Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Modification in Single-Tooth Winding Double-Stator Switched Reluctance Motor for Torque Ripple Mitigation

Double-stator switched reluctance motors (DSSRMs) with single-tooth winding topology possesses high torque density when compared to conventional switched reluctance motors (SRMs). However, their inherent high torque ripple is still an issue for industrial applications. In SRMs, the torque shared by the outgoing phase reduces significantly in the commutation region. However, at the same time, the incoming phase does not achieve sufficient torque generation. This results in a high torque ripple in this region. In this paper, several design procedures are discussed to improve the performance of the radial flux DSSRM with single-tooth winding topology. Firstly, the pole arc equations of stator pole and rotor segments for the higher difference between aligned and unaligned inductance are derived for high output torque and based on this, the selection of the number of stator slots/rotor segments is discussed. Furthermore, the influence of winding polarities on the core loss and output torque of DSSRM is discussed. Finally, the design modification in rotor structure is proposed with an angular shift in the alternate rotor segments in the direction of rotation to mitigate the torque ripple. To investigate the effectiveness of the proposed design modification, a finite-element model of a 3-phase 12/10/12 pole radial flux DSSRM is developed in ANSYS/MAXWELL software, and simulation results are presented. It is observed that a 40% reduction in the torque ripple is achieved in the case of the proposed motor. The proposed design modification improves the torque generating capability of the incoming phase in the commutation region, which reduces the torque dip in this region and subsequently reduces the torque ripple.
Risk Assessment of Drinking Water Source Based on High Spatial Resolution Remote Sensing

This study combines object-based image analysis (OBIA) and deep learning (DL) techniques for automatic risk source extraction and risk assessment in drinking water sources using high-spatial-resolution (HSR) imagery. HSR images are first segmented to obtain the object primitives (OP), and the DL training samples are collected based on the OPs. U-Net semantic segmentation and object-based classification methods are then used to extract the risk source targets. Analytic hierarchy process (AHP) method is used to obtain the weights of risk sources. The risk assessment model is then established which comprehensively evaluates the safety level of drinking water source environment. Experiments at the Jiajiang water source of Nanjing city are carried out based on china's GaoFen-2 (GF-2) imagery, in which the risk sources such as farmlands, woodlands, grasslands, roads, and industrial and residential buildings are extracted and evaluated of their risk influences by the comprehensive risk assessment index (RAI). It shows that on December 12, 2018, the environment of Jiajiang drinking water source is at a safety level of “high”.
Gain Enhancement of Curvy Spoof Surface Plasmon Polariton Antenna With Controllable Bandwidth for 5G Applications

A method for improving gain in spoof surface plasmon polaritons (SSPPs) antenna including guiding and radiating parts is proposed. This method is based on using a curvy tapering area instead of linear one to matched the antenna to the air. The obtained results show that realized gain can be improved approximately 3 dBi by this method. Furthermore, by using corrugated unit cells and stubs an undesired frequency band can be rejected. Since the antenna works at 27.2-28.2 GHz, it can be used for 5G applications.
Promoting Network Automation for Heterogeneous Networks Collaboration

The Internet has made a significant success, which is on the basis of TCP/IP stacks. However, due to the dramatic development of the Internet of Things and 5G, giving rise to the continuous expansion of the network scale and the emergence of new applications, it becomes more and more complicated to manage the Internet. Specifically, the best-effort model and the device-centric working manner have become the inhibitors to meet the demands of the intelligent and coordinated transmission under heterogeneous networks in the future. In this paper, we proposed a novel Internet architecture named Smart Integration Identifier Networking (SINET-I) after comprehensively summarizing the related researches of the future Internet. SINET-I enhanced the ability of network automation and realized heterogeneous networks collaboration via introducing intent scheme and making full use of machine learning technologies. The experiment results show that SINET-I performs well in coordinated transmission across different heterogeneous protocols scenario and the available bandwidth of multi-paths is more than 2 times of that of single-path.
A Modified D-Linknet with Transfer Learning for Road Extraction from High-Resolution Remote Sensing

