Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of PCG Signals Using A Nonlinear Autoregressive Network with Exogenous Inputs (NARX)

Artificial neural networks have proven high efficiency in the classification of features in many application fields. It is widely proved that dynamic recurrent networks perform better than any other types of networks, for the feedback connections and the embedded memory. In this paper, we use a dynamic recurrent network called the nonlinear autoregressive network with exogenous inputs (NARX) for the normal/abnormal classification of phonocardiography (PCG) signals. Up to the authors' knowledge, this is the first time that the NARX network is used for such classification task. The network is trained and tested using a dataset of 6316 feature vectors, each vector of length 27 features. The evaluation of this network is achieved at different sizes of the embedded memory. The experimental results show that the NARX network outperforms other two networks, and its performance increases as a function of the memory size till a certain value. Further increase of the memory size degrades the performance as a results of model overfitting.
High Efficiency High Linearity Class-E PA using Analog Predistortion (APD) and reactance compensation Technique

Mobile communications standards require high power efficiency and linear power amplifier. In This paper a wide band class-E power amplifier (PA) with linearity improvement using analog predistortion (APD) technique. And high power-added efficiency using reactance compensation technique has been proposed. The simulation results display that the circuit realizes a very high power-added efficiency (PAE) of 73%, the output power is 33dBm, maximum Gain of 23dB and output third order intercept point (OIP3) of 44dBm. The circuit operates from 3 to 7.4 GHz using TSMC 130nm at 2V power supply.
Kalman Filter Finite Element Method for Real-Time Soft Tissue Modeling

Soft tissue modelling plays a significant role in surgery simulation as well as surgical procedure planning and training. However, it is a challenging research task to satisfy both physical realism and real-time simulation for soft tissue deformation. The finite element method (FEM) is a representative strategy for modelling of soft tissue deformation with highly physical realism. However, it suffers from expensive computations, unable to meet the requirement of real-time simulation. This paper proposes a novel method by combining FEM with the Kalman filter for real-time and accurate modelling of soft tissue deformation. The novelty of this method is that soft tissue deformation is formulated as a filtering identification process to online estimate soft tissue deformation from local measurement of displacement. To construct the discrete system state equation for filtering estimation, soft tissue deformation is discretised based on elastic theory in the space domain by FEM and is further discretised in the time domain by using the Wilson- $\theta $ implicit integration to solve the dynamic equilibrium equation of FEM deformation modelling. Subsequently, a Kalman filter is developed for online estimation and analysis of soft tissue deformation according to local measurement of displacement. Interactive tool-tissue interaction with haptic feedback is also achieved for surgery simulation. The presented method significantly improves the computational performance of the traditional FEM, but still maintains a similar level of accuracy. It not only achieves the real-time performance, but also exhibits the similar deformation behaviours as the traditional FEM and enables the use of large time steps to improve the simulation efficiency.
Token-Based Access Control

Traditional centralized access control has some shortcomings in robustness, trustworthiness and circulation. Blockchains have the advantages of fault tolerance and trust. Smart contracts have the characteristics of automatic execution and flexible expansion. Tokens can well record credential information and transfer easily. In this paper, blockchain, smart contract and token are integrated and applied to access control to solve the shortcomings of traditional access control. First, access control, blockchain, smart contract and token are briefly described. Second, this paper proposes a solution by giving the general data structure of access control token, elaborating the equivalence, split, merge and verification algorithms of access control token, and explaining the system architecture of token-based access control. Last, this paper uses a token-based access control simulation system to verify that token-based access control has certain comparative advantages in robustness, trustworthiness, circulation, concurrency and so on.
Influence Factor Analysis of the Recoverable Rocket Trajectory Planning Based on Multi-Pulse Path Planning Model

Based on the robotic path planning method, this paper conducted a research on the multi-pulse trajectory planning model of the recoverable rocket, and verified whether the model has enough feasibility and recovery precision to satisfy the engineering requirements. Firstly, by comparing the instantaneous pulse model and finite pulse width overload model, this paper analyzed the effectiveness, feasibility and precision of the model. Then this paper verified the validity of the multi-pulse trajectory planning model and analyzed the influences of dynamic constraints on the rocket trajectory quantitatively. Lastly, this paper analyzed the limit of the pulse number and the influence factors of the pulse number. The analysis results show the multi-pulse trajectory planning model of the recoverable rocket is correct, and the trajectory generated by this model is feasible in engineering practice.
A novel triage system using Bluetooth devices in support of incident management

