Miracle-dz/UniFAR-FTUs huggingface dataset:
Crisscross Optimization Algorithm for the Designing of Quadrature Mirror Filter Bank

During designing of two channel Quadrature Mirror Filter(QMF) bank, there occurs many errors such as peak reconstruction error, error in pass band, error in stop band, error in transition band and amplitude distortion. There are many meta-heuristic algorithms for the minimization of various errors of two channel QMF banks but they have drawbacks that either their convergence speed is slow or they have poor solution accuracy or premature convergence. In this paper, a concept of the Crisscross Optimization method for minimizing designing error of two channel linear phase QMF bank in frequency domain is introduced. This problem is taken as single objective function. The crisscross optimization algorithm makes use of two important operators that are horizontal crossover (HCX) and vertical crossover (VCX). The HCX operator works by dividing the problem space into simple hypercube with smaller search probability while VCX operator makes a way out for the stagnant dimensions which may be indifferentiable and cause premature convergence. The simulation results are compared with the modified particle swarm optimization, artificial bee colony, XSG and Levenberg Marquart method. From the comparison it is obvious that convergence speed and good solution accuracy.
Research on Speech Enhancement Algorithm Based on SA-Unet

Most of the traditional speech enhancement algorithms are studied in the speech and audio domain. However, the suppression of noise by these methods is not obvious, and the effect of extracting pure speech is not good. In recent years, deep learning has been effectively developed based on its strong learning ability and is being used by more and more researchers. In view of this, this paper proposes a deep learning network model for research in the time domain, which is a SA-Unet network model that combines self-attention and Unet network structure and uses it for speech separation tasks. The model adds a self-attention mechanism between up-sampling and down-sampling in the Unet structure to enhance the perception between contexts, thereby more accurately separating the noise portion of the speech. Finally, the speech quality assessment scores are objectively obtained through the speech quality evaluation indicators and compared with the traditional speech enhancement algorithms. Experiments prove that SA-Unet has made outstanding contributions to speech enhancement technology.
Prediction of Indoor Wireless LAN Field Strength Distribution Based on Ray Tracing Method

Since the indoor propagation environment is complex, it is difficult to accurately establish the signal multipath propagation model, which affects the accuracy of indoor wireless LAN field strength resolution. This paper improves the indoor wireless LAN signal multipath model by using the ray tracing method to simulate the real wave propagation more accurately. The path is computed by the indoor environment modeling, and compared with the measured data, the average error is within 2dB, which proves the effectiveness of the ray tracing method in solving the field coverage problem of indoor wireless LAN.
Design, Characterization, and Mechanical Programming of Fabric-Reinforced Textile Actuators for a Soft Robotic Hand

In this paper, we present the design, fabrication, and evaluation of robust, fabric-reinforced textile actuators, which are capable of performing a variety and combination of motions, such as axial extension, radial expansion, bending, and twisting along its central axis. A simple fabrication procedure using a combination of lamination and sewing is described. The relationship between the fabric reinforcement characteristics and the actuator deformation is studied and experimentally verified. Multi-segment actuators can be created by tailoring different sections of fabric-reinforcements together in order to generate combination of motions to perform specific tasks. We demonstrate this by designing an anthropomorphic soft robotic hand and providing preliminary evaluations on grasping daily living objects of various size and shapes.
Design and Evaluation of Decentralized Scaling Mechanisms for Stream Processing

Over the last decade, stream processing (SP) gained momentum as an efficient paradigm dealing with the real-time processing of large streams of data. As mature as they are, mainstream stream processing engines are not ready to be used over more distributed platforms that are today emerging such as Fog platforms. Streams being produced at the edge of the platform, and edges being composed of less powerful compute nodes, a decentralized approach of Stream Processing is needed. This paper moves a step towards decentralized SP by proposing a decentralized autoscaling mechanism for SP applications. Autoscaling refers to the ability for a system to automatically scale in and out as the load varies. As a stream processing application is generally composed of a set of operators in a pipeline, our protocol lets each operator's replica take scaling decisions and enforce them independently. Assuming a fully decentralized setting, we consider each replica has only a partial view of the graph of operators. Then, without care, concurrent distributed scaling could lead to inconsistencies in replicas' views and to data loss. To address this issue, we devise a maintenance protocol ensuring the consistency of replicas' views, no data loss and no downtime during reconfigurations. Finally, the paper describes the early experimentations conducted with a software prototype of a decentralized SP engine over a computing cluster, showing the usability of the mechanism.
Experimental Testbed and Methodology for the Assessment of RTK GNSS Receivers Used in Precision Agriculture