Road extraction, which aims to label remote images with a specific road detection, is a fundamental task for understanding remote sensing imagery. Deep learning has strong characteristic learning ability, for example, the state-of-the-art D-Linknet is an effective way to capture the road information. However, existing regularization methods either do not match the performance for large batches, or still exhibit degradation in performance for smaller batches. Besides, the roads in different areas have various characteristics and lack a good transfer. To remedy these issues, we proposed a novel road extraction network which integrated the filter response normalization (FRN) layer with D-Linknet (FND-Linknet). The FRN layer is effective and robust for road extraction task, and can eliminate the dependency on other batch samples. In addition, the multisource road dataset is collected and annotated to improve features transfer. Experimental results on three datasets verify that the proposed FND-Linknet framework outperforms the state-of-the-art methods both in accuracy and connectivity.
RGB-based Android Malware Detection and Classification Using Convolutional Neural Network

With the proliferation of handheld devices and due to numerous routes for malware creation, the detection of new sophisticated malware becomes a real challenge. In contrary to conventional machine learning approaches, which require feature engineering and source code analysis, we propose here to use a new RGB-based imaging technique for android malware detection and classification. To combat malware threats, our system is built on a static analysis of the android application packaging (APK) file. First, we perform a novel transformation of the APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping. Second, we train a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The experimental results on the AndroZoo [1] dataset show that our system can classify both legacy and new malware applications with a high accuracy of 99.37%, a False Negative Rate (FNR) of 0.8%, and a False Positive Rate (FPR) of 0.39%.
3D Coordination Polymer Incorporating Discrete Molecular Octahedra

Copper(II) oxalate coordination polymer [{Cu4(C2O4)4(L)4}3 · {Cu3(C2O4)3(L)6}2 · 3L · 25H2O]n (L = 3,3′,5,5′-tetramethyl-4,4′-bipyrazole) reveals a structure that is related to the Pt3O4 net topology. The 3D linkage is sustained with copper-oxalate squares and copper-bipyrazole triangles sharing vertices. The framework supports giant icosahedral cages and entraps discrete molecular octahedra formed by two molecular complexes Cu3(C2O4)3(L)6 associated by means of NH---N hydrogen bonding. The coexistence of the discrete and 3D portions formed by the same components suggests self-templation as a key feature of the system. Simpler copper oxalate compounds [Cu(C2O4)(L)2(H2O)] · CH3OH · 3.75H2O and [Cu2(C2O4)2(L)5] · L · 11H2O are concomitant products of the reaction mixture and they exist in the form of molecular mono- and binuclear complexes. Ein dreidimensionales Koordinationspolymer mit eingeschlossenen diskreten, molekularen OktaedernInhaltsubersicht. Im aus Kupfer(II)-oxalat und Tetramethylbipyrazol gebildeten Koordinationspolymer [{Cu4(C2O4)4(L)4}3 · {Cu3(C2O4)3(L)6}2 · 3L · 25H2O]n (L = 3,3′,5,5′-tetramethyl-4,4′-bipyrazole) wird eine Struktur verwirklicht, deren Topologie dem netzartigen Aufbau in Pt3O4 entspricht. Die dreidimensionale Verknupfung wird durch quadratische Kupfer-Oxalat- und trigonale Kupfer-Bipyrazol-Einheiten mit gemeinsamen Ecken aufgebaut. Das dadurch aufgebaute Gerust bildet riesige ikosaedrische Hohlraume, in die diskrete molekulare Oktaeder eingeschlossen sind, welche wiederum aus zwei molekularen, uber NH---N-Wasserstoffbrucken gebundenen Cu3(C2O4)3(L)6-Einheiten bestehen. Die Koexistenz von einerseits diskreten und andererseits dreidimensional polymeren Teilen, die aus den selben Bausteinen aufgebaut sind, deutet auf einen “Eigentemplat-Effekt” hin, der fur die Bildung dieser Struktur masgeblich ist. Die einfacheren Kupferoxalat-Verbindungen [Cu(C2O4)(L)2(H2O)] · CH3OH · 3,75H2O und [Cu2(C2O4)2(L)5] · L · 11H2O sind Nebenprodukte der Reaktion und bilden molekulare ein- bzw. zweikernige Komplexe.
High Resolution Spatial Mapping of Soil Nutrients Using K - Nearest Neighbor Based CNN Approach

