Miracle-dz/UniFAR-FTUs huggingface dataset:
A High Q Dual E-Shaped Defected Ground Structure for Wireless Power Transfer Applications

Dual E-shape Defected Ground Structure (DGS) resonator coupled back to back for medium range non-radiative Wireless Power Transfer (WPT) is presented in this paper. The proposed design is based around the concept of resonant inductive coupling for power transfer and consists of E-shaped defect etched in the ground plane of the substrate. The design incorporates capacitive loading for miniaturization and is supported by analytical design procedure. An equivalent parallel RLC resonant circuit in conjunction with coupled mode theory (CMT) and S-parameters aids in the understanding of the proposed WPT system. A prototype, on Rogers RO4350B substrate, has been developed to operate at 0.27 GHz frequency and takes a board size of $20 \times 20\text{mm}^{2}$. A very good agreement between the simulated and measured results demonstrate the effectiveness of the proposed technique. The achieved performance significantly improves the efficiency of the DGS based WPT systems reported so far.
A Game-Theoretic Approach for Fair Coexistence Between LTE-U and Wi-Fi Systems

LTE over unlicensed band (LTE-U) has emerged as an effective technique to overcome the challenge of spectrum scarcity. Using LTE-U along with advanced techniques such as carrier aggregation, one can boost the performance of existing cellular networks. However, if not properly managed, the use of LTE-U can potentially degrade the performance of co-existing Wi-Fi access points, which operate over the unlicensed frequency bands. Moreover, most of the existing works consider single operator in their proposals. In this paper, an effective coexistence mechanism between LTE-U and Wi-Fi systems is studied. The goal is to enable the cellular network to use LTE-U with CA to meet the quality-of-service (QoS) needs of its users while protecting Wi-Fi access points (WAPs) for a network with multiple operators. In particular, the problem of LTE-U sum-rate maximization is addressed under user QoS and WAP-LTE-U coexistence constraints. To solve this problem, a cooperative Nash bargaining game (NBG) and a one-sided matching game are proposed. Here, the NBG solves the coexistence issue between LTE-U and Wi-Fi system, while the matching game solves the resources allocation problem in the LTE-U system. These two games repeat until convergence. Simulation results show the quality of the proposed approach over other comparing methods in terms of the per-user achieved rate, percentage of unsatisfied users, and fairness. The result also shows that the proposed approach can better protect the performance of Wi-Fi users, compared to the conventional listen-before-talk scheme.
A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

This paper proposes an observer for generating depth maps of a scene from a sequence of measurements acquired by a two-plane light-field (plenoptic) camera. The observer is based on a gradient-descent methodology. The use of motion allows for estimation of depth maps where the scene contains insufficient texture for static estimation methods to work. A rigourous analysis of stability of the observer error is provided, and the observer is tested in simulation, demonstrating convergence behaviour.
Robust Attitude Control based on Continuous Differentiator and Adaptive Super Twisting for 3 DOF Quadrotor

A quaternion-based spacecraft model is used in this paper to design a nonlinear control law based on the adaptive second order sliding mode Super Twisting algorithm. The robust controller combined with the quaternion representation is applied to an unmanned aerial vehicle for attitude tracking and stabilisation in presence of external disturbances, parameters uncertainties and singularities. The stability and finite time convergence are proved through Lyapunov function candidate. The experimental results related to a real-time quadrotor plateform are included to show and demonstrate the effectiveness of the quaternion-based control.
A Hybrid Method for Dissimilarity Analysis between Short Text Documents

Similarity analysis is an extremely popular aspect of Natural Language Processing (NLP). Most of the existing works focuses on analysing content of large documents. There are comparatively a smaller number of researches available which focuses on analysing similarity between short unstructured documents. This work proposes a hybrid approach which uses WordNet Path vector cosine angle analysis and Dice co-efficient overlap level analysis to determine the similarity levels of short texts. A regression model is used to dynamically weight and combine the calculated two individual scores into a single score. This hybrid approach was found to have significantly higher accuracy rates against Term Frequency_Inverse Document Frequency (TF-IDF) and Dice co-efficient techniques.
Codes Correcting Two Deletions

