Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

In the existing studies on remote sensing image scene classification, the supervised learning methods which are fine-tuned from pre-trained model require a large amount of labeled training data and parameters, while unsupervised learning methods do not make full use of label information, and the classification performance could be improved. In this paper, we introduced semi-supervised learning into generative adversarial network (GAN), so the discriminator learned more discriminative features from labeled data and unlabeled data. Moreover, the mixup data augmentation method was introduced into our classification model to augment the data and stabilized the training process. We carried out extensive experiments for both UC-Merced and NWPU-RESISC45 datasets with a 5-fold cross-validation protocol using a linear SVM as classifier. We trained the proposed method on UC-Merced dataset and achieve an average overall accuracy of 94.05% under 80% training ratio. When trained on NWPU-RESISC45 dataset, the proposed method reached an average overall accuracy of 83.12% and 92.78% under the training ratios of 20% and 80% respectively, which achieves the state-of-the-art deep learning methods without pre-training.
Learning-based Downlink User Selection Algorithm for UAV-BS Communication Network

Recently, the development of Unmanned Aerial Vehicle (UAV) has been nearly matured and widely used in various fields. The combination of UAV and communication technologies, such as UAV Base Station (UAV-BS), can significantly increase the flexibility and scalability of the overall communication networks to provide more efficient communication services. While the UAV-BS improves the network service efficiency, the quality of services (QoS) in the air-to-ground communication link is highly affected unless the right users are unknown. In this paper, we propose the learning-based downlink user selection algorithm. The 3D downlink channel can be fast identified to judiciously select the users subset. In our proposed framework, we combine the k-means clustering and Convolutional Neural Network (CNN) that can increase the estimation accuracy of 3D wireless channels to enhance the communication service efficiency of the UAV-BS network. The field measurement results show that proposed method can achieve an average bit error rate (BER) of 3.56x10−7, which is better than the distance-based selection scheme that has an average of BER 2.88x10−3. The feasibility and effectiveness of the proposed method in real environment are proved, experimentally.
A Novel Lead-Acid Battery Equalization Circuit Based on Multiple Planar Transformers

Due to the defects in the producing procedure, series storage battery inevitably presents inconsistency. When being charged, high voltage battery of battery pack is easy to be overcharged; when being discharged, low voltage battery of battery pack is easy to be excessively operated. As the charge-discharge cycle of storage battery pack continuously increases, the inconsistency is also increased, which would greatly shorten the battery lifetime. In this work, a novel battery equalization circuit was realized to dynamically adjust the battery energy. This circuit could significantly reduce the inconsistency of a battery pack.
Methods for User Profiling across Social Networks

Users have their accounts on multiple Online Social Networks (OSNs) to access a variety of content and connect to their friends. Consequently, user behaviors get distributed across many OSNs. Collection of comprehensive user information referred to as user profiling; an essential first step is to link user accounts (identities) belonging to the same individual across OSNs. To this end, we provide a detailed methodology of five methods useful for user profiling, which we refer to as Advanced Search Operator (ASO), Social Aggregator (SA), Cross-Platform Sharing (CPS), Self-Disclosure (SD) and Friend Finding Feature (FFF). Taken together, we collect linked identities of 208,120 individuals distributed across 43 different OSNs. We compare these methods quantitatively based on social network coverage and the number of linked identities obtained per-individual. And also perform a qualitative assessment of linked user data, thus obtained by these methods, on the criteria of completeness, validity, consistency, accuracy, and timeliness.
Optimization of Stacked Nanoplate FET for 3-nm Node

In this article, various characteristics of nanoplate FET were studied based on TCAD simulation for a 3-nm node. The optimum geometry specification was proposed through comparison of RC delay from previous studies. Additionally, the impacts of self-heating effects (SHEs) in the 3-nm node were evaluated in the targeting device because it is expected that highly scaled areas such as channels have a negative effect on the performance due to low heat dissipation. This degradation was verified in a single device and in ring oscillator (RO) operation. pMOS is comparatively stronger than nMOS in terms of SHEs due to wider channel width. In RO operation, the influence of SHEs begins to appear at over 0.65 V through power and speed comparison. Therefore, an operation voltage under 0.65 V can be the optimal voltage to suppress SHEs.
An End-to-End Detection Method for WebShell with Deep Learning

In this paper, a generic static end-to-end detection framework with deep neural network for WebShell is designed, which is free from human labor and domain knowledge. In this paper, we simultaneously introduce word embedding in Natural Language Processing(NLP) and lexical analysis in Programming Language Processing(PLP) to obtain an accurate, structured, semantic-rich vector representation of the script code. For the obtaining's sake, a series of effective tricks are designed to further dig out the high-value information in the script while filtering noise. Then, we provide a desirable algorithm to down-sampling, which drastically reduces the computational costs at a relatively small information loss. Finally, we achieve high detection accuracy by employing the Deep Neural Network (DNN) composed of LSTM and pooling layers. The framework has a significant advantage at least on data set of the experiment.
Design Automation of Approximate Circuits With Runtime Reconfigurable Accuracy

