Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Cooperative Relaying Based on Space-Time Block Code: System Description and Measurement Campaign

In cooperative relaying, intermediate stations are required to enhance the end-to-end transmission performance. The performance of the cooperative relaying scheme has been investigated theoretically and via computer simulations. However, cooperative relaying using transmit diversity techniques in actual environments has not been investigated thus far. This paper presents an experimental system for distributed cooperative relaying using space-time block code and evaluations of its transmission performances in real propagation channels. To this end, four wireless stations—specifically, one source, two relays, and one destination—were developed using analog transceivers and field-programmable gate arrays for real-time digital signal processing. Sample timing and frequency synchronizations among the four wireless stations were established by using the received signals as a reference. The end-to-end error performance of distributed cooperative relaying was compared to those of noncooperative relaying schemes, and the performances of three relaying schemes were evaluated quasisimultaneously in terms of their cumulative distribution functions of the bit-error ratios (BERs). The experimental results indicated that the BER performance of the two-hop distributed cooperative relaying scheme was substantially superior to those of noncooperative two-hop relaying schemes, including a route diversity scheme.
A Colocated Quad-polarized MIMO Antenna Based on Two Dual-mode Loops

In this paper, a low coupled, co-located quad-polarized (QP) vector antenna consisting of two orthogonal, dual-mode loops is proposed. This QP antenna has the radiation characteristic of two electric dipoles and two magnetic dipoles, which are co-located orthogonal. Simulation results show that the four polarization modes resonate at 2.45 GHz, with the mutual coupling lower than −14.5 dB.
Locomotion Strategies for Amphibious Robots-A Review

In the past two decades, unmanned amphibious robots have proven the most promising and efficient systems ranging from scientific, military, and commercial applications. The applications like monitoring, surveillance, reconnaissance, and military combat operations require platforms to maneuver on challenging, complex, rugged terrains and diverse environments. The recent technological advancements and development in aquatic robotics and mobile robotics have facilitated a more agile, robust, and efficient amphibious robots maneuvering in multiple environments and various terrain profiles. Amphibious robot locomotion inspired by nature, such as amphibians, offers augmented flexibility, improved adaptability, and higher mobility over terrestrial, aquatic, and aerial mediums. In this review, amphibious robots’ locomotion mechanism designed and developed previously are consolidated, systematically The review also analyzes the literature on amphibious robot highlighting the limitations, open research areas, recent key development in this research field. Further development and contributions to amphibious robot locomotion, actuation, and control can be utilized to perform specific missions in sophisticated environments, where tasks are unsafe or hardly feasible for the divers or traditional aquatic and terrestrial robots.
Green chemistry in China

The principles of green chemistry provide a framework for rational design of environmentally friendly chemicals and chemical processes having reduced intrinsic hazard. In China, the academic community has made advances in key areas, developing new catalysts, solvents, polymers, plastics additives, and biomass transformations that add to the “toolbox” of alternative, more benign, and transformative technologies. Educational and outreach activities have also flourished in recent years. This perspective highlights examples of green chemistry breakthroughs in China, focusing on literature reports from 2008 to 2010. While we cannot hope to be comprehensive, we aim to provide examples that demonstrate the scope of the current research field.
Soil Moisture Estimation Based on the AIEM for Bare Agricultural Area

An AIEM based soil moisture retrieval strategy for bare agricultural area was developed in this study. In order to solve the ill-posed problem for soil moisture retrieval, multi-sensor SAR observations were integrated to estimate soil moisture based on the AIEM simulation. Soil surface parameters were derived using the least square method. The influence of surface roughness for soil moisture estimation was removed. Based on the in situ measurement and multi-sensor SAR images acquired on October 2015 over agricultural area, quantitative evaluation of the developed method was implemented. The results indicate that accurate soil moisture was obtained over the study area. The root mean square error of the estimated result is less than 0.05 cm3/cm3.
Multi-area Peer-to-Peer Energy Trading

With the influx of third-party capitals, the traditional centralized operation problem is transformed into multi-area autonomous optimization problems charged by multiple aggregators, and the multi-area energy trading market is springing up. Considering different self-interests and collaborative relationships of multiple aggregators, the multi-area peer-to-peer (P2P) energy trading mechanism is proposed in this paper. First, a bi-level hybrid architecture of the distributed P2P energy trading market is constructed. Then, a multi-area P2P energy trading algorithm is proposed for the trading negotiation. Furthermore, the relay transaction form is proposed for the energy trading between aggregators without trading connection. Finally, the proposed methodology is implemented and benchmarked on an actual urban distribution system. The effectiveness of the proposed method is verified in terms of promoting the consumption of renewable energy sources (RES) and improving multi-area economic benefits.
A Deep Learning-Based Method for Automatic Detection of Epileptic Seizure in a Dataset With Both Generalized and Focal Seizure Types