One of the major components of precision agriculture is the precision fertilization. The principle of precision fertilization is to adjust the fertilizer input according to the properties of soils at each location for the least waste and the highest production of yield. The paper presents a feasible approach for developing high resolution spatial map of soil nutrients based on k-nearest neighbor Convolutional Neural Network (CNN) model. Our CNN-based approach is appropriate for mapping soil nutrients because of its ability to predict soil nutrient value at less computational expense and real-time processing capabilities unlike traditional geostatistical methods. Using the field sampling database from Govt. of India, (Soil Health Card-SHC), soil nutrients (N, P, K & OC) map is generated at field scale (< ha). The nutrients distribution is able to capture the spatial variability with high accuracy. This research is a methodological contribution to precision agriculture and lays the ground for precise application of fertilizers.
Spatial Attention-based Efficiently Features Fusion Network for 3D-MR Brain Tumor Segmentation

Accurate segmentation of brain tumors is critical for gliomas diagnosis and treatment planning. A large number of studies have shown that 3D convolutional neural networks (CNN) can use 3D convolution to capture the correlation between adjacent slices of brain magnetic resonance images (MRI). However, these methods often failed in the insufficient fusion of different layer features which have resolution and semantic difference and resulted in model information confusion. To this end, this paper presents a 3D spatial attention based efficient feature fusion network (SA2FNet) to achieve more accurate dense volumetric segmentation. Spatial attention mechanism methods are introduced in this paper to solve the problem of missing segmentation details caused by model information confusion. Then the deep supervision is employed to prevent the gradient explosion/disappearance problem of CNN and speed up the convergence, which significantly improves the performance. The experiment results indicate the SA2FNet has better performance in segmenting the brain tumor detail.
SE-HRNet: A Deep High-Resolution Network with Attention for Remote Sensing Scene Classification

Remote sensing scene classification is a challenging task due to the complex high-resolution textures and similarity within and variations between classes. The high-resolution network (HRN et) is introduced into this application considering its capability of high-resolution and multi-scale semantic representations. Moreover, squeeze-and-excitation (SE) attention mechanism is employed to enhance and suppress features based on their interdependence between channels. By fusing SE block into HRNet, the proposed SE-HRNet can better distinguish scene categories with enriched features. Experimental results on the NWPU-RESIS45 and AID data sets validate the state-of-the-art performance of the proposed SE-HRNet.
Enhancement in Dual Polarization Phased Array Antenna Performance and Calibration Techniques for Weather Radar Applications

Phased array antennas are being increasingly used in weather radars. However, these antennas have the inherent property that the horizontal and vertical waves are not orthogonal in all azimuth and elevation angles range. Thus, designing phased array antennas with very low cross-polarization is of significant interest to radar scientists and engineers. This work aims at implementing a dual linearly polarized aperture-coupled antenna design and layout so as to reduce this undesired cross-polarization while maintaining the required beamwidth, radiation pattern and scan range range. Here, an antenna element and the corresponding subarray of the antenna elements are implemented, which result in significantly low cross-polarization. This sub-array will be used for full array design.
Multi-area Joint Scheduling Model in Consideration of Flexible Loads