In this paper, we investigate the problem of constructing codes capable of correcting two deletions. In particular, we construct a code that requires redundancy approximately <inline-formula> <tex-math notation="LaTeX">$8 \log _{2} n + {\mathcal{ O}}(\log _{2} \log _{2} n)$ </tex-math></inline-formula> bits of redundancy, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> denotes the length of the code. To the best of the authors’ knowledge, this represents the best known construction in that it requires the lowest number of redundant bits for a code correcting two deletions.
A Torque Compensation Control Scheme of PMSM Considering Wide Variation of Permanent Magnet Temperature

This paper presents a torque control method that compensates the reduction of the power output of a permanent magnet synchronous motor (PMSM) caused by the wide variation of rotor-mounted permanent magnet temperature. The characteristics of the permanent magnet according to temperature were analyzed, and the need for motor parameter estimation was identified. Numerical and computational techniques using the steepest descent method and the affine projection algorithm were used for online motor parameter estimator. The estimated parameters were used to achieve a torque compensation control scheme based on field-oriented control of PMSM considering the wide variation of permanent magnet temperature. The proposed control scheme was verified by the dynamometer test of the test motor under each rotor-mounted permanent magnet temperature condition.
Bit Rate and Task Scheduling in Cloud Computing for Multimedia Big Data

As video traffic increases with plentiful multimedia services and the proliferation of mobile devices such as smartphones, stream mining to extract valuable information out of multimedia big data is garnering attention. By applying cloud computing to stream mining, resource-scarce mobile devices can offload the workloads of heavy applications to a remote cloud. However, resource provisioning for task scheduling is an inherent challenge of stream mining in cloud computing. In this paper we consider problem of resource provisioning and bit rate scaling for multimedia big data processing. We aim to minimize the virtual machine (VM) leasing cost and the classification error cost while satisfying the deadline constraints of workloads which is formulated as a mixed integer nonlinear programming. Deadline based task scheduling and bit rate scaling are developed to find near optimal solution of the NP-hard problem. The upper and lower bounds of the required number of VMs are obtained for infeasible and feasible schedules respectively. Scaling down the highest bit rate first in the bit rate set of a workload is suggested to guarantee the minimum increase of error cost. Our simulation results show the efficiency of bit rate scaling in task scheduling. 5–10% cost reduction is achieved by bit rate scaling in a cloud computing environment.
Portfolio Optimization Considering Diversified Investment Methods Using GNQTS and Trend Ratio

In the stock selection problem, the Sharpe ratio is one of the commonly used indicators, but it tends to identify the portfolio with a flat trend as the best one. This paper uses the trend ratio to access the portfolio with a stable upward trend. By the portfolio trend line with initial funds, the trend ratio can simultaneously consider the daily expected return, daily risk and fairly compare with the different portfolios and different investment periods lengths. In addition to the access indicator, this paper provides diversified investments such as time deposit, buying round lots or buying odd lots. Different situation suits different investment method. Therefore, this paper applies the 2-phase investment sliding windows to avoid the overfitting problem and chooses the best investment method by multiple training using Global-best Guided Quantum-inspired Tabu Search with Not Gate (GNQTS) to find the best portfolio effectively and efficiently. Furthermore, the experimental results show that the proposed method can find the well-performing portfolio with higher return and lower risk in both the training and testing periods.
CHaChA: Clustering Heuristic and Channel Assignment for IEEE 802.11s Mesh Networks