Epilepsy is the second most popular neurological disorder affecting 65 million people around the world. Seizures are classified into two kinds; focal and generalized ictal activities, reflecting the spread of seizure activity on the brain. Focal seizures start and affect specific regions of the brain, whereas the generalized propagates throughout the brain. Current approaches to developing an automatic seizure detection algorithm do not consider the types of seizures. However, to detect the focal seizures, the locations of onset of seizure must be identified by an expert through inspection of the electroencephalogram (EEG), which is an expensive and time-consuming procedure. Moreover, most proposed methods are patient-specific and cannot be generalized on an unseen patient, limiting the clinical usage of previous studies. This work presents a generalizable seizure detection algorithm by considering different seizure types. After pre-processing data and rejecting artifacts, a deep neural network is used to extract robust representations across seizures and a population. The proposed method includes deep recurrent and convolutional neural networks to capture spatial and temporal information simultaneously. Experiments on the TUH EEG seizure dataset, which contains both generalized and focal seizures, show that the proposed method increases the accuracy over state-of-the-art from 80.72% to 82%, precision from 67.55% to 71.69%, and sensitivity from 80% to 85%.
Malicious users control and management in cognitive radio networks with priority queues

Malicious users (MUs) have the tendency to disrupt the activities of honest users in the network if not properly controlled. In a massive cognitive radio network (CRN) with priority queues, malicious secondary users (SUs) can manipulate their priority queue requirements and mislead legitimate SUs to vacate the channels. In this paper, a game theoretic based signal detection approach is proposed to control the presence of MUs in CRN. If the received signal strength is less than the predefined threshold for primary transmissions in the presence of interference and noise, such a user is marked to be malicious and its payoff table is updated. Through the mixed strategy Nash equilibrium method, the payoff table of each user can be updated to aid removal of MUs from the network. The outcome of the simulation results shows that such an approach can reduce the impact of malicious activities in the massive CRN where SUs are expected to be low-power energy-efficient devices.
Multi-mode phase slots antenna with dual ports

The multi-mode phase slots antenna with dual-port is proposed. The whole size of the antenna is smaller than half wavelength, and these two ports can work simultaneously with high isolation. By varying the phase difference between these two ports, the main beam direction of the proposed antenna can scan from $-59^{\circ}$ to 59^{\circ}$ in the E plane with a gain fluctuation about 2 dB during 1.45GHz to 1.57GHz.
Analysis of Outage Probability and Duration in Millimeter Wave Vehicle-to-Infrastructure Networks

We consider vehicle-to-infrastructure (V2I) type communication over millimeter wave (mmWave) frequencies and evaluate its performance limitations in terms of coverage. Specifically, we analyze the outage probability and average outage duration for a communicating vehicle exchanging data with roadside units (RSUs), where the system performance is limited by the path-loss that the intermediate links experience. The path-loss depends on the length of the link, as well as the number of vehicles blocking the line-of-sight (LOS) path. By using a Markov chain formulation and its steady-state solution, we obtain analytical expressions for the outage probability and average outage duration. After demonstrating that the durations of outage events constitute a greater limitation on the system performance than outage probability, we examine two approaches to mitigate this limitation: increasing the RSU density and increasing the path-loss threshold for munication.
Advanced Switching Technique based High Frequency Magnetic Linked Asymmetric Multistring Inverter with Reduced THDs

This paper suggests an advanced modulation technique for the enlargement of the performance of high frequency magnetic linked asymmetric multi-string multilevel inverter. This proposed modulation technique enhances the output voltage quality by reducing total harmonic distortion. Moreover, it reduces the switching losses and enhances the efficiency of the power inverter. To ensure quality performance, recently published PWM and Proposed PWM topologies are used for this multilevel inverter having a carrier frequency of 1-5 kHz and a modulation index of 0.8-1. The total work is verified by using the MATLAB/Simulink software. From the simulation results and comparative study, the advanced switching technique is advantageous compared to all the other topologies for the modulation index 1. Also for all frequency modulation indexes, the proposed topology offers better results. The proposed technique lessens almost 20.80 %, 33.78 %, and 33.33 % of THD than the third harmonic injected thirty-degree bus clamping PWM (THTDBCPWM), conventional space vector pulse width modulation (CSVPWM), and third harmonics injected pulse width modulation (THPWM) technique respectively. The switching losses of the proposed switching technique are lower than all other techniques and almost 2.58 % less to the THPWM. The proposed technique has extraordinary potentiality for industrial applications and a sustainable solar PV system.
Design of an Optimized Space Diversified Antenna Arrays for Extended FM-RF Propagation

