Miracle-dz/UniFAR-FTUs huggingface dataset:
A Face-off - Classical and Heuristic-based Path Planning Approaches

Robot path planning is a computational problem to find a valid sequence of configurations to move a robot from an initial to a final destination. Several classical and heuristic-based methods exist that can be used to solve the problem. This paper compares the performance of a classical method based on potential field, Lyapunov-based Control Scheme, with those of the standard and stepping ahead Firefly Algorithms. The performance comparison is based on the optimal path distance and time. The results show that the stepping ahead Firefly algorithm finds a shorter path in lesser duration when compared with the Lyapunov-based method. The LbCS also inherently faces the local minima problem when the start, target, and obstacle’s center coordinates are collinear. This problem is solved using the firefly algorithm where the diversification of the fireflies helps escape local minima.
Investigation of Printed Meander-Line Bow-Tie Structures for GNSS Antenna Elements and Arrays With Planar-3D Integral Equation Methods

This contribution deals with the first design of an antenna element for the GPS-L1 frequency based on crossed dipoles with a meander-line bow-tie geometry leading to a high size reduction rate. The structure can be printed on a PCB together with other components leading to a cheap and robust fabrication. For the simulation of such structures, our planar-3D integral equation framework is very well suited with an own mesh generation and further enhanced quadrature strategies and redundancy reduction algorithms. SMD-elements for the matching circuit are incorporated as line impedances. First the admittance behavior of the dipole structure is derived to generate a circular polarization followed by a fine tuning of the combined structure to get minimum axial ratio. The element values of the matching circuit are determined with a particle swarm method which are further optimized by full simulations with the integrated matching network.
Exposing data locality in HPC-based systems by using the HDFS backend

Nowadays, there are two main approaches for dealing with data-intensive applications: parallel file systems in classical High-Performance Computing (HPC) centers and Big Data like parallel file system for ensuring the data centric vision. Furthermore, there is a growing overlap between HPC and Big Data applications, given that Big Data paradigm is a growing consumer of HPC resources. HDFS is one of the most important file systems for data intensive applications while, from the parallel file systems point of view, MPI-IO is the most used interface for parallel I/O. In this paper, we propose a novel solution for taking advantage of HDFS through MPI-based parallel applications. To demonstrate its feasibility, we have included our approach in MIMIR, a MapReduce framework for MPI-based applications. We have optimized MIMIR framework by providing data locality features provided by our approach. The experimental evaluation demonstrates that our solution offers around 25% performance for map phase compared with the MIMIR baseline solution.
Soft-Error Susceptibility in Flip-Flop in EUV 7 nm Bulk-FinFET Technology

This paper presents single-event upset (SEU) rates in flip-flops (FFs) in EUV 7 nm bulk-FinFET technology. EUV technology achieves high transistor-density, small FF cell-size, and low SEU rate. The alpha-SEU rate in EUV 7 nm FFs is 0.7X of the SEU rates in 10 nm FFs. The neutron-SEU rate in EUV 7 nm FFs is 0.6X of the SEU rates in 10 nm FFs. This paper also discusses the circuit dependence of the SEU rate in Normal-, Reset-, Set-FFs, and a soft-error immune FF (SEIFF), and SRAM.
Hemostasis mechanism and applications of N‐alkylated chitosan sponge

Chitosan (CHI) is a versatile biological material that is well known for its hemostatic properties. This preliminary study evaluated several self-assembling hydrophobically modified chitosan (HM–CHI) sponges to determine their efficacy on hemostasis. Fourier transform infrared (FT-IR) spectroscopy was used to determine the successful graft of dodecyl groups onto the nitrogen atoms of CHI molecules. A platelet aggregation assay revealed that HM–CHI accelerated the platelet aggregation rate. Fluorescence spectroscopy showed that the HM–CHI changed the structure of fibrinogen in blood. Activated partial thromboplastin time, prothrombin time, fibrinogen time, and thromboelastographic assays were used to explore the effect of HM–CHI on the autologous blood coagulation pathway. Finally, a hemostatic sponge was made with HM–CHI and freeze-dried zeolite composite film and was applied to the rat femoral artery hemostasis model. A hemostasis time of 86 ± 5 sec was achieved, which was significantly better than the one composed with pure CHI. The experimental results of the HM–CHI hemostatic materials are inspiring and will encourage the research and development of such materials. HM–CHI may be a strong candidate as a safe and effective hemostatic material. Copyright © 2017 John Wiley & Sons, Ltd.
Dielectric Relaxation, Aging and Recovery in High-K MIM Capacitors