Leveraging the inherent error tolerance of a vast number of application domains that are rapidly growing, approximate computing arises as a design alternative to improve the efficiency of our computing systems by trading accuracy for energy savings. However, the requirement for computational accuracy is not fixed. Controlling the applied level of approximation dynamically at runtime is a key to effectively optimize energy, while still containing and bounding the induced errors at runtime. In this paper, we propose and implement an automatic and circuit independent design framework that generates approximate circuits with dynamically reconfigurable accuracy at runtime. The generated circuits feature varying accuracy levels, supporting also accurate execution. Extensive experimental evaluation, using industry strength flow and circuits, demonstrates that our generated approximate circuits improve the energy by up to 41% for 2% error bound and by 17.5% on average under a pessimistic scenario that assumes full accuracy requirement in the 33% of the runtime. To demonstrate further the efficiency of our framework, we considered two state-of-the-art technology libraries which are a 7nm conventional FinFET and an emerging technology that boosts performance at a high cost of increased dynamic power.
RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User

Traditional recommendation algorithms such as matrix factorization, collaborative filtering perform poorly when lack of interactive information of user and product, known as the user cold-start problem, which may cut down the revenue of E-Commerce platform. Moreover, it is more challenging to generate recommendation lists for users who have no information at all because there is no preference information about them that could be leveraged, which is the user fully-cold-start problem. In this paper, a review aware cross-domain recommendation algorithm, called RACRec, is proposed to address the fully-cold-start problem in the field of product recommendation. Firstly, reviews are dynamically selected by using the adjacency matrix. Secondly, domain-specific preference vectors and domain-shared preference vectors of the cold start user are extracted by a migration model. On the other hand, the product feature vector in the target domain, which is generated from review texts by encoder and decoder, is combined with preference vectors of the cold-start user to make the rating prediction. Experiments on the Amazon datasets reveal that RACRec outperforms the state-of-the-art recommendation algorithms.
Sub-Frame Layer Rate Distortion Optimization for High Efficiency Video Coding

This paper proposes a sub-frame layer RDO algorithm to further improve coding performance of low-delay hierarchical prediction structure in High Efficiency Video Coding. In the proposed algorithm, each frame is segmented into two sub-frames according to motion intensity, and a sub-frame layer is taken as a basic optimizing unit. An inter-frame dependency model is proposed to quantify inter-frame dependency of a frame or a sub-frame. The proposed inter-frame dependency model is composed of two parts, Laplace distribution based direct inter-frame dependency model and distortion propagation chain. The direct inter-frame dependency model is used to evaluate dependency caused by direct reference, and the distortion propagation chain shows the relationship between direct reference and indirect reference. With quantified inter-frame dependency, Lagrange optimization is then used to solve the optimal $\lambda $ -QP pair for each sub-frame layer in frame layer 1 (L1) to frame layer 4 (L4). For frame layer 0 (L0), additional L0 frames are adaptively inserted according to inter-frame dependency of the previous L0 frame, and the optimal $\lambda $ -QP pair for each sub-frame in L0 is solved using Lagrange optimization. When peak signal to noise ratio is used as quality criterion, the proposed algorithm achieves 7.0% and 7.0% bit-rate reduction with lowdelay_P_main (LDP) and lowdelay_main (LDB) configurations respectively; when structural similarity is used as quality criterion, the proposed algorithm achieves 13.3% and 13.1% bit-rate reduction with LDP and LDB configurations respectively.
Research on Internal Temperature of Transformer Internal Temperature Based on Optical Fiber Sensing Technology

Transformer is an important link of power system, and its operation state directly affects the security and stability of power system operation. With the continuous development of the national economy and the continuous improvement of the people's needs, the power grid load has been continuously improved, and the capacity of the transformer has been continuously expanded, and the accident rate has increased accordingly. According to related statistics, more than 66 kv transformer of the world's average accident rate over 0.89%, especially in recent years, the transformer for winding overtemperature operation, result in transformer winding insulation aging and breakdown, accident of burning large ratio (according to the statistics, about 80% of the accident rate above). For oil-immersed power transformer, the time depends on the service life of solid insulating materials (fiber paper), prompting insulation materials is the main factor of aging temperature, moisture and oxygen, heat effect of transformer insulation aging is the decisive factor, therefore the transformer internal main parts (winding and iron core, the place such as insulating oil) temperature decides the service life of the transformer temperature due to the electric power equipment testing environment with high voltage and strong electromagnetic interference, and space is narrow, the traditional method cannot directly on the power equipment temperature state of online measurement, and a new temperature measuring system based on semiconductor optical fiber sensing technology has small volume, good insulation, good resistance to electromagnetic interference, temperature measurement consistency, can be directly installed on the transformer and other power equipment inside, can accurate and fast reflect the change of the internal temperature of main components of power transformer, the transformer winding and iron core, the place such as insulating oil temperature real-time online measurement.
A Light-Weight Deep Convolutional Neural Network for Speech Emotion Recognition using Mel-Spectrograms

