Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term Load Forecasting of Long-short Term Memory Neural Network Based on Genetic Algorithm

Accurate load forecasting is conducive to the reasonable arrangement of power grid dispatching plans. Traditional load forecasting methods cannot handle the time series and nonlinear characteristics of load well. Long-short term memory (LSTM) neural networks can record long-term and short-term information, which can effectively solve this kind of problem. But the parameters of LSTM network are difficult to determine. For this reason, this paper proposes a long-short term neural network based on genetic algorithm. The learning rate and iteration number of the LSTM network are used as chromosomes, and the genes are continuously selected, crossed, and mutated to obtain more good genes. Comparing this method with the standard LSTM network, the simulation results show that the LSTM network using genetic algorithm for parameter optimization improves the prediction accuracy of the standard LSTM network by 63%.
Hybrid Millimeter-Wave Massive MIMO Systems with Low CSI Overhead and Few-Bit DACs/ADCs

Hybrid precoding/combining (HPC) architecture is a promising candidate for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. It is capable of reducing the hardware cost/complexity and power consumption compared to the full-digital precoding/combining (FDPC) while keeping the similar spectral efficiency. Most of the prior works on HPC consider the availability of full channel state information (CSI) to design both radio-frequency (RF) and baseband (BB) stages. In this work, an angular-based HPC (AB-HPC) design requiring low CSI overhead is proposed for mmWave massive MIMO systems equipped with low-resolution digital-to-analog converters (DAC) and analog-to-digital converters (ADC). Based on the 3D geometry-based mmWave channel model, the transmit and receive RF beamformers are first developed based on the slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) parameters, respectively. Then, the transmit BB precoder and receive BB combiner are designed by employing the reduced-size effective CSI seen from the BB-stages. Considering the effect of low-resolution DACs/ADCs, the receive BB combiner is obtained by the minimum mean square error (MMSE) criterion. The numerical results reveal that the proposed AB-HPC technique can closely approach the achievable rate performance of FDPC while remarkably reducing the number of power-hungry RF chains and CSI overhead size (e.g., around 94.1% – 98.5%). Moreover, the quantization error occurred due to the low-resolution DACs/ADCs causes a performance floor. For a given signal-to-noise ratio (SNR), we also ask the required number of bits for the low-resolution DACs/ADCs for converging to the same achievable rate performance in full-precision DACs/ADCs.
Modeling and Analysis of Small Cell Networks with NOMA: A Stochastic Geometry Approach

In this paper, a stochastic geometry approach is used to model and analyse a downlink small cell network (SCN). Carrier sense multiple access with collision avoidance (CSMA/CA) is used as medium access control (MAC) protocol at the base stations (BSs). To boost spectral efficiency, each active BS deploys both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) to transmit signals to the user equipments (UEs) in its coverage area. The choice between OMA and NOMA mode is based on the density of UEs and aims to improve the sum rate of the BS. If there exist two UEs that sufficiently differ in their channel power gains to the BS, the BS will use NOMA superimposing the signals of the UEs in the power-domain. Otherwise, the BS will operate in OMA mode to transmit the signal of a single UE in its coverage area. Stochastic geometry is used to include the spatial densities of the UEs and BSs in the performance assessment of the SCN. On this basis, analytical expressions for the coverage probability, data rates of the UEs, and sum rate of the BSs of the considered system are derived. Numerical results are provided to illustrate the impact of system parameters on the performance of this SCN with NOMA subject to the spatial densities of the BSs and UEs.
EV Charging Recommendation Concerning Preemptive Service and Charging Urgency Policy

Compared with traditional internal combustion engine vehicles, Electric Vehicles (EVs) have the advantage of eliminating harmful gases in the environment, with great development potential in recent years. However, because the battery capacity of EVs is limited at the current stage, where to charge (to select charging station) and when/whether to charge (order the charging priority of EVs) still limit the large-scale popularity of EVs. In this paper, we develop an Urgency First Charging (UFC) charging scheduling policy, which takes the remaining parking time and charging time of EVs as the standard of charging priority. With this, the CS benefits to the shortest trip duration (summation of travelling time through CS, and charging service time at CS) is selected as optimal solution. We have conducted simulations through Helsinki’s traffic scenarios. The results have shown that our proposed CS-Selection scheme effectively improves the charging comfort (in terms of waiting time and trip time) and charging efficiency (in terms of not-fully charged service due to limited parking duration).
Multi-Scale Deep Residual Learning for Cloud Removal