High-K metal-insulator-metal capacitors are used in many high-performance applications that require both excellent energy storage and minimal energy loss. Often the increase in dielectric permittivity is coupled with an increase in dielectric relaxation or absorption. Additionally, scaling demands that the devices often be used at higher fields, leading to the need to characterize the impact of voltage stress. In this work, the impact of temperature and constant voltage stress on dielectric relaxation in Al2O3 MIM capacitors is studied using measurements of the complex permittivity as a function of frequency and time with a measure-stress-measure program. We observe both the degradation of loss parameters extracted from these spectra and the slow recovery over time. We postulate the existence of a potential threshold of temperature acceleration, and a state of permanent irreversible degradation.
Customer Segment Application of Machine Learning in Business Operation of China Mobile

The purpose of this paper is to use machine learning technology to build more user profiles in the process of customer segmentation to assist marketing decision-making. Specifically, by introducing cross features into the construction of Feature Engineering, the accuracy of target group prediction can be further improved and the user subgroup after feature crossing can be mined. In addition, this paper also proposes an improved decision tree model, which can build more comprehensive user profiles, and then assist in intelligent marketing. Firstly, both methods need to preprocess the data. In addition, for the subsequent feature crossing, it is necessary to discretize the continuous features and implement one-hot encoding for all data. In the process of discretization, the paper realizes the automatic selection of better discrete granularity value. A specific example is given and a comparative experiment is carried out to verify the effectiveness of the method.
Enabling Self-defined Navigation on Road Graph via Double Rewarded Generalized VIN

Nowadays navigation systems are mostly centralized and mainly relying on the traffic conditions of road network, and other influence factors are rarely considered, such as roads topology, domain knowledge and planning autonomy, which is result in the recommended seemingly optimal routes may attract numerous vehicles and then congestion. Besides, the existing region-based methods have deviation on acquiring the traffic conditions of road segments, which lead to the inadequate use of road resource and the waste of drivers’ time.Considering that MEC can provide low latency and quick responses support for intelligent driving, we propose an online planning approach on the road graph via a double rewarded Generalized Value Iteration Network, 2r-GVIN for short. In detail, a large-scale real GPS trajectories are mapped and discretized into traffic sequence pairs anonymously, that is, capturing the traffic status and driving actions as dataset. Then a neural network ConvLSTM which can effectively capture the spatio-temporal properties, is trained to predict the future traffic situation to form “prediction reward”. To enable learning and planning on irregular road graphs straightforwardly, an economical embedding graph convolution operator network GVIN is used. Combining with the model input “current reward”, 2r-GVIN is formed to solve the plan-involved function iteratively. Lastly, we train and evaluate 2r-GVIN on planning problem in road networks, showing that it can achieve road segment-based self-defined navigation with high success rate.
Reliability and Variability-Aware DTCO Flow: Demonstration of Projections to N3 FinFET and Nanosheet Technologies

Reliability and variability-aware simulations of logic cells are essential to correctly analyze and predict the performance of upcoming technologies. A simulation flow for DTCO is presented here, which combines the accuracy of TCAD with the performance of SPICE - utilizing parasitic extractions, the impedance field method for variations, and the compact-physics simulator Comphy for reliability. Good agreement with experimental RO performance of iN14 is demonstrated and projections to N3 FinFET and nanosheet technologies are made.
Novel Weight Mapping Method for Reliable NVM based Neural Network

Errors of vector-matrix-multiplication induced by interconnect resistance become a crucial reliability challenge in non-volatile memory (NVM) based neural network. Here, we propose a novel weight mapping method, called weight mapping correction (WMC), to mitigate the deviation of weight represented by the conductance of NVM array without time-consuming retraining and circuit overheads. Simulation results show that accuracy is recovered significantly when WMC is applied to various size arrays consisting of mainstream NVM in advanced technology nodes.
Single Event Hard Error due to Terrestrial Radiation

Terrestrial radiation is ubiquitous. Alpha particle radioactive contamination is often found in semiconductor packaging materials, and neutrons generated by cosmic rays constantly approach the ground. Historically, these radiations are regarded as a source of soft-error. We are in the era of aggressive device miniaturization, operation voltage scaling and increasing frequency. Herein, we present that the terrestrial radiation-induced single event can potentially result in hard-error. As a result, radiation hard-error hardening might be necessary in the near future even in consumer electronics.
DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction

In this paper, we discuss a new approach to direction of arrival estimation for systems with subarray sampling. We propose to estimate the covariance matrix of the full array from the sample covariance matrices of the subarrays using a neural network. This technique enables the estimation of more sources than radio frequency chains by applying a MUSIC estimator to the reconstructed full covariance matrix. The proposed method is able to outperform classical estimators and has some benefits compared to a recently proposed machine learning-based technique for these systems, which models the direction of arrival estimation problem as a end-to-end regression task.
Profit-driven Computation Offloading for Mobile Edge Computing in Wireless Metropolitan Area Networks