At present, speech emotion recognition is a challenging field of studies about emotions of speakers. Speech emotion recognition also enhances interaction between humans and machines. In many situations (e.g. embedded systems), we have to detect emotion in speech within limitation of both computing and memory resources. Even several previous works reported that a reasonable recognition rate can be achieved using transfer learning techniques with popular models, such as AlexNet, they suffered with a large model size and can not be executed on an embedded system. To address this problem, we propose a lightweight deep convolutional neural network architecture, which utilizes only partial component of the AlexNet with Log-Mel-Spectrograms as input. Our result shows that the proposed lightweight model can achieve a comparable recognition rate with the state of the art, but the number of parameters used in our model decreases around 272 times from the AlexNet.
Long-Term Trend Prediction Algorithm Based on Neural Network for Short Time Series

In this paper, we focus on the problem of forecasting the trend of patents in different technologies. Different from other time series forecasting datasets, the length of series in our dataset is much shorter with fewer instances. So, other forecasting models which treat the time series as long high-dimension embedding do not perform well enough on this problem. Those models require a number of parameters. That is hard to trained by our dataset. And this kind of models can only applied on specified number of time series. While new technology emerges, the number of time series increases. That increases the dimension, so the model should be trained again. At the same time, not only do we value the error of forecasting, we also value the trend in our forecasting. So we develop a novel model that is trained by all the series to find the common patterns and generates corresponding prediction to deal with the trend. And we use a more appropriate index to evaluate trend that the model predicts. Treating the evolution of every technology as a time series, Convolution Neural Network (CNN) is used to capture the patterns among series. Therefore, our model requires less parameters and can be trained incrementally. Then, Recurrent Neural Network (RNN) is used to encode the information into an intermediate representation. With decoding the intermediate representation into values in multiple steps, the trend can be forecast. Finally, we test our model on other datasets. It achieves better results on some other datasets with that kind of characteristics.
Differential Pressure Transmitter Based on Programmable System-on-Chip (PSoC)

Differential pressure is an important technological parameter, one urgent task of which is control and measurement. To date, the lion's share of research in this area has focused on the development and improvement of differential pressure transmitters. This study builds on the literature in its aim to develop a differential pressure transmitter with improved operational and metrological characteristics. The measuring unit of the developed pressure transmitter is based on two strain gauge sensors. To achieve the stated aim, modern methods and technologies were used in the development of the electronics unit of the pressure transmitter. The Programmable System-on-Chip (PSoC) technology was used to develop the electronics unit. Due to the use of PSoC technology, it was possible to increase the noise immunity of the developed differential pressure transmitter, as well as provide an unparalleled combination of flexibility, integration analog and digital functionality.
Level Set Evolution with SOM-Based Least Squares Twin SVM for Noisy Image Segmentation

This paper presents a novel hybrid framework derived from the least squares twin support vector machine (LSTSVM) and active contour model (ACM) for noisy image segmentation. It contains a two-stage process, where the concurrent self organizing maps (SOM) are firstly employed to approximate the image intensity distributions to establish the original training sets for the LSTSVM. The training sets are then updated by adding the global region intensity means during the curve evolution. Further, the discrimination functions of the LSTSVM are embedded into the energy function of the ACM to guide the curve movement. Besides, a variable regional coefficient is designed in the energy function to enhance the noise robustness. Experiment results demonstrate that our model holds higher segmentation accuracy and more noise robustness.
Enhancement of Microgrid Stability Using a Novel Scheme of Load Shedding Based-DFR Coordinated with SMES