With the rapid development of the ultra-high voltage AC/DC channel, power grid of China shows obvious characteristics of power balance integration in the whole grid. Therefore, it is necessary to optimize the generation-transmission-utilization plan of the whole grid at large. To this end, for the uncertainty in practical response of the flexible loads, a multi-area joint scheduling in consideration of flexible loads is proposed. In the method, according to the load response characteristics, flexible loads are divided into transferable loads, shiftable loads and reducible loads; it is defined that the transferable loads guide response through time-of-use power price to reduce the peak valley difference of the load. Shiftable loads and reducible loads participate in the scheduling to optimize and improve the consumption of regional new energy. In the end, based on the power characteristics of AC/DC tie lines, a joint scheduling model with complementary sources in multi-area is established to reduce the overall operation cost of power grid and improve the clean energy accommodation. The model is applied to a practical example, and the analysis results of the example verify the feasibility of the proposed method.
Framework Development for 3D Human Shape Reconstruction from Security Camera

This research investigates a framework for 3D Human Shape Reconstruction from RGB Images captured by Security Camera or Internet Protocol Camera (IP Camera). At first, the study gives an overview of current technologies to create a Human Body 3D Model. The proposed method is then described, including two main stages: (1) Preprocess Stage: calibrating camera and coordinate system; (2) Process Stage: collecting and optimizing data. Advantages and disadvantages of the proposed method were analyzed experimentally and compared with previous publications. Research results demonstrates that our framework for 3D human reconstruction could provide a positive outcome, which can be applied in a wide array of industrial sectors.
An Adaptive Superframe Change in a Wireless Vital Sensor Network for a Group of Outdoor Exercisers

To collect vital data from a group of outdoor exercisers in real-time and reliably, we propose a wireless multihop network system based on a flooding/time division multiple access (TDMA) protocol with an extended superframe structure. For wireless vital sensor nodes (VSNs) put to the bodies of exercisers, the system first randomly and temporarily divides a whole group of VSNs into several subgroups and assigns a distinct superframe to each subgroup. Then, when VSNs move around in a ground, the system adaptively changes subgroup members so as to make them uniformly and widely spread in the ground by using the location information on all the VSNs.In this paper, we evaluate the performance of the system by an experiment. We conduct the experiment in an outdoor ground of 60m×90m for 45 min, involving 50 subjects, where we put VSNs to 18 subjects out of them. We show in the performance evaluation that the system can collect vital data from the 18 subjects even when all the subjects perform a variety of exercises, once in 2 sec in average, for 45 min regularly, and in 94.9 % data collection rate reliably.
CNN-based Leaf Image Classification for Bangladeshi Medicinal Plant Recognition

Classifying plant species has taken much attention in the research area to help people recognizing plants easily. In recent years, the convolutional neural networks (CNN) have achieved tremendous computer vision results, especially in image classification. Usually, humans find it difficult to recognize proper medicinal plants. It requires the intuition of an expert botanist, which is a time consuming manual task. In this research, we proposed an automated system for the medicinal plant classification, which will help people identify useful plant species quickly. A new dataset of 10 medicinal plants of Bangladesh is introduced, collected from different regions across the country, and some state-of-the images collected from different sources. After that, a three-layer convolutional neural network is employed to extract the high-level features for the classification trained with the data augmentation technique. The training process was done on 34123 images, and the experimental result on another 3570 images proved that this method is quite feasible and effective, which gave by a 71.3% accuracy rate.
Semi-Supervised Deep Learning Seismic Impedance Inversion Using Generative Adversarial Networks

Deep learning methods have been successfully applied to solve seismic inversion problems in recent years. Though deep learning inversion can obtain results with much higher resolution compared to geophysical inversion, its performance often suffers from the limitation of the well logs which are main source of labels in training data. To overcome this problem, we propose a semi-supervised deep learning workflow based on Generative Adversarial Network (GAN) for seismic impedance inversion. The workflow contains three networks: a generator, a discriminator, and a forward model. The training of the generator and discriminator are guided by well logs and constrained by unlabeled data via the forward model. Test on Marmousi2 model shows that, by making use of both labeled and unlabeled data, the proposed method predicts impedance with better consistency than conventional deep learning inversion.
H2 production photosensitized by aqueous semiconductor dispersions