Precision Agriculture (PA) refers to applications asking for reliable and highly available precise positions, at centimeter level, in most of operational scenarios. Machinery guidance, automatic steering and controlled traffic farming enable machinery to move along repeatable tracks on the field, minimizing pass-to-pass errors and overlaps. In the recent years, satellite-based navigation has also opened the door to (semi) autonomous machineries for some specific farming scenarios and operations. Farming industry is now looking to use small robots to bring efficiencies and benefits to farms, capable of complex tasks that have not been possible with traditional large-scale agricultural machinery. Even though the state-of-the-art Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) receivers usually match the requirements posed by PA applications in open fields, propagation effects degrade the performance under foliage or with surrounding obstacles. This paper presents an experimental testbed and methodology suitable to assess the real performance of RTK GNSS-based devices in operational environments. Such testbed and methodology were effective to compare different devices, which resulted to be equivalent in open-sky conditions, but with significant differences in other types of environments. The paper also discusses opportunities and current limits of GNSS for emerging PA applications based on small robots and artificial intelligence.
Design of Frequency and Pattern Reconfigurable Wideband Semi-Circular Slot Ring Antenna

In this paper, a printed and miniaturized planar slot antenna is designed, which has the function of pattern and frequency reconfigurability. The antenna structure consists of two semi-circular slots, a CPW open ended branch and a CPW feedline. By changing the states of switches, the antenna can operate among the three frequency reconfigurable states of low band, high band and dual band. The low and high band cover 2.23-3.49GHz and 5.43-5.94 GHz, respectively. The null direction of radiation pattern at high frequency band can also be steered by changing the states of the switches installed in the semi-circular slot. When the antenna operates in 5.8 GHz. The antenna does not have any additional matching circuit, which brings simple structure and wideband.
Study on Processing Process of SiC Single Crystal Cut with Wire Saw Excited by Ultrasonic

Owing to high hardness and brittleness, SiC single crystal is the typical difficult-to-machine material. The SiC single crystal workpiece was cut by wire saw excited by transverse ultrasonic, and the cutting process was analyzed in current research. The kinematics equation of ultrasonic excitation wire saw and ordinary wire saw were established, and two different kinematics characteristics were analyzed respectively. The trajectory of abrasive particles in two cutting mode changed fundamentally. The finite element simulation model of single diamond abrasive cutting SiC single crystal was established. The influence of processing parameters on sawing force, surface morphology of SiC single crystal cut with abrasive excited by ultrasonic and ordinary diamond abrasive cutting was analyzed. The results show that sawing force of diamond abrasive excited by ultrasonic is much less than that of ordinary diamond abrasive cutting and the surface morphology is fairly flat. The cutting depth of abrasive has a significant influence on the sawing force and wafer surface.
A Deep Convolutional-Recurrent Neural Network Architecture for Parkinson’s Disease EEG Classification

Parkinson’s disease (PD), characterized by slowness of movement, tremor and rigidity, is one of the most prevalent neurodegenerative disorders. Recent studies have demonstrated that abnormal neural oscillations within and between multiple brain regions play a critical role in the motor symptoms through invasive neural recordings. Progressions have been also made in EEG studies to use features in cortical oscillations recorded non-invasively as a diagnostic tool for PD. However, it is still challenging to effectively use EEG recordings for PD diagnosis. In this work, we design a novel deep learning framework for PD EEG classification. Specifically, the convolutional neural network (CNN) and the recurrent neural network (RNN) with long shortterm memory (LSTM) cells are exploited in our framework. First, we design two 1D-CNN layers to derive features to represent spatial (topological) relationships across EEG channels. Then, we apply LSTM on the spatial features from the CNN to further improve its performance. Finally, we validate our model on the PD classification on resting EEG recorded from 20 PD and 21 healthy subjects. Our method achieves accuracy of 96.9%, precision of 100%, and recall of 93.4% for differentiating PD from healthy controls and outperforms the state-of-the-art PD EEG classification results in the deep learning literature.
Proto-object based saliency for event-driven cameras