WLAN mesh networks are one of the key technologies for upcoming smart city applications and characterized by a flexible and low-cost deployment. The amendment IEEE 802.11s (.11s) introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by management traffic overhead, routing delays, medium contention, and interference are common issues in wireless mesh networks and also apply to. 11s networks. Possible solutions proposed in research recommend a divide-and-conquer scheme that partitions the network into clusters and forms smaller broadcast and collision domains by assigning different channels. We present CHaChA, a distributed cross-layer approach for clustering and channel assignment that directly integrates the default. 11s mesh protocol information and operation modes, retaining unrestricted compliance to the standard. The practical performance and implied benefits of CHaChA are demonstrated in a real-world testbed.
Cartesian Genetic Programming with Module Mutation for Symbolic Regression

Symbolic regression aims to find mathematical expressions of functions that can fit a finite set of given data. This problem is a typical problem for evaluating performance in the field of Genetic Programming (GP). Cartesian GP (CGP) is one of the extensions of GP, which generating the graph structure programs. By using the graph structure, the solutions can be represented by more compact programs. Therefore, CGP is widely applied to the various problems. In standard symbolic regression problem, the sample data is expressed by a simple function, which is continuous and smooth. On the other hand, In a complex system appearing in the real world, they can be produced by a discontinuous or non-smooth function. When conventional GP or CGP is applied to this complex system's modelling, it is difficult to obtain good performance. In this paper, we propose a new CGP framework for complex symbolic regression problem. The proposed CGP modularizes the output nodes, and module mutation is introduced to increase or decrease the number of modules during the search. Each module consists of a node corresponding to the output of the network and a node for selecting the output to be used. By switching the output node for each input, it is possible to output appropriate values for each section of the objective function. We have examined its effectiveness by applying it to symbolic regression problem where the objective function is divided into several different sub-function fields. Experimental results have shown that it outperforms conventional CGP.
Detecting Abnormal Events Using Locality-Sparse Tensor Reconstruction on Covariance Descriptors

Abnormal event detection in videos is to find spatially or temporally isolated feature points that do not conform to a pre-defined notion of normal ones. Because the covariance descriptors can naturally fuse appearance and motion statistics, resulting in a much smaller dimensionality and compact representation than vector-based features. In this paper, we employ it as event representation, to capture the anomalies that different from normal patterns in appearance and motion simultaneously. Motivated by the application of tensor sparse coding, we propose a novel Locality-Sparse Tensor Reconstruction Model (LTR) to learn the covariance feature distribution for video events. The locality-sparse scheme makes the sample reconstructed only from its neighbourhoods, which bears much less computational complexity and obtain more robustness. To efficiently adapt new video data streams, we employ an online updates for the model. The proposed approach is evaluated on several publicly available datasets and outperforms several methods based on vector feature representation proposed before.
Fitness-Based Acceleration Coefficients to Enhance the Convergence Speed of Novel Binary Particle Swarm Optimization

Acceleration coefficients are the key parameters of particle swarm optimization (PSO) algorithm used to control the movement of particles by modifying its cognitive and social components. Several variants have been proposed that modify the acceleration coefficients to improve the convergence speed of PSO in continuous search space. In this regard, a few attentions have been paid to improve the convergence speed of binary particle swarm optimization (BPSO). Moreover, in presence of distinct position of particles by ignoring the dispersion of particles in a search space, BPSO deals all particles equally. To address this issue, we have proposed a fitness-based acceleration coefficients Novel BPSO, called FAC-NBPSO. In the proposed algorithm, the fitness of each particle is used to modify the cognitive and social components of each particle. The performance of the proposed algorithm is tested on four benchmark test functions. The experimental results show that the proposed algorithm performs better than the compared algorithm with improved convergence speed.
Design and Implementation of Transmitting and Receiving Antenna Pair on Flat Ring Surface