The applications and services are growing computation-intensive vary with the advent of wireless communication technology, whereas the computing resources and capabilities of mobile devices (MDs) are finite. Mobile edge computing (MEC) is a compelling way to offload applications from MDs to the cloudlets for performing, by means of deploying cloudlets nearby the MDs, which is capable of alleviating the computing burden of MDs and reducing the completion time of applications. However, MDs are irregularly dispersed in the Wireless Metropolitan Area Networks (WMAN), and the service provider needs to recover the cost of the deployment and maintenance of cloudlets and achieve profitability. As a consequence, it poses a significant challenge to reasonably offload applications from MD to the proper cloudlet for MEC in the WMAN. In this paper, we study the user satisfaction of MDs, the profit of service provider and the resource utilization of cloudlets as a multi-objective optimization problem. In the sequel, we devise a profit-driven computation offloading method, under the collaboration of MD- cloudlet-cloud, to obtain the optimal offloading strategy such that maximizes the user satisfaction of MDs, the profit of service provider and the resource utilization of cloudlets. To this end, we conduct comprehensive experiments to validate the efficiency of the proposed method. The numerical results demonstrate the performance of the proposed algorithm is prominent.
Path Loss Results in an Indoor Corridor Scenario at the 26, 32 and 39 GHz Millimeter-Wave Bands

Indoor propagation scenarios have drawn high interest, with several reported results being available. Nevertheless, more campaigns are still needed. Universidad Politécnica de Madrid (UPM) has carried out several indoor propagation campaigns at the 26, 32 and 39 GHz millimeter-wave frequency bands in a corridor indoor scenario. In this paper, multi-frequency path loss propagation results are presented for both line-of-sight (LOS) and non-light-of-sight (NLOS) conditions. These results are modeled with the CIF and ABG fittings and compared with available millimeter-wave models. Also, CI and FI coefficients for NLOS conditions are provided.
Frequency Reconfigurable Low-Profile UWB Magneto-Electric Dipole in VHF Band

Reducing the size of directive and wideband antennas operating in the Very High-Frequency band is a major challenge for small satellite integration. At 100 MHz, the wavelength is 3 m, and the size reduction will make the integration easier and reduces fabrication costs. The original technique presented in this paper enables a significant height reduction (up to 50%) of a crossed Magneto-Electric Dipole antenna while maintaining an ultra-wideband operation bandwidth from 93 to 360 MHz (118%). The proposed technique is based on adding frequency reconfigurable capability to a crossed Magneto-Electric Dipole antenna structure. The antenna has been prototyped and experimentally characterized in a large-sized anechoic chamber. The measured results are in excellent agreement with full-wave electromagnetic simulated results.
Improved deep learning model text classification

Deep learning technology develops rapidly. Convolutional Neural Network (CNN), as a key technology in deep learning, has been favored and concerned by many scholars and widely applied in information retrieval, classification, data management, mining and other fields. In order to fully obtain the local features and key words of text, a TCNN-DAM model is proposed based on the study of TCNN model, which aims at maximizing the representation of text features, improving the text classification effect, and promoting the model to better classify in sogou news corpus. Tests show that the improved model has outstanding classification effects, which can effectively improve the accuracy, precision, recall and F1 value of classification.
Determinants of computerised accounting information system adoption using an integrated environmental perspective: An Empirical Study

A computerised accounting information system (CAIS) enables enterprises to carry out financial operations that were formerly done manually. In emerging markets, such as Saudi Arabia, small and medium-sized enterprises (SMEs) rarely use CAISs. Motivating their adoption among SMEs necessitates an understanding of why some enterprises are willing to adopt them while others are not. The aim of this research was to explore the potential determinants of Saudi SMEs’ decisions regarding CAIS adoption by examining the influence of SMEs’ environmental factors on CAIS adoption. To this end, a model was developed to integrate determinants of CAIS adoption drawn from the literature and other factors that have not yet been investigated by researchers. The examination focused on the role of governmental factors on CAIS adoption. The study tested twelve factors that influence SMEs’ adoption of CAISs Saudi Arabia. This work has contributed to the literature by proposing an integrated model that links governmental pressures to CAIS adoption.
A Rotationally Symmetric Miniaturized and Stable Frequency Selective Surface Structure

A rotationally symmetric miniaturized low profile frequency selective surface (FSS) is proposed in this paper. The FSS unit is composed of convoluted dipoles, and the hexagonal elements are arranged obliquely to form a compact FSS. An equivalent circuit model is presented to demonstrate the working mechanism of the FSS. To further reduce the size, the similar patterns are etched on both sides of the substrate, and the two metallic layers are connected by vias. By iteratively increasing the layers and metallic vias, the resonant frequency will be greatly reduced. The proposed miniaturized FSS attains good stability for different polarization angles and incident angles.
A Balanced Cost Fault-Tolerant Scheduling Algorithm in Heterogeneous Real-Time Systems