Autonomous robots can rely on attention mechanisms to explore complex scenes and select salient stimuli relevant for behaviour. Stimulus selection should be fast to efficiently allocate available (and limited) computational resources to process in detail a subset of the otherwise overwhelmingly large sensory input. The amount of processing required is a product of the amount of data sampled by a robot’s sensors; while a standard RGB camera produces a fixed amount of data for every pixel of the sensor, an event-camera produces data only for where there is a contrast change in the field of view, and does so with a lower latency. In this paper, we describe the implementation of a state-of-the-art bottom-up attention model, based on structuring the visual scene in terms of proto-objects. As an event-camera encodes different visual information compared to frame-based cameras, the original algorithm must be adapted and modified. We find that the event-camera’s inherent detection of edges removes the need for some early stages of processing in the model. We describe the modifications, compare the event-driven algorithm to the original, and validate the potential for use on the iCub humanoid robot.
Parkinson’s Disease – How Virtual Reality Exercises can Improve the Neurorehabilitation Process

Nowadays, neurorehabilitation has started to benefit from the evolution of technology – from robotics to motion recognition and virtual reality, the complex procedures needed to recover lost functionalities after a neurological condition can be improved and made more accessible for a wide range of patients. Parkinson’s Disease is a disorder which can benefit greatly from the adaptability of the virtual environments. Our paper analyzes Parkinson’s Disease as a medical disorder and the currently existing classical and technological treatments. We then present our own system, including specially designed exercises for training specific competencies which were lost or affected by the neurological disorder.
GPU Accelerated Robust Scene Reconstruction

We propose a fast and accurate 3D reconstruction system that takes a sequence of RGB-D frames and produces a globally consistent camera trajectory and a dense 3D geometry. We redesign core modules of a state-of-the-art offline reconstruction pipeline to maximally exploit the power of GPU. We introduce GPU accelerated core modules that include RGBD odometry, geometric feature extraction and matching, point cloud registration, volumetric integration, and mesh extraction. Therefore, while being able to reproduce the results of the high-fidelity offline reconstruction system, our system runs more than 10 times faster on average. Nearly 10Hz can be achieved in medium size indoor scenes, making our offline system even comparable to online Simultaneous Localization and Mapping (SLAM) systems in terms of the speed. Experimental results show that our system produces more accurate results than several state-of-the-art online systems. The system is open source at https://github.com/theNded/Open3D.
Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine

Photodiode-based (PD-based) visible light positioning (VLP) has become a research focus of indoor positioning technology, while the existing VLP models rarely consider the anti-interference and positioning time of that. In this paper, indoor real-time three-dimensional visible light positioning system using fingerprinting and extreme learning machine (ELM) is proposed to make the system achieve not only high positioning accuracy and elevated anti-interference but also well-behaved real-time ability. In contrast to the positioning system based on K-Nearest Neighbor or Support Vector Machine, the proposed system achieves the highest positioning accuracy and the state-of-the-art positioning speed. Furthermore, the visible light positioning kernel is proposed as a method to reduce the size of the fingerprint database and thus reduce the training time exponentially. Both the simulation and the experiment results show that the proposed system achieves real-time 3-D positioning with high anti-interference. Therefore, this scheme can be considered as one of the effective methods for indoor 3-D positioning.
Intelligent Resource Allocation in Wireless Communications Systems

The emergence of DL represents a potential paradigm shift in the design of WCS, from conventional handcrafted schemes based on mathematical models with assumptions, to autonomous schemes based on DL using large sets of data. In this article, we discuss the essential elements of DL and investigate an intelligent RA scheme based on a DNN, in which multiple goals with various constraints can be satisfied through DL. Having confirmed the optimality and feasibility of DNN-based RA through simulation, we discuss some of the key technical challenges that remain problematic for the application of DL in the practical application of WCS.
Nanoparticles in the Therapy of Breast Cancer

Breast cancer is the most common malignancy in women, with incidence and mortality continuously increasing. Chemotherapy and radiation therapy, used in combination, represent the most common treatment. The association of chemotherapy with nanoparticles emerges as a new line of treatment, taking into consideration that nanoparticles are small enough to pass through some biological barriers and reach the tumor cells. The most frequently used types of nanoparticles are liposomes, polymeric micelles, dendrimers, iron oxide, and silica nanoparticles. Paclitaxel or trastuzumab conjugation with nanoparticles, trastuzumab and doxorubicine-loaded silica nanoparticles as well as viral nanoparticles could represent new therapies for HER2 positive breast carcinoma. In conclusion, the use of nanoparticles as carriers for chemotherapy drugs may open a new era in the treatment of breast carcinoma.
Double-Selection based High-Dimensional Factor Model with Application in Asset Pricing

This paper proposes a principal component analysis (PCA) approach after a double-selection Lasso and applies it to both Chinese and US stock market data. Similar to the idea of Post-Lasso, we perform least squares regression on the principal component factors. To accommodate the nonlinear nature of the data, this paper compares the support vector regression (SVR) model with least squares regression model. Empirical results show that the SVR method can improve the prediction ability, as evidenced by the superior accumulated rate of return using the test set sample of both markets.
TerrainFusion: Real-time Digital Surface Model Reconstruction based on Monocular SLAM