This paper proposes a multi-scale deep residual network (MDRN) for removing clouds from remote sensing images. MDRN characterizes learning the residuals of cloud-free images and cloudy images instead of directly mapping the two parts. The learned residuals are subsequently integrated with the input cloudy images to generate the de-clouded results. The advantages of our MDRN are threefold. Firstly, we untangle the sparse details of cloudy images from the bases by a guided filter, making the learning focus on processing the textures and the cloud features in the details. Secondly, we employ multi-scale convolution units (MS-Conv), which have larger receptive fields than plain convolutions and assist in extracting representative features. Thirdly, we leverage deep residual learning to avoid performance degradation and relax learning burdens. Experimental results on the remote sensing image cloud removing dataset (RICE) validate the effectiveness of our MDRN.
Mobility-Aware Differentially Private Trajectory for Privacy-Preserving Continual Crowdsourcing

Participating in mobile services by synthesizing trajectories with consistent lifestyle and meaningful mobility as actual traces are the most popular way to protect location privacy. However, recent trajectory synthesizing techniques are still threatened by the information that the attacker inevitably obtains, such as the locations of the accepted tasks in the crowdsourcing application. With this information and the spatiotemporal correlation hidden in the user’s mobility, the attacker can infer the user’s actual location and even future behaviors. It remains open to defend against such inferential attacks in the continual crowdsourcing scenarios. In this paper, we propose a mobility-aware differentially private solution, ConCrowd-DP, for achieving the privacy-preserving continual crowdsourcing application. Specifically, before starting the application, we first construct a spatiotemporal mobile model, STMarkov, to model the spatiotemporal correlation in users’ mobility. Then, a perturbed location is generated for the user to participate in the crowdsourcing application, according to STMarkov and $K$ -norm DP. Finally, we eliminate the privacy threat brought by the accepted task based on $K$ -norm DP and Bayesian posterior theorem. With ConCrowd-DP in place, a mobility-aware differentially private trace is generated for the user to participate in the application continually. Extensive experiments with real-world datasets demonstrate that ConCrowd-DP guarantees the usability of the synthesized trajectory effectively, while providing the DP protection for defending against the inferential attacks which stem from the multiple accepted tasks.
Compact Substrate Integrated Choke Ring Ground Structure for High-Precision GNSS Applications

A compact dual-band substrate integrated choke ring (SICR) ground structure has been designed and presented. Miniaturization has been achieved by using a high permittivity substrate and employing only two corrugations rather than the commonly used four to six corrugations. Besides, the dual-band behavior is obtained by engineering the location of via holes and partial copper cladding of the substrates in a multilayer structure. The performance of a right-handed circularly polarized (RHCP) global navigation satellite system (GNSS) antenna has been investigated while it is mounted on the designed SICR ground, on the classic one and by itself. The proposed SICR demonstrates high multipath mitigation ability, comparable to the classic choke ring, while it eliminates the disadvantages of the classic one such as large size, heaviness, and high fabrication cost. An example design shows miniaturization of 70% in height and 44% in diameter compared to the classic choke ring,
Enabling Massive Access of IoT by Superposition Bloom Filter and Compressed Sensing

The pilot design and the corresponding receiver algorithm are the key issue to support massive access via grant-free uplink transmission. In this paper, we propose a Superposition Bloom Filter (SBF) pilot design, where an SBF pilot is composed of several sequences. A SBF pilot can be distinguished from another one by different combination of sequences, i.e., the signature of the SBF pilot. The key improvement over previous BF-related work is to consider superposed complex hash-coded information instead of binary one. To guarantee the detection performance under large pilot pool size, we give the rule of constructing pilot pool based on (K;D;ρ)-expander and design a receiver algorithm including correlation-based sequence detection and compressed sensing (CS) based signature recognition, which uses the amplitude and phase information of received sequences. The simulation results show our proposed SBF pilot and corresponding receiver algorithm greatly improve the detection performance in terms of missing detection probability and false alarm probability in frequency-selective channels, compared with previous work.
Leaf Counting in Rice (Oryza Sativa L.) Using Object Detection: A Deep Learning Approach

Leaf count is one of the crucial tasks in plant phenotyping, and leaves are the basic unit of plant architecture involved in photosynthesis, growth, and yield of a plant. Therefore, the total number of leaves per plant is considered as one of the essential physio-morphological plant traits for phenotyping. The current work proposes to estimate the total number of leaves of a rice plant by detecting their leaves tips. A rice plant has a single tip for a single leaf. Hence, this proposed framework counts the total number of leaves by counting the number of leaves tips equal to the number of leaves. You Only Look Once (YOLO) algorithm is used for the detection of the leaves tips as an object. This hypothesis builds a basis for counting the total number of leaves in a plant like rice, and similar field crops such as wheat (Triticum aestivum L), maize (Zea mays L.), sorghum (Sorghum bicolor), barley (Hordeum vulgare L.). The model detected leaves of a rice plant (RGB images) by detecting corresponding leaves tips with YOLO having average accuracy up to 82% and IOU around 0.53-0.60 and estimates the number of leaves in a plant by counting predicted bounding boxes around tips. The model also performed well with the wheat crop.
High-Order Triplet CRF-Pcanet for Unsupervised Segmentation of SAR Image