In this work, the design and implementation of a novel triage and victim identification system is presented. Triage-tags depict the classes of the medical emergency of victims using specific colors. The traditional triage-tags are made of paper and the triage color is permanently printed on the tags before the actual use. Using paper tags has many limitations, e.g. it is not easy to upgrade or down-grade the triage classes when a patient's condition changes. The triage-tags of the proposed solution are novel electronic devices with enhanced capabilities, including Bluetooth connectivity, aiming at replacing the legacy paper cards. These devices can emit light in different frequencies (so in different colors) and can be controlled using Bluetooth enabled devices such as computers or smartphones. The devices are also equipped with NFC passive tags, enabling the fast and easy connectivity with the computers/smartphones, but also the unique identification of victims. The process followed consists of three steps and requires no technical knowledge from the medical personnel: a) scanning the NFC tag attached to the device (in order to retrieve its unique identification), b) discovering and connecting to the device (using Bluetooth technology) and c) changing the emitted color (by sending the proper commands). The first prototype of this novel solution has been evaluated by emergency personnel and the preliminary results shows that the proposed system improves and simplifies the triage and victims identification procedures and enables the re-triaging of victims.
User Grouping for the Uplink of Multiuser Hybrid mmWave MIMO

Hybrid analog/digital schemes for precoding/combining have proved to be a low-complexity and/or low-power strategy to obtain reasonable beamforming gains in multiuser millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Hybrid precoding/combining performs jointly baseband processing and analog processing in the radio frequency (RF) domain. In these systems, the number of RF chains limits the maximum number of streams simultaneously handled by the transceivers. In the uplink of a multiuser mmWave MIMO system, the hardware reduction based on hybrid transceivers is limited by the number of data streams that must be simultaneously served by the centralized node. Most works approach hybrid transceiver design by considering more RF chains than data streams, an unrealistic assumption when the number of nodes is large. On the other hand, statistically independent information is conventionally assumed in multiuser mmWave systems. This assumption does not hold in scenarios like wireless sensor networks (WSNs), where the sources produce correlated information. In this paper, by enabling inter-user correlation exploitation, we propose a grouping approach to handle a high number of individual sources with a limited number of RF chains through distributed quantizer linear coding (DQLC) mappings. The allocation of the users per group and the hybrid design of the combiner at the common central node to serve the grouped users is also analyzed. We also propose a hybrid minimum mean square error (MMSE) combining design in order to exploit the spatial correlation between the sources in a conventional uncoded mmWave uplink. Simulation results show the performance advantages of the proposed approaches in various hardware-constrained system settings.
Implementation of Pulse Compression Algorithm Based on Multicore CPU

To realize real-timely detection of low altitude aircraft target, this paper introduces a parallel model based on multicore CPU aiming at pulse compression (PC). Eight cores are utilized to process parallel intensive task. The detailed implementation of pulse compression (PC) algorithm is introduced in this paper. The result indicates that this design meets the requirement of real-timely pulse compression with one echo sample.
Software-Based Memory Analysis Environments for In-Memory Wear-Leveling

Emerging non-volatile memory (NVM) architectures are considered as a replacement for DRAM and storage in the near future, since NVMs provide low power consumption, fast access speed, and low unit cost. Due to the lower write-endurance of NVMs, several in-memory wear-leveling techniques have been studied over the last years. Since most approaches propose or rely on specialized hardware, the techniques are often evaluated based on assumptions and in-house simulations rather than on real systems. To address this issue, we develop a setup consisting of a gem5 instance and an NVMain2.0 instance, which simulates an entire system (CPU, peripherals, etc.) together with an NVM plugged into the system. Taking a recorded memory access pattern from a low-level simulation into consideration to design and optimize wear-leveling techniques as operating system services allows a cross-layer design of wear-leveling techniques. With the insights gathered by analyzing the recorded memory access patterns, we develop a software-only wear-leveling solution, which does not require special hardware at all. This algorithm is evaluated afterwards by the full system simulation.
Quad Band Antenna with CPW feed for Wireless and Satellite Applications

A quad-band low-profile CPW (Coplanar Wave Guide) fed planar patch antenna is designed and analyzed in this paper. The antenna is backed by low-cost widely available 1.6 mm thick FR-4 substrate. The design has compact-form-factor of $30.3\times 32.1\ \text{mm}^{2}$. The multiband characteristic causes the antenna to operate in four different bands having center frequencies at 3.38, 5.56, 8.36 and 11.82 GHz corresponding to the applications of WiMAX, WLAN, C-Band and X-Band, respectively. In comparison to the recently developed prototypes, this design exhibits good performance characteristics in terms of bandwidth, gain, efficiency, return loss and VSWR. The proposed design is analyzed using the CST Microwave Studio® software. The prototype model of the antenna is fabricated, tested and measured to validate the simulation outcome. Both the numerical and experimental results reveal the significance of the presented compact structure for the selected applications.
Low-Rank Tensor Completion and Total Variation Minimization for Color Image Inpainting