This paper presents an algorithm which can generate live digtial surface model (DSM) during the flight based on simultaneous localization and mapping (SLAM). We process the keyframe which is output by a monocular SLAM system to generate a local DSM, and fuse the local DSM to the global tiled DSM incrementally. During the local DSM generation, a local digital elevation model (DEM) is estimated by projecting the filtered 2D Delaunay mesh to a 3D mesh, and a local orthomosaic is obtained by projecting triangle image patches onto a 2D mesh. During the DSM fusion, both the local DEM and orthomosaic are split into tiles and fused to the global tiled DEM and orthomosaic respectively with multiband algorithm. Both the efficient DSM generation and fusion algorithms contribute to achieving a real-time reconstruction. Qualitative and quantitative experiments on a public aerial image dataset with different scenarios are performed to validate the effectiveness of the proposed method. Compared with traditional structure from motion (SfM) based approaches, the presented system is able to output both large-scale high-quality DEM and orthomosaic in real-time with low computational cost.
An Evaluation of Robot-to-Human Handover Configurations for Commercial Robots

The handover of objects is a fundamental task in robot-human interaction. The nature of the handover can be varied by adjusting various parameters in order to achieve a human-like interaction. In this paper the final handover configuration is examined and analyzed. For this purpose, we conducted an experiment in which subjects taught a robot proper and improper end poses for different objects in different situations. We analyze the learned poses to determine what factors proper poses depend on. In addition to the poses, the configurations of the individual joints of the commercial robot are evaluated. Our results demonstrate that proper poses can be grouped into three clusters. These differ mainly in the rotation of the forearm. The height of a proper handover is also determined based on external factors.
Blind Community Detection From Low-Rank Excitations of a Graph Filter

This paper considers a new framework to detect communities in a graph from the observation of signals at its nodes. We model the observed signals as noisy outputs of an unknown network process, represented as a graph filter that is excited by a set of unknown low-rank inputs/excitations. Application scenarios of this model include diffusion dynamics, pricing experiments, and opinion dynamics. Rather than learning the precise parameters of the graph itself, we aim at retrieving the community structure directly. The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals. Our analysis indicates that the community detection performance depends on an intrinsic ‘low-pass’ property of the graph filter. We also show that the performance can be improved via a low-rank matrix plus sparse decomposition method when the latent parameter vectors are known. Numerical results demonstrate that our approach is effective.
Secure MISO Cognitive-Based Mobile Edge Computing With Wireless Power Transfer

The finite battery capacity, limited spectrum resource and the transmission security are three challenges in designing mobile edge computing (MEC) systems. In this paper, a framework for wireless powered secure multiple-input single-output (MISO) cognitive-based MEC-enabled networks is proposed, which integrates several technologies: physical-layer security, cooperative relaying, cognitive radio (CR), wireless power transfer (WPT) and MEC. Two optimization problems are formulated to maximize the number of computation bits (NCB) of the mobile device (MD) and minimize the total transmission power (TTP) of the primary transmitter (PT) and the SBS, respectively. The formulated problems are non-convex and hard to solve. Two two-phase methods with block coordinate decent (BCD) and Lagrangian dual decomposition methods are proposed to jointly design the beamforming vector of the PT, beamforming matrix of the secondary base station (SBS), the central processing unit (CPU) frequency, the transmit power, the number of the offloaded bits and the offloading time of the MD. Simulation results are presented to show the effectiveness of the proposed methods.
LTE for Trains - Performance Interactions Examined with DL, ML and Resampling

Current LTE networks provide a large fraction of the mobile communication needs. One recent application area that have attained additional interest is the provision of mobile communication services to train passengers. To allow more efficient use of network resources and better onboard communication experience, onboard traffic aggregation can be performed. In this work we examine a large-scale operational data set from a routerbased LTE traffic aggregation system mounted onboard more than 100 trains belonging to a major Swedish train operator. We use both deep learning (DL) with Deep Neural Networks and traditional machine learning (ML) with Random Forests to examine an observed association between train velocity and achieved throughput, which curiously varies over different radio conditions. More than 37000 train journeys are analyzed to explore for structure and learn potential explanatory features. The results indicate that the association has a limited presence on a per cell basis, and that there is only a limited amount of learnable structure per cell. A resampling evaluation shows that the association becomes apparent when cell measurements are aggregated at an order of tens to a hundred cells.