Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatically matching topographical measurements of cartridge cases using a record linkage framework

Firing a gun leaves marks on cartridge cases which purportedly uniquely identify the gun. Firearms examiners typically use a visual examination to evaluate if two cartridge cases were fired from the same gun, and this is a subjective process that has come under scrutiny. Matching can be done in a more reproducible manner using automated algorithms. In this paper, we develop methodology to compare topographical measurements of cartridge cases. We demonstrate the use of a record linkage framework in this context. We compare performance using topographical measurements to older reflectance microscopy images, investigating the extent to which the former produce more accurate comparisons. Using a diverse collection of images of over 1,100 cartridge cases, we find that overall performance is generally improved using topographical data. Some subsets of the data achieve almost perfect predictive performance in terms of precision and recall, while some produce extremely poor performance. Further work needs to be done to assess if examiners face similar difficulties on certain gun and ammunition combinations. For automatic methods, a fuller investigation into their fairness and robustness is necessary before they can be deployed in practice.
Improved Access Control Decision Diagrams for ABAC Policy Evaluation and Management

The Multi-data-type Interval Decision Diagram (MIDD) approach can neither express and deal with the critical mark of an attribute correctly, nor present the obligation and advices consistently and process them simply. To remedy MIDD approach defects and deficiencies, some improvements and expansions were proposed. Firstly, the node which was originally defined to represent an attribute of subjects, objects, operations, and environment conditions is re-defined to represent an element of an attribute so that the elements in an attribute-based access control policy can be represented accurately, and no problem for the critical attribute mark. Secondly, the obligation and advices are also used as an element and represented in an internal node to keep nodes consistency and make processing simply. Finally, the combining-algorithm is also added to the decision nodes, so that the policy decision point (PDP) can use it when needed.
Comparative Wavelength-Dependent Analysis of GaAs Buried-Gate OPFET for Visible Light Communication

Visible-Light Communication (VLC) is an area of interest in the recent years. GaAs buried-gate Metal-Semiconductor Field Effect Transistor (MESFET)-based Opto-Electronic Integrated Circuit (OEIC) receivers can function as high performance receivers exhibiting tremendous sensitivity with large bandwidth and simultaneous amplification in the visible range. In this paper, we study the wavelength-dependence of optically-controlled buried gate GaAs MESFET parameters. The results show that there is no considerable variation in the characteristics with the wavelength of visible light in the range 450 nm to 700 nm. We have analyzed the results based on the absorption phenomena, the photoconductive and the photovoltaic effects, and the scaling rules-induced effects. The device shows high potential for applications wherein the same light source is used for lighting as well as communication.
A Multi-Sensor Simulation Environment for Autonomous Cars

This paper describes a multi-sensor simulation environment. This environment is being used to develop tracking methods to improve the accuracy of environmental perception and obstacle detection for autonomous vehicles. The system is being developed as part of a collaborative project entitled: Artificial Learning Environment for Autonomous Driving (ALEAD). The system currently incorporates a range of different sensor models, such as camera, infrared (IR) camera and LiDAR, with radar and GNSS-aided navigation systems to be added at a later stage. Each sensor model has been developed to be as realistic as possible - incorporating physical defects and other artefacts found in real sensors. This paper describes the environment, sensors and demonstrates the use of a Kalman filter based tracking algorithm to fuse data to predict the trajectories of dynamic obstacles. The multi-sensor tracking system has been tested to track a ball bouncing in a 3D environment constructed using Unity3D software.
Bus Voltage Control of DC Microgrid using Sliding Mode Control

The world is witnessing tremendous growth in the contribution of renewable energy sources (RES) to meet the power demand. Continuous development and decreasing costs of renewable energy technology and semiconductor devices can be considered as key players behind this. With power semiconductor devices supporting efficient power conversion, DC compatible systems have made their places in not only industrial but also in domestic environments. DC microgrids consisting of microsources, group of loads and their corresponding power electronic interface have proved themselves to be more efficient than their AC counter parts. In DC microgrids, keeping the bus voltage constant and providing an uninterrupted power to sensitive loads are a major concerns. Point of load converters which are strictly controlled when supplying a simple resistor make up a Constant Power Loads (CPL) and posses' negative incremental resistance characteristics makes the system vulnerable to instability. The Battery Energy Storage (BES) system with bidirectional DC- DC converter is usually assigned a duty to keep the power balance and bus voltage unchanged. A non linear Sliding Mode Control (SMC) is implemented to control this DC-DC converter and the performance of the same is evaluated using MATLAB simulation.
Improving the Performance of Multitask Diffusion APA via Controlled Inter-Cluster Cooperation