In this paper, we combine the modeling power of conditional random fields (CRF) model with the representation-learning ability of principal component analysis network (PCANet), and propose a high-order triplet CRF model, named as HOTCRF-PCANet, for unsupervised synthetic aperture radar (SAR) image segmentation. HOTCRF-PCANet introduces an auxiliary field to explicitly regulate label interactions of complex SAR image. In the label and auxiliary fields, HOTCRF-PCANet defines a discrete quadrilateral pairwise Markov fields (DQPMF) model, and thus constructs a high-order DQPMF potential to model the high-order label interactions in an unsupervised way. Additionally, HOTCRF-PCANet uses a product-of-expert (POE) potential to enforce the regions' labeling consistency for pixels within the weak-structured region. Moreover, HOTCRF-PCANet modifies PCANet into an unsupervised mode, i.e. UPCANet, automatically learns rich features of SAR image and constructs an UPCANet-based unary potential to predict the local class probability. The effectiveness of HOTCRF-PCANet is demonstrated by the application to the unsupervised segmentation of simulated and real SAR images.
Short-term Load Forecasting Based on Kmeans and FR-DBN Models

In view of the fact that the combination of several models is mostly considered from the perspective of model weight combination, and the selection of weights has certain subjectivity, this study based on the perspective of unsupervised feature learning, presents a combined model prediction method based on kmeans and conjugate gradient method (FR) to improve deep learning network (DBN). The clustering algorithm accelerates the learning rate by batch data. Meanwhile, the unsupervised feature learning mechanism of DBN is introduced into the load forecasting model, and the FR Conjugate gradient method is used to optimize the model. The prediction results and the prediction time are compared with the traditional prediction model. Finally, the performance of fr-dbn combined forecasting model is better than other models in short-term load forecasting.
Development of Cable-driven Anthropomorphic Robot Hand*

This letter presents a novel design of an anthropomorphic robotic hand. By using a parallel plate knuckle with a universal joint, it is possible to achieve two degree of freedom (DOF) at a joint of a finger with wide working range in the proposed design. Each degree of freedom of the robotic hand is driven by parallel cables with single motor that mimics the muscle antagonism of the human hand. The proposed robotic hand embodies human-like features and achieves an adaptive grasping capability by adopting compliant coupling between the distal and the proximal interphalangeal joints. Experiments are conducted to validate the performance of the robotic hand.
Second harmonic generation in collagen

Ordered collagen in native, intact rat tail tendon has been studied by means of optical second harmonic generation (SHG). The two independent hyperpolarizability tensor elements of the collagen molecule, β3 and β1, have been found to be of opposite sign and roughly equal in absolute magnitude. Evidence is presented for a systematic variation in the ratio β3/β1 with age which may reflect the effects of progressive cross linking. Our experimental findings are in reasonably good agreement with a simple theory constructed on the basis of current models of the tendon structure, and thereby confirm this structure in the native, intact tissue. The many advantages of SHG for the structural study of intact partially ordered biological tissues are outlined.
Asymptotic Cumulative Distribution Functions for Correlated Lognormal Channels and Their Applications in Selection Combining

Traditional asymptotic analysis techniques fail for lognormal random variables (RV) because a lognormal probability density function cannot be quantified by Taylor series at the origin, and the moment-generating function of a lognormal RV does not yield a unified form. In this work, we derive two closed-form asymptotic expressions for the multivariate cumulative distribution function (CDF) of L correlated lognormal RVs. Among these two asymptotic expressions, one has an elegant form but low converging speed, while the other has a fast converging speed but involves Gaussian Q-functions. Furthermore, we derive asymptotically tight upper and lower bounds for the multivariate CDF to evaluate the error of the asymptotic approximation. The new analytical results are shown to be effective in evaluating the outage probability of selection combining (SC) over correlated lognormal fading channels when Monte Carlo simulation is expensive. More importantly, the elegant asymptotic expressions show that little outage performance improvement can be introduced to the SC system by adding more antennas for some cases.
Single-input Single-output SAR-FFT for Fast Multistatic Millimeter Wave Imaging

In this paper, fast multistatic millimeter wave imaging (MMWI) is considered. A single-input single output (SISO) SAR-FFT algorithm using significantly less data is proposed. The novel algorithm implements SISO configuration to acquire echo data. Such a scheme leads to faster acquisition of less data and faster reconstruction at negligible cost of image quality. It has been validated by both synthetic and experimental imaging.
A Robust Line Parameter Identification Method Based on LSTM and Modified SCADA Data

