Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of a Five-phase Synchronous Reluctance Motor Connected to Matrix Converter

This paper analyses the performance of a high reliability multiphase electric drive system. The drive system consists of a five-phase synchronous reluctance machine (SynRM) connected to a three to five-phase matrix converter (MC). In this system, there is no vulnerable electrolytic DC-link capacitors, which considered a point of failure in conventional electric drive systems. The indirect space vector modulation (SVM) is used to control the five-phase MC. Indirect SVM deals with the five-phase MC as a virtual two-stage converter consisting of a virtual DC link, a virtual rectifier stage and a virtual inverter stage. In addition, a modified closed loop field-oriented control based on the indirect SVM is proposed to control the five-phase SynRM. The performance of the drive system is analyzed at different operating conditions.
Research on Technologies of User Side Integrated Demand Response Communication Service for Multi-Energy Coordination

Integrated Energy System (IES) can integrate various forms of energy, bringing new developments to demand response (DR). The demand response of a single type of energy system cannot satisfy user satisfaction and corresponding effects at the same time, traditional demand response is developing towards integrated demand response (IDR). This paper first proposes the concept of integrated energy system and the frame of integrated demand response for multi-energy coordination. Then, the communication service level for IDR is specified, and the idea of communication service optimization is proposed. This paper designs an improved communication service optimization algorithm, which can solve the communication quality problem in the user-side integrated demand response. Case study illustrates the benefits of the algorithm proposed in this paper.
Joint Subchannel and Power Allocation for Secure Transmission in Femtocell Networks

This paper studies the physical layer security problem in the downlink two-tier femtocell network system, where an eavesdropper wiretaps the legitimate macrocell users. In this system, femtocell base stations (FBSs) can reduce the performance of eavesdropper through cochannel interference, which in turn improves the secure transmission rate of the entire system. The power allocation problem of the FBSs and macro base station (MBS) has an important influence on the cochannel interference and system performance in femtocell networks. In this paper, the power allocation problem is modeled as an optimization problem. We aim to maximize the secrecy rate and reduce energy consumption. Based on this, a joint subchannel and power allocation algorithm is proposed. Simulation results show that the proposed algorithm can effectively improve the network transmission performance and reduce the energy consumption, compared with existing algorithms.
Primary Study on Application of SPAD Value to Estimate Chlorophyll and Nitrogen Content in Wheat Leaves

SPAD (Soil and Plant Analyzer Development) values at different growth stages and its relationship to chlorophyll, total nitrogen and NO_3~--N (content) in leaves of three medium-gluten wheat cultivars, namely Yangmai 11, WH510 and Xuzhou 26, were investigated. The results showed that SPAD values varied distinctly with cultivars. The relationship between SPAD value and chlorophyll (content) also (differed) among wheat cultivars. The SPAD values were positively correlated with chlorophyll (content) in the leaves of (different) plant positions for the same cultivar wheat. The SPAD value was also positively correlated with total nitrogen (content) in the leaves of (different) wheat cultivars. Therefore, it may be suggested that SPAD value can be used to (estimate) total nitrogen (content) to diagnose nitrogen status in wheat plants. Since the SPAD value of the (different) position leaves of the same cultivar wheat maybe not have obvious relationship with the total nitrogen (content). The SPAD value should be careful used to diagnose the nitrogen content of (different) part of the wheat leaf.
Learning Higher Quality Rotation Invariance Features for Multioriented Object Detection in Remote Sensing Images

Multioriented object detection, an important yet challenging task because of the bird's-eye-view perspective, complex background, and densely packed objects, is in the spotlight of detection in remote sensing images. Although existing methods have recently experienced substantial progress based on oriented head, they learn little about essential rotation invariance of the objects. In this article, a novel framework is proposed that can learn high-quality rotation invariance features of the multioriented objects by three measures. Given a remote sensing image, the multiscale semantic segmentation feature fusion module first merges the global semantic segmentation features predicted by the semantic segmentation branch and the multiscale features extracted by the backbone with FPN in order to distinguish complex background. Then, the discriminative features are used by rotation mainstream, whose structure is similar to cascade R-CNN and can extract higher quality rotation invariance features and predict more accurate location information by adaptively adjusting the distribution of the samples through progressive intersection over union thresholds. And in order to improve the performance of mainstream to predict more accurate oriented bounding box, the horizontal tributaries that can fully leverage the reciprocal relationship between the oriented detection and horizontal detection were added to the latter two stages. Extensive experiments on three public datasets for remote sensing images, i.e., Gaofen Airplane, HRSC2016, and DOTA demonstrate that without bells and whistles, the proposed method achieves superior performances compared with the existing state-of-the-art methods for multioriented detection. Moreover, our overall system achieves 59.264% mAP of airplane Detection in 2020 Gaofen challenge, ranking third in the final.
Impact of LTE-V2X Connectivity on Global Occupancy Maps in a Cooperative Collision Avoidance (CoCA) System