High reliability is required for many safety-critical and real-time applications. It is difficult to avoid permanent faults during the executing process of tasks. Once such faults are not processed in time, real-time tasks cannot be completed within the specified time, which may lead to catastrophic consequences. In order to deal with the problem of permanent fault tolerance in heterogeneous real-time systems, in this paper, we propose a balanced cost fault-tolerant scheduling algorithm. First, the algorithm uses the task computation time and the average communication time of its successor nodes as the ranking factors when calculating task priority, so that the priority of the task with larger average communication time improved; second, a task cost table is established, and tasks are allocated to appropriate processors according to the values in the task cost table in processor allocation stage. Experimental results show that the proposed algorithm can effectively improve the system reliability and reduce the completion time.
Time series modeling of the cycle-to-cycle variability in h-BN based memristors

We have characterized and modeled memristor devices based on the Au/Ti/multilayer h-BN/Au/Ti stack. Resistive switching (RS) operation has been analysed by extracting the reset and set voltages and currents. The evolution of the set and reset parameters along a RS series was mathematically modeled in a cycle-to-cycle (CTC) basis by means of the Time Series Analysis (TSA). To do so, the Autocorrelation Functions (ACF) and the Partial Autocorrelation Functions (PACF) have been calculated. These tools help to perform a comprehensive variability study and to obtain the corresponding analytical models within the TSA context. Finally, we have included this modeling procedure in a complete compact model such as the Stanford to be able to account for this variability at the circuit level. Experimental current versus voltage (I- V) curves have been correctly fitted with the model.
Miniaturized Omnidirectional Circularly Polarized Antenna for IoT Applications

In this paper, a miniaturized omnidirectional circularly polarized (OCP) antenna for Internet-of-Things (IoT) applications is presented. The CP waveform is synthesized by combining two linearly polarized antenna elements which act as electric and magnetic dipole, respectively. The vertical and the horizontally polarized elements are integrated and fed by a single port. The OCP antenna achieves a CP bandwidth covering 80 MHz from 2.4 to 2.48 GHz. The results show that the antenna can provide at 2.44 GHz an axial ratio lower than 3 dB and gain variations of less than 1.1 dB over all azimuth angles. The OCP antenna is printed on two cost-effective FR-4 PCBs and it is very low profile. Thanks to its radiation and geometric properties, the proposed antenna solution fits well in specific IoT use cases where a monopole-type radiation pattern is required.
Time-frequency Resolution Optimization Features on Spoof Detection

With the improvement of synthesized speech and converted speech technology, computers can simulate speech that is difficult for human ears to distinguish, and speaker recognition systems are extremely vulnerable to such fraudulent speech attacks. Therefore, the research on deceptive speech detection attacks is of great significance for improving the reliability of speaker recognition systems. In this article, we study the characteristics of the Automatic Speaker Verification Deception and Countermeasure Challenge (ASVSpoof2019) dataset based on Fourier Transform-based Linear Frequency Cepstral Coefficients (LFCC) and Constant Q-Transformed Constant Q Cepstral Coefficients (CQCC). And analyzed these features in the time resolution and frequency resolution optimization method and energy expression optimization method. The back-end classifier uses Gaussian Mixture Model (GMM) and deep residual network as the back-end classifier. The result of using equal error rate (EER) on the GMM classifier is 6.98%, which is an increase of 13.72% compared with baseline LFCC.
Deep Learning Neural Networks for Music Information Retrieval

Music Information Retrieval is a vast area of study and research that deals with understanding the different features of soundtracks and being able to classify, group and modify them based on it. It has multiple sub fields like Music Classification, Generation, Recommendation, Processing, Recognition etc. In this project, I focused only on Music Classification. This paper’s main objective is to compare two different neural network architectures used in examining the genre of music. It employs the use of different parameters and types of optimization algorithms which helps us analyze which will help achieve higher accuracy.
An Enhanced Multi-Objective Evolutionary Algorithm with Decomposition for Signed Community Detection Problem

Community detection is useful for better understanding of the structure of complex networks, and aids in the extraction of required information from such networks. Community detection problem can be modelled as an NP-hard combinatorial optimization problem. Many optimization algorithms (both single -objective and multi-objectives) have been implemented to address community detection problem, where the first objective is usually representing the maximization of the internal connections, while the second objective represents the minimization of the external connections between the communities or clusters. In this research, an enhanced mutation operator is proposed for improving the search performance of multi-objective evolutionary algorithm with decomposition (MOEA/D), based on the types of the connections between the nodes. The proposed algorithm was evaluated based on a set of five benchmark datasets, in terms of Normalized Mutual Information (NMI), Weighted NMI (WNMI), Signed Modularity (Qs), and Error rate (Error). The results showed that our proposed enhanced algorithm has attained the best position as compared to the standard version of MOEA/D, and other state of art research papers.