision, natural language processing, cybersecurity, communications, and so on. For the particular case of computer vision, several algorithms like object detection in real time videos have been proposed and they work well on Desktop GPUs and distributed computing platforms. However these algorithms are still heavy for mobile and embedded visual applications. The rapid spreading of smart portable devices and the emerging 5G network are introducing new smart multimedia applications in mobile environments. As a consequence, the possibility of implementing deep neural networks to mobile environments has attracted a lot of researchers. This paper presents emerging deep learning acceleration techniques that can enable the delivery of real time visual recognition into the hands of end users, anytime and anywhere.
Segmentation of Substantia Nigra for the Automated Characterization of Parkinson’s Disease

Segmentation of smaller brainstem nuclei like substantia nigra (SN) is one of the first stage in computer-aided diagnosis to investigate the Parkinson’s disease (PD) characteristics and progression. PD patients generally have smaller SN in comparison to healthy individuals. There are very few automated methods proposed for SN segmentation and most of them require quite a few number of reference images for increasing the accuracy. Therefore, we propose an improved algorithm for the segmentation of SN using level set method and dual-tree complex wavelet transform (DT-CWT). The proposed level set method uses the local image information and maintains the regularity of the level set function for accurate computation, thus precluding the expensive re-initialization process. DT-CWT is used to solve the problem of over-segmentation, also resulting in holes, and to smooth the jagged outputs generated by the level set method. The experimental results suggest that the proposed algorithm is able to segment SN and is closer to manual delineation.
Metabolome Analysis of Biosynthetic Mutants Reveals a Diversity of Metabolic Changes and Allows Identification of a Large Number of New Compounds in Arabidopsis1[W][OA]

Metabolomics is facing a major challenge: the lack of knowledge about metabolites present in a given biological system. Thus, large-scale discovery of metabolites is considered an essential step toward a better understanding of plant metabolism. We show here that the application of a metabolomics approach generating structural information for the analysis of Arabidopsis (Arabidopsis thaliana) mutants allows the efficient cataloging of metabolites. Fifty-six percent of the features that showed significant differences in abundance between seeds of wild-type, transparent testa4, and transparent testa5 plants could be annotated. Seventy-five compounds were structurally characterized, 21 of which could be identified. About 40 compounds had not been known from Arabidopsis before. Also, the high-resolution analysis revealed an unanticipated expansion of metabolic conversions upstream of biosynthetic blocks. Deficiency in chalcone synthase results in the increased seed-specific biosynthesis of a range of phenolic choline esters. Similarly, a lack of chalcone isomerase activity leads to the accumulation of various naringenin chalcone derivatives. Furthermore, our data provide insight into the connection between p-coumaroyl-coenzyme A-dependent pathways. Lack of flavonoid biosynthesis results in elevated synthesis not only of p-coumarate-derived choline esters but also of sinapate-derived metabolites. However, sinapoylcholine is not the only accumulating end product. Instead, we observed specific and sophisticated changes in the complex pattern of sinapate derivatives.
On The Selection of Relevant Features for Fear Emotion Detection from Speech

In this paper, we present a study to select the most relevant features, among a whole set of 27 pitch-based ones, in order to detect fear emotion from speech. To do so, four different relevance indexes have been used: Fisher discriminant ratio, probability divergence, scatter measure and ANOVA statistical test. Each feature has been tested separately. Then, the most relevant features according to each criterion have been picked. Their classification ability is then addressed. Features with highest classification that appear in all relevance indexes are retained. Thanks to this approach, the fear emotion recognition results using KNN reach 86.7% as an accuracy rate.
Opsin vs opsin: new materials for biotechnological applications

The need of new diagnostic methods satisfying, as an early detection, a low invasive procedure and a cost-efficient value, is orienting the technological research toward the use of bio-integrated devices, in particular bio-sensors. The set of know-why necessary to achieve this goal is wide, from biochemistry to electronics and is summarized in an emerging branch of electronics, called \textit{proteotronics}. Proteotronics is here here applied to state a comparative analysis of the electrical responses coming from type-1 and type-2 opsins. In particular, the procedure is used as an early investigation of a recently discovered family of opsins, the proteorhodopsins activated by blue light, BPRs. The results reveal some interesting and unexpected similarities between proteins of the two families, suggesting the global electrical response are not strictly linked to the class identity.
Unveiled reality: comment on d'Espagnat's note on measurement

According to d'Espagnat we must choose between nonlinear breaks in quantum state evolution and weak objectivity. In this comment it is shown that this choice is forced on us by an inconsistent pseudo-realistic interpretation of quantum states. A strongly objective one-world interpretation of linear quantum mechanics is presented. It is argued that the weak objectivity favored by d'Espagnat is, in fact, inconsistent with quantum mechanics.