Cooperative Intelligent Transport Systems (C-ITS) are expected to fulfill the needs of various emerging road safety applications. In this work, we focus on improving C-ITS vulnerable road users safety by considering a Cooperative Collision Avoidance (CoCA) system. In this system, vehicles rely on on-board sensors in order to generate local occupancy maps that are transmitted by means of LTE-V2X connectivity to a fusion center. The latter executes the fusion of successfully received local maps, in order to generate a global occupancy map, which can reveal obstacles that could not be perceived based uniquely on standalone vehicle’s perception means. The resulting map is broadcast to all connected vehicles in transmission range (i.e., even those who have not contributed to the global map) in order to announce a risk of collision. The main objective of this work is to evaluate the impact of LTE-V2X connectivity performance on the fusion result and to define the best compromises between the communication configuration, the definition of occupancy maps and obstacle detection capabilities.
Propagation-aware Gaussian Process Regression for Signal-Strength Interpolation along Street Canyons

Providing accurate estimates for Key Performance Indicators (KPIs) of mobile cellular networks is crucial to fulfill agreed-upon service requirements. Typically, such performance maps are based on measurements, which are expensive and only offer sparse samples of the underlying ground-truth. Therefore, we require interpolation schemes to further increase the spatial resolution in the area of interest. In dense urban areas, the propagation environment is often governed by tunneling effects along streets and blockages introduced by buildings. However, these aspects are typically not accounted for in state-of-the-art interpolation approaches. As such, we propose a propagation-aware interpolation scheme based on Gaussian Process Regression (GPR), which utilizes available geospatial information via the diffusion kernel. Based on simulations in a stochastic Manhattan Grid (MG) environment, we show that our approach is well equipped to handle environments dominated by tunneling effects and significantly outperforms the omnidirectional Radial Basis Function (RBF) kernel. Further, the proposed graph-based scheme offers a high degree of flexibility in modeling the environment. Therefore, it is not specific to the MG scenario but can easily be extended to generic blockages and real-world street layouts. We show this by applying our approach to a set of Reference Signal Received Power (RSRP) measurements from an operational LTE network collected in a drive-test campaign in Vienna, Austria — again resulting in an error reduction.
Processing of Bistatic SAR Data With Nonlinear Trajectory Using a Controlled-SVD Algorithm

The nonlinear trajectory and bistatic characteristics of general bistatic synthetic aperture radar (SAR) can cause severe two-dimensional space-variance in the echo signal, and therefore it is difficult to focus the echo signal directly using the traditional frequency-domain imaging algorithm based on the assumption of azimuth translational invariance. At present, the state-of-the-art nonlinear trajectory imaging algorithm is based on singular value decomposition (SVD), which has the problem that SVD may be not controlled, and thus may lead to a high imaging complexity or low imaging accuracy. Therefore, this article proposes a nonlinear trajectory SAR imaging algorithm based on controlled SVD (CSVD). First, the chirp scaling algorithm is used to correct the range space-variance, and then SVD is used to decompose the remaining azimuth space-variant phase, and the first two feature components after SVD are integrated to make them be represented by a new feature component. Finally, the new feature component is used for interpolation to correct the azimuth space-variance. The simulation results show that the proposed CSVD can further improve the image quality compared with SVD-Stolt.
BSDNet: Balanced Sample Distribution Network for Real-Time Semantic Segmentation of Road Scenes