Low-rank (LR) and total variation (TV) are two most frequent priors that occur in image processing problems, and they have sparked a tremendous amount of researches, particularly for moving from scalar to vector, matrix or even high-order based functions. However, discretization schemes used for TV regularization often ignore the difference of the intrinsic properties, so it will lead to the problem that local smoothness cannot be effectively generated, let alone the problem of blurred edges. To address the image inpainting problem with corrupted data, in this paper, the color images are naturally considered as three-dimensional tensors, whose prior of smoothness can be measured by varietal TV norm along different dimensions. Specifically, we propose incorporating Shannon total variation (STV) and low-rank tensor completion (LRTC) into the construction of the final cost function, in which a new nonconvex low-rank constraint, namely truncated $\gamma $ -norm, is involved for closer rank approximation. Moreover, two methods are developed, i.e., LRRSTV and LRRSTV-T, due to the fact that LRTC can be represented by tensor unfolding and tensor decomposition. The final solution can be achieved by a practical variant of the augmented Lagrangian alternating direction method (ALADM). Experiments on color image inpainting tasks demonstrate that the proposed methods perform better then the state-of-the-art algorithms, both qualitatively and quantitatively.
Identifying Interpretable Link Communities with User Interactions and Messages in Social Networks

Community detection is a fundamental step in social network analysis. In this study, we focus on the hybrid identification of overlapping communities with network topology (e.g., user interaction) and edge-induced content (e.g., user messages). Conventional hybrid methods tend to combine topology and node-induced content to explore disjoint communities, but fail to integrate edge-induced content and to derive overlapping communities. Moreover, although several semantic community detection approaches have been developed, which can generate the semantic description for each community besides the partition result, they can only incorporate the word-level content to derived coarse-grained descriptions. We propose a novel Semantic Link Community Detection (SLCD) model based on non-negative matrix factorization (NMF). It adopts the perspective of link communities to integrate topology and edge-induced content, where the partitioned link communities can be further transformed into an overlapping node-induced membership. Moreover, SLCD incorporates both the word-level and sentence-level content while considering the diversity of user messages. Hence, it can also derive strongly interpretable and multi-view community descriptions simultaneously when community partition is finished. SLCD's superior performance and powerful application ability over other state-of-the-art competitors are further demonstrated in our experiments on a series of real social networks.
Convolutional Neural Network with Character Embeddings for Malicious Web Request Detection

In this paper, a deep learning based approach to malicious Web request detection is proposed, where a Web request is a message sent from a client to a server for communication. The characters in a request message can be considered as words in a sentence from the perspective of natural language processing. Consequently, character embeddings can be learned from a corpus of Web requests in an unsupervised manner via language models. In terms of Web requests, the URL path and request query string convey the most information, which can be extracted as the corpus to learn character-level embeddings. Furthermore, a specially designed convolutional neural network (CNN) is applied to the learned character-level embeddings for malicious and legitimate request identification. Our approach is totally data-driven, being free from hand-crafted features that require domain expertise and high computational cost. Extensive experiments conducted on HTTP DATASET CSIC 2010 dataset show significant improvement on the performance against existing state-of-the-art methods, especially for extremely imbalanced cases. The devised CNN model exploiting the embedding representation of Web requests achieves a false positive rate (FPR) of 0.02% with 98.72% on true positive rate (TPR), and 99.46% on accuracy (ACC), respectively. Codes are publicly available at https://github.com/acew14/CL-Embedding-CNN.
Generating IoT Traffic in Smart Home Environment

The Internet of Things is expected to count for a large part of the overall Internet traffic but its main characteristics are still widely unknown. In this work, we focus on IoT Traffic and we present a novel IoT Traffic Generator. As an use-case, we generate IoT traffic within a Smart-Home Environment. Our generator allows studying the IoT traffic and can serve for describing the main properties of the IoT traffic. It is a first step toward the detection of security threats and anomalies for IoT Traffic.
Numerical Modeling of Glucose Biosensor With pH-Based Electrochemical Field-Effect Transistor Device

In this article, we present a new model for glucose detection using an electrochemical field-effect transistor (ElecFET) with the MATLAB software. ElecFET is a device in which the basic element is a pH-sensitive chemical field-effect transistor (pH-ChemFET) designed with an integrated platinum microelectrode around the sensitive gate dielectric capable to trigger electrochemical reactions. A number of parameters, such as polarization time (<inline-formula> <tex-math notation="LaTeX">${T}_{\text {pol}}$ </tex-math></inline-formula>) and polarization voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{p}$ </tex-math></inline-formula>), have been investigated, as well as the enzymatic unit number per volume (<inline-formula> <tex-math notation="LaTeX">${N}_{\text {enz}}$ </tex-math></inline-formula>), the Michaelis constant (<inline-formula> <tex-math notation="LaTeX">${K}_{M}$ </tex-math></inline-formula>), and the glucose concentration (<inline-formula> <tex-math notation="LaTeX">${C}_{{0}}$ </tex-math></inline-formula>). Each of the aforementioned parameters influences the glucose sensor response. Experimental data compared with our numerical model showed a reasonable fit.
Original Music Album Diffusion Sustainability in Social Network-Based Communities: A Network Embedded Perspective