Transmission line parameters are the basis of power system calculation, and their accuracy directly affects the safe and stable operation of the system. At the same time, with the improvement of computing power and the rapid growth of the amount of power grid operation data, deep learning has developed rapidly and applied in power systems. However, there is little research on line parameter identification combined with deep learning. Therefore, from the perspective of line model and deep learning, combined with median estimation and modified Supervisory Control And Data Acquisition (SCADA) data based on line model, this paper proposes a robust method for parameter identification of transmission line based on Long Short-Term Memory (LSTM) and modified SCADA data. Speciffically, the overall process of the line parameter identification method based on LSTM is given first. Then the settings of the proposed method are introduced in detail. Combined with the line model, a modified input data based on the SCADA data is constructed, and a multi-case training set considering different operating conditions and different line parameters is established. According to a large number of tests, the optimized parameter configuration of the LSTM is given. Finally, the median estimation is used to give the parameter identification results. Case studies in simulated and measured data verify the effectiveness and robustness of the proposed method.
Target Influence on Ground Control Points (GCPs) Identification in Aerial Images

Unmanned aerial vehicles (UAVs) are used nowadays as a standard tool to derive very high-resolution geospatial data. However, UAV payload limitation imposes the use of not such reliable hardware affecting the georeferencing precision. In the literature it is possible to find numerous studies investigating the parameters influencing UAV-based products quality. Even if new photogrammetry methods could, in theory, avoid the use of ground control points (GCPs), they still playa key role to assure quality products. Nevertheless, usually only the number and distribution of GCPs are taking into account, since both change the geometric accuracy of the final products. In order to improve the understanding of the actual influence of GCPs, in this study we evaluate how can different physical characteristics affect GCPs identification in aerial images. The results demonstrate that GCPs' color, material, size and shape, among others, may influence a precise identification in aerial imagery.
Regularized Zero Forcing Beamforming for Serving More Users in Energy-Harvesting Enabled Networks

This paper considers energy-harvesting (EH) enabled networks, which serve more users by a lesser number of transmit antennas. By transmitting energy and information over different fractions of a time-slot, it employs conjugate beamforming (match filtering) to amplify the power of received signals for EH and regularized zero-forcing (RZF) beamforming to suppress the multi-user interference for information decoding (ID), maintaining the computational efficiency in the beamforming design. The problem of max-min users' information throughput optimization subject to EH constraints is addressed by a path-following algorithm, which solves a simple convex quadratic problem at each iteration to generate a better feasible point. A similar optimization problem for the simultaneous wireless information and power transfer (SWIPT) is also addressed. The provided simulations show the advantage of the former over the latter.
Oxidase-like Function by Apo-Transferrin towards Manganese(III) Chelates

We have observed that oxidation of Mn(III) ion in several manganese chelates to a Mn(IV) state occurs in the presence of apo-transferrin, and at the same time facile up-take of manganese ion by apo-transferrin proceeds. This clearly shows that much manganese ions can be transported to the brain by transferrin under the certain conditions, which may lead to manganism. Based on these results we have prepared the new chelate to capture only the Mn(III) and Mn(IV) ions selectively, but not Mn(II) ion, and to remove these manganese ions from the solution without toxicity.
Performance Analysis of Energy Beamforming over Line-of-sight Links

Performance analysis of energy beamforming with a multiple-antenna access point (AP) and a single-antenna user over line-of-sight (LOS) channels is not readily available. In this paper, we thus consider Rician channels between the AP and the user and derive the distribution of the received signal-to-noise ratio (SNR) via the distribution of the product of two independent non-central Chi-square variables. We also derive exact expressions for outage probability (OP), ergodic capacity (EC), throughput optimal energy harvesting time and bit error rates (BERs). Several approximate or asymptotic expressions are also derived. Finally, simulation results are presented to verify our analytical results.
The Method of Improving the Signal Detection Quality by Accounting for Interference

In the process of detecting the radio signal of information transmission in information systems, an urgent issue is to increase noise immunity. The article investigates the peculiarities of using low - pass filters with quadratic and linear dependence of the response on the input signal. It is shown that the principle of operation of filters is that the summation process is performed. In this case, the useful signal is summed coherently, and the interference signal is incoherent, ie the useful signal increases and the interference signal decreases.When influencing the input of linear and square filters, a rectangular pulse that mimics the signal of modern digital radio signals, the necessary parameters for further use of input and output signals are determined: mathematical expectation, correlation coefficient, variance, standard deviation.It is proved that the use of narrow-band low-pass filters in the process of signal processing allows to increase the noise immunity of the system for detection, recognition and localization of radio signals by 11%. Modeling of the process of filtering radio signals at different correlation coefficients is carried out. The simulation results confirmed the results of theoretical calculations.