In this work, design and implementation of broadside altimeter antenna on circular ring surface are performed. Utilizing array factor formulation, possible locations for circular array elements are chosen. Slitted microstrip patch antennas are used for an efficient design. Satisfactory results are obtained for an altimeter antenna that can be utilized in missile, aircraft or spacecraft applications.
Low Power 20.625 Gbps Type-C USB3.2/DPl.4/ Thunderbolt3 Combo Linear Redriver in 0.25 μm BiCMOS Technology

PCB traces have high-frequency losses due to skin effect and dielectric losses. These high-frequency losses results in inter-symbol Interference, ISI, as the signal passes through PCB traces. A linear analog equalizer, linear redriver, removes the ISI which results in a longer signal reach. A linear redriver operating up to 20Gbps can support signaling as specified in USB Type-C and VESA Display Port Alt Mode, Alt mode standards and the USB3.2 specification. It consumes 90mW from a 1.8V supply. It has programmable peaking gain up to 17 dB at 10.3 GHz with 1 dB steps to compensate for the channel loss and a programmable 1-dB compression point of 500 to 950 mV-ppd. This redriver achieves low return loss, better than −16 dB up to 10.3 GHz and low cross-talk, better than −50 dB up to 10.3 GHz. It has been implemented in NXP BiCMOS Technology with/fr~ 170 GHz and consumes 46 rnA per channel from a 1.8 V supply.
An Efficient Authentication Scheme for Privacy-Preserving in Secure Vehicular Communications

VANETs can significantly improve traffic management and driving safety. It has attracted great attention of government departments, academia and industry circles. To perform cooperative operations, vehicles should broadcast traffic information to RSUs and other vehicles periodically. Unfortunately, the massive messages should be digitally signed and verified in a very short time to the real-time constraints. To deal with this issue, we propose an efficient anonymous authentication scheme, in which RSU becomes the message center that can establish the session relationship with the surrounding vehicles and assign temporary identities to vehicles. Using the temporary identity and session key, every vehicle is only responsible for transmitting messages to RSU, the latter for collecting and validating messages from vehicles and broadcasting the information verified. To obtain traffic information, any vehicle only need verify the broadcast messages from RSU. In addition, the proposed scheme can provide anonymity in the RSU's coverage. The security and performance analysis illustrates that the proposed scheme can effectively protect the user's security and can provide fast authentication.
A Neural Network based Digital Forensics Classification

Digital forensics (DF) turn out to be an urgent and a timely subject to investigate with the significant rise in computer crimes these days. Typically, DF analysis aims to maintain all collected evidence in its most original status by determining, collecting, and evaluating the digital data for re-constructing past incidents. Most evidence related to digital crime are kept within the computer system files. This article explores and evaluates the applicability of Neural Network techniques in DF analysis by analysing information related to computer's file system to determine whether they have been manipulated by a specific application program. A data set described as a vector of attributes related to file system activities thru a specific time is collected and utilized for creating a neural network classification model. The experimental results show good results with respect to different performance estimation measures.
Freezing of Gait Detection Considering Leaky Wave Cable

A novel study on monitoring and analysis of the debilitating condition of patients suffering from the neurological disorder is presented. Parkinson’s disease is characterized by limited motor ability of a patient. Freezing of gait is a major nonmotor condition among aging patients and its evaluation can reduce the chances of any secondary disorders. In this paper, amplitude and phase information of the radio signals observed for a fixed period of time are used to differentiate the motor and nonmotor symptoms. The amplitude information is classified using a support vector machine, while the linear transformation is applied to obtain sanitized phase information for detection. The proposed method is very handy with the minimum deployment overhead. The analysis shows that this method also offers a high-accuracy level of around 99% based on the observation of a number of patients. These features make it an attractive solution for real-time patient monitoring systems.
A Novel Half-Rate Dual-Response Phase Detector Implementation for a 25-28.3 Gb/s Clock and Data Recovery Circuit