In recent years, semantic segmentation based on deep convolutional neural networks has developed rapidly. However, it is still a challenge to balance the computing cost and segmentation performance for the current semantic segmentation methods. This paper proposes a lightweight real-time semantic segmentation model Balanced Sample Distribution Network (BSDNet), to solve this problem. In BSDNet, we introduce the Balanced Sample Distribution Module (BSDModule) to balance the sampling distribution of convolution and obtain features with a larger receptive field. To optimize the segmentation effect, we introduce a Shuffle Channel Attention Module (SCAModule) to enhance the interaction of channel features at the cost of a small number of parameters. BSDModule and SCAModule are lightweight and flexible and can adapt to different types of network structures. Extensive experiments on CityScapes and CamVid show that the proposed method can balance the computing cost and segmentation performance. Specifically, on the CityScapes with $512\times1024$ resolution, BSDNet-Xception39 achieves 68.3% MIoU and 84.6 FPS with only 1.2M parameters.
Compact Bit-Parallel Systolic Multiplier Over GF(2m)

This article presents a compact and efficient bit-parallel systolic array structure for multiplication over the extended binary field, GF(2<inline-formula> <tex-math notation="LaTeX">$^{m}$ </tex-math></inline-formula>). The systolic array has a regular arrangement with local connections, making it more suitable for VLSI implementations. Also, it has the merits of having hardware complexity of order <inline-formula> <tex-math notation="LaTeX">$\mathcal{O}(m)$ </tex-math></inline-formula> that distinguishes it from the previously reported bit-parallel designs having hardware complexity of order <inline-formula> <tex-math notation="LaTeX">$\mathcal{O}(m^{2})$ </tex-math></inline-formula>. The achieved results exhibited that the suggested parallel architecture realizes a significant reduction in hardware complexity and the area-delay complexity over the competitor architectures previously published in the literature. Therefore, it is more suitable for usage in constrained hardware environments, having more restrictions on space, such as portable devices and smart cards.
Index Code Construction via Deep Matrix Factorization

In this paper, we consider the problem of on-demand source coding for collaborative data dissemination in vehicular ad-hoc networks (VANETs). Specifically, we address the problem of index code construction for a big-data network. In this setup, users request a certain subset of data from a central server, while possessing some side information. The central server broadcasts a compressed message to meet all the users’ demands. We consider a practical and generalized scenario where the side information and requested data packets of a user may be coded. Constructing index codes for such a setup is known to be an NP-hard problem. For this scenario, we propose a deep learning based approach to construct index codes through low-rank matrix completion. The proposed deep neural network performs matrix factorization to obtain a low-rank index code. We show that the proposed deep learning based approach to construct index codes is not only computationally feasible for networks with large data packets, but also provides better performance compared to the alternating minimization based approach for index code construction.
Boosting Secret Key Generation for IRS-Assisted Symbiotic Radio Communications

Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS’s phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS’s random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS’s phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.
Slot Water Jacket Cooling System for Traction Electrical Machines with Hairpin Windings: Analysis and Comparison

This work aims to analyze and compare the electromagnetic and thermal behavior of two commonly used electrical machines in the automotive field: an induction motor and a brushless permanent magnet one, here equipped with slot water jacket cooling systems. The same stator design, drive management, and winding layout, with hairpin conductors, are adopted for both machines. Four different slot cooling system design arrangements are investigated, keeping the same overall machine volume. Both motors are simulated with the proposed cooling system arrangements. Loss analyses are carried out to evaluate the impact of the different channel positioning inside the slot and their effects on the electromagnetic performance. In the end, the best cooling solution is identified for each machine, highlighting the main differences between the two machines' behavior, in addition to proving the cooling system topology goodness for hairpin windings machines in the automotive sector.
Performance Evaluation of Carrier Aggregation as a Diversity Technique in mmWave Bands

Millimeter wave (mmWave) frequencies have been selected to provide the high data rates and capacity targeted by the next generation (5G) of mobile communication networks. Compared to the sub 6-GHz bands that have been historically exploited by popular communication systems, mmWave bands suffer from adverse radio propagation degradations such as higher path loss and blockage, however they provide significantly larger bandwidths and lower levels of usage than sub 6-GHz bands. Mobile operators can employ Carrier Aggregation (CA) techniques to combine the newly acquired spectrum in mmWave bands with their already allocated spectrum in sub 6-GHz bands. The use of CA as a technique to combine chunks of spectrum in a transparent manner for higher layers of the protocol stack has been widely researched. In this paper, a different focus is considered by exploring the use of CA as a diversity technique in order to further improve the network capacity under various radio propagation scenarios. The obtained results indicate that CA can effectively be exploited as a diversity technique to optimize the overall system performance and increase the network capacity, with the optimum number of component carriers depending on the radio propagation distance.
An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