An original music album is a collection of music created by an online musician, and is a special form of User Generated Content. This paper aims to identify the factors that drive original music album diffusion in online music community. Based on information acceptance theory and social networking, this study constructed a diffusion model of original music albums. To empirically test the theoretically driven hypotheses, the study uses two-stage data from albums and musicians from NetEase Music, one of the most popular online entertainment communities in China. The findings suggested that album created days, musician network degree, album subscription quantity, and album forward quantity had significant positive effects on diffusion sustainability. Musician network embedding had a significant positive effect on diffusion sustainability. There was an inverted u-shaped relationship between album music quantity and diffusion sustainability.
Parallel Computation of Division over GF(2^n) Covering Divide-by-Zero Based on Tile Assembly Model

This paper proposes how to compute division over finite field GF(2^n) that could cover the case of zero divisor based on tile assembly model. Key functions are accomplished by combining n final configurations of two different sub-models. The final configuration of every sub-model contains 2n^2-n computation tiles which perform 10 different functions. The coding ways of most computation tiles are optimized than the previous model to improve the complexity. The highest bit of modulus number is omitted to simplify the encoding way of computation tiles in the assembly process. This model requires 4591 types of computation tiles and 13 types of boundary tiles. The assembly time complexity is 2n^2-1 and the space complexity is n^4.
Wind Measurement and Simulation Techniques in Multi-Rotor Small Unmanned Aerial Vehicles

Wind disturbance presents a formidable challenge to the flight performance of multi-rotor small unmanned aerial vehicles (sUAVs). This paper presents a comprehensive review of techniques for measuring wind speed and airspeed for multi-rotor sUAVs. Three categories of sensing techniques are reviewed: flow sensors, anemometers, and tilt-angle based approaches. We also review techniques for generating wind disturbances in simulation. Wind simulation techniques that use power spectral density (PSD) functions, computational fluid dynamics (CFD), and probabilistic models are examined. Finally, we provide an open-source Python implementation of the Dryden wind turbulence model and embedded code to interface with an ultrasonic anemometer.
BF-Join: An Efficient Hash Join Algorithm for DRAM-NVM-Based Hybrid Memory Systems

Non-volatile memory (NVM) has been regarded as the next-generation memory that can replace DRAM in modern computer systems. However, NVM also has some limitations, e.g., read/write asymmetry and limited write endurance. Thus, a practical way is to construct hybrid memory composed of both NVM and DRAM. Such hybrid memory architecture introduces many new challenges for existing algorithms. In this paper, we focus on improving hash join algorithms for DRAM-NVM-based hybrid memory. In particular, we propose a memory-efficient and NVM-friendly join scheme called BF-Join. BF-Join has several new designs. First, BF-Join proposes to maintain the build table and the probe table on NVM to ensure the persistency of data as well as the avoidance of NVM writes. Second, BF-Join devises a Bloom filter based compact structure on DRAM to organize the partitions generated in the hash-join process, through which we can reduce the memory cost during the join process. Moreover, we propose an improved cache-optimized BF-Join that employs cache-friendly Bloom filters to reduce cache misses in the join process. We present theoretical analysis on the time and space complexity of BF-Join, and conduct experiments on a server equipped with DRAM and NVDIMM. The comparative results between BF-Join and four baseline hash join algorithms suggest the space efficiency and high time performance of our proposal.
Polyimide films coated by magnetron sputtered boron nitride for high-temperature capacitor dielectrics

Polymers are widely used as dielectric materials in dielectric capacitors, which are the key component of various circuits in power electronic devices. The sharply increased conduction loss at elevated temperatures limits their high-temperature applications. To address this issue, here in this study boron nitride (BN)/polyimide (PI)/BN sandwich-structured films were developed by depositing BN of optimal thickness onto both sides of PI films using magnetron sputtering. Dielectric, insulating and energy-storage performance at 150 °C of the resultant composite films were investigated. The experimental results showed that a dense layer of hexagonal boron nitride (h-BN) was formed on the surface of the polyimide and the coating layer could effectively suppress the charge injection at the electrode/dielectric interfaces by improving the barrier height. Consequently, the sandwiched film of optimal BN thickness exhibited a declined leakage current of about one order of magnitude lower and an improved breakdown strength by 12% in comparison to the neat PI. The sandwiched film could discharge an energy density of 0.493 J/cm3 with charge-discharge efficiency of over 90% under an electric field of 200 MV/m at 150 °C, while the neat PI only possesses an efficiency of 72% and a much lower discharged energy density.