CdS powder has been shown to photosensitize the reduction of H2O by sacrificial electron donors (cysteine and EDTA). Platinized CdS was found to be 20 times more efficient for H2 production. H2 was also evolved efficiently from aerobic solutions, although concomitant reduction of O2 was observed. The formal quantum efficiency (f.q.e.) for H2 production was 0.04 mol ein–1 at 436 nm and yields of H2 correlated well with the known spectrum of CdS. Other platinized semiconductor powders (Si, CdSe and CuPc) were found to evolve H2 when illuminated in the presence of EDTA, but these systems were less efficient (f.q.e. < 10–3 mol ein–1).
Motif Difference Field: An Effective Image-based Time Series Classification and Applications in Machine Malfunction Detection

Time series motifs are widely used in time series analysis. The time series motifs are used for the discovery of order structures and patterns in time series. The motif difference field (MDF) is proposed based on time series motifs. Compared to the other image representations of time series such as Recurrence Plots, MDF images are simple and effective to be construct. Taking the Fully Convolution Network (FCN) as the classifier, MDF demonstrates the state-of-the-art performance on the UCR time series datasets compared with other time series classification methods. The triadic MDF-FCN classifier gives the best result in the test. Furthermore, the MDF-FCN classifier is used for the malfunctioning industrial machine investigation and inspection sound dataset (MIMII). The MDF-FCN classification AUC demonstrates that it can have outstanding performance in realistic scenarios. Given the recent development of the Cloud Tensor Processing Units (TPU), the MDF-FCN classifier has great application potential in edge computing.
CFD Model of Particle Agglomeration in Spray Drying

A 3D CFD model of the agglomeration of droplets and particles in a counter-current spray-drying process was developed and verified. An original discrete phase model was elaborated, with an agglomeration module taking into account hydrodynamic segregation of particles, droplet coalescence, and droplet shrinkage for accurate calculations of mass balance of the discrete phase. The characteristic drying curves were applied to the model of particle moisture evaporation, which included the coupling of particle agglomeration with heat, mass, and momentum transfer between the discrete and continuous phases. Two agglomeration zones were observed in the tower: wet particle agglomeration in the atomization zone, and “dry agglomeration” above the air inlets, due to the intensive mixing of particle streams. A comparison of the calculated particle size distributions and experimental data obtained from particle dynamics analysis (PDA) measurements proves the accuracy of the developed methodology. The elaborated model allows the final PSD of the powder in the spray towers to be predicted.
Development of MEC system for indigenous 5G Test-Bed

Multi-access Edge Computing (MEC) is an evolving technology and considered as one of the critical enablers for technologies to meet the IMT definition of 5G communications. It integrates the service provider’s network with the high-performance computation facility to cater to various applications such as augmented reality, vision applications, etc., at the network edge. Thus, reducing the overall latency and preserving the bandwidth to send the complete data to the centralized data centers for processing instead. Furthermore, MEC opens up tremendous business opportunities for telecommunication companies by offering a service platform for deploying various services specific to consumers and industry at the network edge. In this work, we have demonstrated a full-fledged MEC architecture, including its entities, and integrated with 5G core following ETSI 5G specification with an application deployment use-case. The various MEC and 5G core components are instantiated in the virtual machine as a guest; the complete data path and control path is established between all core components following a service-based architecture. The MEC system developed is a part of the indigenous 5G Test-Bed project and follows the ETSI architecture. The developed system is suitable for executing various applications that require data offloading, extensive storage, and computing resources. Thus, our working MEC system establishes a proof of concept and closely resembles a real-world deployment of MEC system in 5G environment.