The implementation of a novel half-rate dual-response phase detector, generating both linear and binary responses concurrently, is presented. Processing both binary and linear responses to drive a single on-chip voltage-controlled oscillator (VCO), lower jitter generation and higher jitter tolerance are attained simultaneously, providing a superior jitter performance. The phase detector is measured in a 25-28.3 Gb/s clock and data recovery (CDR) circuit with 7.75ps peak-to-peak total jitter at 1.0E-12 bit error rate (BER). The jitter tolerance is twice better than the optical module required tolerance mask over the entire frequency range. The chip is fabricated in 200GHz/280GHz (fT/fMAX) IBM 8HP $0.13\mu\mathrm{m}$ SiGe BiCMOS process.
OpArray: Exploiting Array Orientation for Accurate Indoor Localization

Signal processing on antenna arrays has recently received extensive attention in the area of angle-of-arrival (AoA)-based indoor localization. Although sufficient array elements can improve the resolution in the AoA estimation, the array orientation has not been well exploited in research into the localization performance. In this paper, we investigate the effect of array orientations on the performance of AoA-based indoor localization systems. Appropriate array orientation can efficiently reduce the uncertainty in AoA estimation, thereby improving the localization accuracy. Accordingly, we present OpArray, an accurate indoor localization system based on flexible array deployment. First, OpArray designs an array deployment scheme, which establishes the foundation for accurate AoA estimates. The deployment scheme can be easily implemented through array rotations so as to optimize array orientations at receivers. Second, OpArray incorporates two refined phase preprocessing algorithms to mitigate the impact of negative factors, which exist in the practical implementation. In addition, aided by an improved AoA estimation algorithm, OpArray can localize a target on commercial off-the-shelf Wi-Fi platforms. Our experiments in a multipath-rich indoor environment show that OpArray achieves a median localization error of 0.5 m and the 80th percentile error is 1.0 m, which outperforms the state-of-the-art localization systems.
Indoor Navigation with Human Assistance for Service Robots Using D*Lite

Service robots aim to help humans in their daily tasks at home or office. To accomplish some of these tasks, robots must be able to navigate intelligently from one location to another in dynamic indoor environments. The indoor navigation is a challenging task as many moving and movable objects can be found in the way, blocking the robot's path. D*Lite is a path planning algorithm that replans more efficiently in case the environment changes. However, depending on how blocked the robot is, replanned paths can be much longer than the original or, in the worst case, the target location can not be reached anymore. Therefore, we introduce an adaptation to D*Lite which allows robots to ask for human assistance to move objects on its surrounding when its path is blocked. The algorithm also computes whether and how long the robot can wait for help. Experiments were carried out using simulation and a real robot.
Microwave and Millimeter-Wave GaN Amplifiers for Satellite Communications

Satellite communications becomes more interesting because of an increasing number of satellite launches for especially Ku-band very small aperture terminals (VSAT). GaN amplifiers play a major role for the systems in lowering the costs by replacing the widely used travelling wave tube amplifiers (TWTAs). Recently, the systems specifications push GaN amplifiers towards millimeter-wave operations such as Ka-band. In order to continue the commercial deployments of the GaN amplifiers, there are many challenges. This paper presents a variety of high power and high efficiency GaN amplifiers at Ku-and Ka-bands.
Distributed Dimensionality Reduction Fusion Estimation for Cyber-Physical Systems Under DoS Attacks

This paper studies the distributed dimensionality reduction fusion estimation problem for a class of cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. The problem is modeled under the resource constraints (i.e., bandwidth or energy) for the defender and attacker. Based on a new attack and compensation model, a recursive distributed Kalman fusion estimator (DKFE) is designed for the addressed CPSs. Though the optimization objects of the defender and attacker are opposite, the corresponding optimization problems are established based on different available information. In this case, an explicit form of suboptimal dimensionality reduction is given against DoS attacks, while an effective attack strategy is proposed for the attacker. A stability condition is derived such that the mean square error of the designed DKFE is bounded. Two illustrative examples are given to show the effectiveness of the proposed methods.