Existing power grids (PGs) and in-home energy management controllers do not offer its users the choice to maintain comfort and provide a bearable solution in terms of low cost and reduced carbon emission. This work is based on energy usage scheduling and management under electric utility and renewable energy sources i.e., solar energy (SE), controllable heat and power (CHP) and wind energy (WE) together. Efficient integration of renewable energy sources (RES) and battery storage system (BSS) have been suggested to solve the energy management problem, reduce the bill cost, peak-to-average ratio (PAR) and carbon emission. User’s electricity bill reduction have been achieved by proposed power usage scheduling method and integrating low cost RESs. PAR minimization have been achieved through shifting the demand in response to real time price from high-peak hours to low-peak hours. In this context, load scheduling and energy storage system management controller (LSEMC) is proposed which is based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization (BFO) and our suggested hybrid of GA, WDO and PSO (HGPDO) algorithm. The performance of the heuristic algorithms and proposed scheme is evaluated numerically. Results demonstrate that our proposed algorithm and the LSEMC reduces the electricity bill, PAR and CO2 in Case 1, by 58.69%, 52.78% and 72.40%, in Case 2, by 47.55%, 45.02% and 92.90% and in Case 3, by 33.6%, 54.35% and 91.64%, respectively as compared with unscheduled. Moreover, the user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality and visual improves by 35.55%, 16.66%, 91.64% and 45%, respectively.
A Micro-Motion Feature Extraction Method Based on CORR-OMP

The micro-Doppler characteristics caused by the target micro-motion can reflect the unique attributes of the target and it can be used as an important basis for aircraft recognition. Firstly, the scattering point model of helicopter is established, and the micro-Doppler modulation phenomenon is analyzed in this paper. Secondly, the time-domain flicker interval of the target is estimated by using multiple autocorrelation method, combined with time-frequency analysis, the parity of the number of target blades is judged, and the dimension of sparse dictionary is reduced. Finally, an orthogonal matching pursuit algorithm is used to extract the micro-motion features of the target and the estimation performance under different SNR conditions is discussed. The simulation results verify the effectiveness of this method and the correctness of the theoretical analysis.
Research on Sub-Nyquist Rate Sampling Method Based on Sparse Fourier Transform Theory

With the development of information technology, the amount of data of the signal acquisition end in high sampling rate fields such as wireless communication and medical imaging based on the Nyquist sampling theorem has increased dramatically. The resulting intensive data processing brought great challenges to the data collection, storage, and transmission of the system. Researchers began to focus on the “sparse” characteristic of the signal, hoping to see the whole from the small, and develop a more efficient signal processing method. Recent studies showed that the Sparse Fourier Transform algorithm can achieve sublinear Fourier transform based on the “sparse” characteristic of signals in the frequency domain. In this paper, based on the theoretical research and application development of Sparse Fourier Transform in recent years, the basic theoretical framework of Sparse Fourier Transform, frequency basket, subsampling Fast Fourier Transform, and other key technical problems and classical reconstruction algorithms are summarized. And an offline method is used to verify that, in the sampling process of one-dimensional random signals which are sparse in the frequency domain, the sampling rate of the subsampling method based on Sparse Fourier Transform is reduced to 1/27 compared with that of the traditional Nyquist sampling, and the recovery error is less than 10e-14 in the case of no noise, thus successfully completing the compression in the signal acquisition process.
Greenhouse Automation Using Internet of Things in Hydroponics

Hydroponics is a technique in agriculture that uses very minimal nutrient material to nourish the plants. In this project it is proposed to connect all the sensors and actuators to the UBIDOTS cloud and make the environment a closed loop system under the concept Internet of Things (IoT). We have used these sensors to create an artificial luminance to provide efficient photosynthesis even in low light conditions/night. The system also focuses on monitoring the temperature and humidity inside greenhouse. Using this ambience values, the weather inside the greenhouse is controlled by actuating the exhaust fan and fogger. The entire project is automated using IoT and the microcontroller used is ESP32 which has an inbuilt Wi-Fi Module for connecting to cloud. All the data will be viewed in UBIDOTS cloud platform in real time. The variations in all the data can be visually seen by the user in the application.