Nowadays, renewable energy sources (RES) have been integrated into the electrical power systems especially microgrids (MGs), as a solution to generate a clean and cheap electric power generation. However, the considerable utilization of RES leads to a decrease of system inertia, which in turn causes undesirable effects to the MG frequency stability and security and could lead to system collapse. Therefore, this paper presents a new application of an adaptive digital frequency relay (DFR) to protect the RES against frequency fluctuations in MGs. The process of the DFR comes as a second strategy after the action of the governor and load frequency control (LFC) control strategies. It is used to carry out automatic load shedding (LS) in case of under frequency disturbances. In high emergency cases (i.e. loss of generation units), the primary control, LFC, and LS technique may not stabilize the MG system frequency. Hence, a third strategy using the superconducting magnetic energy storage (SMES) is proposed to avoid the power system collapse. It is used as a feedback controller to support the LFC by quick power transfer from/to the MG. A systematic study has been conducted for three scenarios: the scheme with a conventional controller; a LS scheme using the DFR; and the scheme with SMES. A comparison of the results has been presented on the MG system. It is found that the SMES device can enhance the MG frequency stability and minimize the economic losses of the cogeneration facility due to unnecessary load shedding.
D2D Enabled Cellular Network Spectrum Allocation Scheme Based on the Cooperative Bargaining Solution

In 5G cellular networks, device-to-device (D2D) communication has undoubtedly become a general trend to increase spectral efficiency while reducing communication delay. In particular, D2D technology is able to bear more and more services, which help user-centred and personalized services in future wireless networks. However, several technical issues and challenges are associated with the deployment of D2D communications. In this paper, we tackle the spectrum allocation problem of D2D enabled cellular networks. By employing the major ideas of cooperative game theory, we design a novel two-stage resource allocation protocol based on the weighted utilitarian and meta bargaining solutions. The purpose of bargaining solutions is to clarify what could be the best solution when game players share a surplus that they can jointly generate. According to the main advantages of step-by-step interactive bargaining mechanism, our proposed solution takes various benefits in a rational way. Some simulation results and numerical analysis are provided to confirm the effectiveness of our two-stage bargaining approach and validate the accuracy of the proposed spectrum allocation scheme. Finally, we address some challenges and identify research areas for the future study.
Field Experiments of 28 GHz Band 5G System at Indoor Train Station Platform

Recently, a fifth-generation cellular system (5G) is widely expected to provide plenty of wireless network resources (i.e., broadband capacity). In this paper, to validate 5G system performances, such as physical-layer and TCP-layer throughputs, we carry out a field trial at an actual indoor train station, named Haneda International Airport Terminal Station. In the field trial, we deploy the prototype 5G system (Central Unit, Distribution Unit, Radio Unit and 5G UE (tablet)) on the train station platform and evaluate mobile 5G downlink throughputs. Through the actual measurements, the results confirm that the prototype 5G system can achieve mobile broadband capacity (more than 1 Gbps) even when the UE is located anywhere at the indoor train station platform.
Joint Wireless Source Management and Task Offloading in Ultra-Dense Network

The ultra-dense network (UDN) based on mobile edge computing (MEC) is an important technology, which can achieve the low-latency of 5G communications and enhance the quality of user experience. However, how to improve the task offloading efficiency is a hot topic of UDN under the constraint on the limited wireless resources. In this article, we propose a heuristic task offloading algorithm HTOA to optimize the delay and energy consumption of offloading tasks in UDN. Firstly, a convex programming model for MEC resource allocation is established, which aims to obtain the optimal allocation set of resources for offloading tasks, and optimize the execution delay of offloading tasks. Followed by, the problem of joint channel allocation and user upload power control is solved by the greedy strategy and golden section method, which aims to optimization the delay and energy consumption of task upload data. Compared with the random task offloading algorithm, numerical simulations show that the algorithm HTOA can effectively reduce the delay and energy consumption of task offloading, and perform better as the number of users increases.
Asymmetric Transmission through Single-Layered All-Dielectric Metasurface

Optical devices require simultaneous control over wavefront and polarization of light to achieve various phenomena such as asymmetric transmission, reflection, and selective absorption. Traditionally, cascaded bulk components were used in these systems, which worked by manipulating the propagation phase of the incident light. These large components made the system impossible to miniaturize, which led to an interest in compact optical systems and the search for novel materials which would enable the system to exhibit the same phase-dictated phenomena through wavefront shaping. Here, we present a single-layered all-dielectric metasurface structure with nano-resonators which engineer the wavefront to achieve asymmetric transmission of circularly polarized light. A mathematical analysis of the phenomena is done using Jones Calculus. Finite Difference Time Domain (FDTD) Solutions is then used to optimize the structure hence achieving a 76% Asymmetric Transmission Parameter.
A Modified Power Allocation Algorithm Based on Convex Optimization and User Selection

This paper studies power allocation schemes based on a precoding algorithm that maximizes signal to leakage plus noise ratio (SLNR) to optimize the sum capacity of MU-MIMO systems. To solve the problem that the result of an existing algorithm may contain negative power allocation factors at low SNRs, we proposed a sub-optimal iterative power allocation algorithm based on convex optimization with user scheduling. Simulation results show that the proposed scheme outperforms the existing ones at low SNRs, which can be used more widely. Moreover, better performance in terms of sum capacity can also be realized by the proposed algorithm compared with existing algorithms.