Most Frequency Modulation broadcast stations are stacking antennas in order to extend the radiation of energy and propagation of signal. This paper presents a design of an optimized shunt-fed FM broadcast antenna also with a circular polarity for weather infiltration and no constraint of the orientation of transmitter and receiver. The method primarily includes the optimization of the relative spacing of dipoles to exhibit a circular azimuth pattern, axial ratio requirement for circularly polarized antenna and the gain in far field. The circular pattern shaping method uses a uniform linear array of two elements and the principle of array multiplication. Two control factors are being utilized to shape the radiation pattern as follows: the relative distance spacing, separating the two elements and their excitation phase difference. In the optimization process, a more accurate and consistent solutions like Global Response Surface Method was used. GRSM yields the most optimal objective value and it follows to determine the optimum values of the parameters in the current design. Results show that the pattern produced by the proposed method can resemble a circular form and exhibited an increased in antenna gain as compared to the commonly used shunt-fed slanted V dipole antenna. The results of the optimized design of a FM antenna and its characteristics are validated using an antenna simulation software.
Hybrid Beamforming Design for C-RAN Based mmWave Cell-Free Systems

This paper considers the cloud radio access network (C-RAN) based millimeter-wave (mmWave) cell-free communications, where multiple remote radio heads (RRHs) are distributed to provide reliable communication links to users via analog beamforming and connected to centralized baseband unit (BBU) which carries out digital signal processing. We aim to jointly design the user association and analog/digital hybrid beamforming along with fronthaul compression to maximize the minimum signal to interference-plus-noise ratio (SINR) among users while satisfying the fronthaul capacity constraints. To solve this difficult combinatory problem, we propose to first obtain the user association and analog beamforing to maximize the minimum beamforming gain among users. Then, given the effective baseband channel, the digital beamformer and quantization noise covariance matrix still cannot be calculated directly due to the non-convexities of objective function and fronthaul constraint. To efficiently solve this problem, we transform the objective function into convex terms based on fractional programming method and iteratively calculate the digital beamformer and quantization noise covariance matrix until convergence is achieved. Simulation results show that the proposed algorithm can achieve comparable performance to the full-digital beamforming.
Fusion-Oriented Aircraft Detection in Large Scene Image Based on Tiny Darknet

High resolution and large field of view in large scene images ($> 10000\times 10000\text{pixels}$) provide rich detail information and wide viewing angle. However, the computational complexity is also drastically increased. Small object detection in large scene remote sensing images is always one of the nontrivial problems. Generally, the body length of aircraft in large scene images is only 30∼80 pixels at 0.5m or 1m resolution. While convolutional neural networks (CNN) have made great breakthroughs in natural images, the fast and accurate identification of small objects has not been effectively tackled. In order to improve accuracy, this paper proposes a two-stage Fusion-oriented aircraft detection model (FOADM) and uses an adjusted detection network model Tiny Darknet based on Darknet53 to adapt for the detection of small aircraft. In the first stage, we combine Line Segment Detection (LSD) with Graph-Based Visual Saliency (GBVS) to acquire the region of interest (ROI) in downsampled images, which can reduce the worthless search to some extent. Then, in original high resolution image, the ROI is located and partitioned. In the second stage, each block is fed into the detection network Tiny Darknet for aircraft detection. Experiments demonstrate the proposed method can effectively improve the recall of detection result in high resolution.
Virtual Power Plant Trading Strategy Based on Block-chain to Satisfy Clean Energy Partiality

The Block-chain-based virtual power plant can realize the coordination and integration of distributed energy resources in accordance with peer-to-peer energy transactions. In particular, the smart contract function of the Block-chain can assist energy transactions in the virtual power plant and can enable the local network to meet the clean energy partiality. In order to achieve the above functions, firstly, a virtual power plant transaction model based on smart contracts was constructed, including energy index, energy transaction partiality, and physical constraints. Secondly, a pre-distributing strategy for energy transactions was proposed with smart contracts designed based on the Minimum Cost Programming, using the automatic settling function of smart contracts to omit energytrading transfer stations and to achieve weakly-centralized energy peer-to-peer transactions. Finally, a building virtual power plant was established as a transaction scenario with a typical distribution network topology. The simulation results illustrated that the strategy could satisfy the clean energy partiality of energy transactions to a certain extent, and it could realize weakly-centralized transactions by Block-chain.
Three-Phase Split Delta-Source Inverter: Operating Principles and Modulation

Three winding coupled inductors have been widely used in impedance-source inverters, such as Y-source inverters and delta-source inverters. They can improve the boosting capability of the inverter without adding active switches. However, their boosting coefficients decrease with the increase of the modulation, which will cause the voltage quality and utilization of the DC bus to be greatly reduced under high-voltage gain. Besides, non-negligible leakage inductance exists in this type of impedance source inverter, which will cause a sudden change in the induced current in the impedance source network and produce a larger voltage spike. So it is necessary to introduce buffer or absorption circuits. In order to overcome these limitations, this paper proposes a split $\Delta$-source inverter. Its boosting capability increases with the increase of the modulation. At the same time, there is no voltage spike under the existence of non-negligible leakage inductance and no absorb-circuit. This paper verifies the performance of the inverter from three aspects: theoretical analysis, simulation, and experimental results.
Utilizing of Flower Pollination Algorithm for Brushless DC Motor Speed Controller

Currently, brushless direct current (BLDC) motors are becoming popular in electric vehicles, automation systems and industrial applications. Basically, the BLDC motors are a special type of electronically controlled electric motor systems. In fact, the successful advancement of electronic technology has made these motors a reality. Without an electronic controller, it is impracticable to run this motor. Thus, it is easily conceivable that the motor controller plays an important role in the overall performance of BLDC motors. Many types of controllers have already been developed for these motors. However, poor efficiency, complexity, bulky size, slow response time, etc. are still challenging tasks to solve these problems. Various algorithms are proposed for designing the controller. Proportional Integral Derivative (PID) controller is the most common control system for the BLDC motor. If the PID controller is modified with a new algorithm, better results can be obtained from the BLDC motor. In this study, a Flower Pollination Algorithm (FPA) has been proposed for the controller to control the speed of BLDC motor. The best outcome of this research is stable is speed and reduce rising time. This study was done by using MATLAB/ Simulink software. The results show a new direction in the improvement of the controller design.
Improved Genetic Algorithm for Bundle Adjustment in Photogrammetry

In this work, a Constraint Law method (CLM) based Genetic Algorithm (CLM-GA) is proposed for Nonlinear Least Square (LS) Regression and Normal Optimization Problems (NOP). Numerical experimental results show that CLM-GA is more robust than traditional methods (Gauss Newton, Levernberg Marquardt, EM-GA, EDBF-GA) for both LS and NOP problems in three aspects: 1) efficiency; 2) accuracy; 3) astringency. There are many LS and NOP applications in the physical world, such as military, economics, industry, photogrammetry and so on. Research of this paper can be easily implemented and applied for those applications.
EPDC: An Enhanced Pipelined Data Collection MAC for Duty-Cycled Linear Sensor Networks

Duty-cycling techniques have been widely adopted to save energy for energy-constrained wireless sensor networks, while they also cause the sleep latency issue, especially in a multihop linear sensor network (LSN). So the duty-cycling and pipelined-forwarding (DCPF) techniques have been proposed to alleviate this issue. However, most of existing DCPF protocols have no effective scheme to handle the contention and interference among those proximately-located nodes which maintain the same sleep-wakeup schedule. As a result, the network performance degrades with low energy efficiency and high packet delivery latency, particularly when experiencing a heavy traffic load. To this end, this paper proposes an enhanced pipelined data collection (EPDC) MAC protocol for LSN. In EPDC, three algorithms are proposed to guarantee that those nodes located within the interference range of each other have mutually staggered sleep-wakeup schedules, so that the contention and interference among them can be eliminated. The extensive OP-NET simulations show that EPDC significantly outperforms an existing DCPF protocol in terms of packet delivery ratio, network throughput, packet delivery latency, and energy efficiency.
Scalability Analysis of Context-Aware Multi-RAT Car-to-Cloud Communication

Nowadays always-connected vehicles create opportunities for novel services like vehicles serving as highly mobile sensor platforms in the Internet of Things context. Hereby, cars upload and transfer their sensor data readings via a mobile communication network into the cloud. Data is shared in the cloud via vehicle big data marketplaces. As wireless resources are limited and shared by all users, data transfers need to be conducted efficiently. The ongoing growth of generated data inside vehicles and thereby the increasing data upload demand becomes more and more challenging to Mobile Network Operators.Within the scope of the work, an opportunistic Multi-Radio-Access-Technology (Multi-RAT) uploading approach is proposed in order to conduct those data uploads faster and more efficiently. This approach leverages multiple network interfaces and combines them to one unified communication link. Data uploads can be delayed to exploit good channel conditions and avoid uploads when the channel quality is insufficient. The simulative results and evaluations highlight that the proposed method is well-suited for vehicular data uploads. Even though a trade-off in the age of information is created, the throughput is increased. Besides, better performance in spectral efficiency is achieved in comparison to traditional single-link transmissions.