In this paper, we consider the problem of estimating multiple parameter vectors over a sensor network in a multitasking framework and under temporally-correlated input conditions. For this, an efficient clustered multitask diffusion affine projection algorithm (APA) is proposed that enjoys both intra-cluster and inter-cluster cooperation via diffusion. It is, however, shown that while collaboration in principle is a useful step to enhance the performance of a network, uncontrolled mode of inter-cluster collaboration can at times be detrimental to its convergence performance, especially near steady-state. To overcome this, a controlled form of inter-cluster collaboration is proposed by means of a control variable which helps in maintaining the collaboration in right direction. The proposed controlled multitask strategy attains improved performance in terms of both transient and steady-state mean square deviation (MSD) vis-a-vis existing algorithms, as also confirmed by simulation studies. We carry out a detailed performance analysis of the proposed algorithm, obtain stability bounds for its convergence in both mean and mean-square senses, and derive expressions for the network level MSD. Simulation results reveal that the proposed scheme performs consistently well even in the absence of cluster information.
Implementation of the Three-Level DC-DC Converter Applied in Medium-Voltage Solid-State Transformer

As the progress of wide-bandgap (WBG) semiconductor devices and the rise of distributed energy resources (DER), medium-voltage solid-state transformer (SST) becomes more and more popular in recent years. Medium-voltage SST features high-performance and fantastic functionality. The SST proposed in this paper is applied in 11.4 kVAC distribution system. The high-voltage terminals are connected in series, and the low-voltage ones are connected in parallel. Steady-state analysis and components design of the converter as well as the parameter design, insulation consideration and loss optimization of the transformer, are described in this paper. Meanwhile, the simulation software SIMPLIS and COMSOL Multiphysics® are used to ensure the validity. Finally, one of 13 modules with input voltage 1.52 kVDC, output voltage 380 VDC and output power 10 kW is designed and realized to verify the feasibility of the prototype converter applied in SST.
Modeling and Reducing Idling Energy Consumption in Energy Harvesting Terahertz Nanonetworks

A variety of nanoscale applications require downlink and broadcast-based transmission of control packets from a powered transmitter to energy-harvesting nanonodes with constrained storage capacity. The nanonode's communication system is anticipated to be a bottleneck for such nanonetworks, hence an accurate modeling of its energy consumption is needed. Currently, TS-OOK is a prevailing scheme for nanocommunication in the terahertz (THz) frequencies, with short pulses representing logical "1"s and silences logical "0"s. In the energy modeling of this scheme, certain energy consumptions are attributed to the transmission and reception of the pulses. However, traditional communication systems teach us that the idling energy consumption should not be neglected. Hence, we provide an energy consumption model for TS-OOK-based nanocommunication systems that accounts for the energy consumed in idling, in addition to the transmission and reception-originated consumptions. We demonstrate that, for a meaningful performance of the considered nanonetwork, the nanonode's idling energy consumption has to be at least nine orders of magnitude lower than the corresponding energy consumption in reception. To increase the tolerable idling energy, we propose a new energy lifecycle for the receiving nanonodes. Assuming frequent packet repetitions on the transmit side, the proposed lifecycle utilizes periodic short wake-ups of the receiving nanonodes. We show that, when the proposed lifecycle is utilized, up to three orders of magnitude higher idling energy consumption can be tolerated compared to the baseline.
Generation of Orbital Angular Momentum modes using Photonic Lanterns

Photonic lanterns are novel photonic devices, which can be realized as an all-fiber device by an adiabatic taper of a single mode fiber (SMF) bunch arranged within a low index capillary. They are broadly classified based on the number of SMF cores and the functionality of mode selectivity. We present a detailed mathematical model to design and analyze generation of orbital angular momentum (OAM) modes using photonic lanterns. The effects of various design parameters like asymmetry and taper profile on the output OAM mode is discussed. Three and six core photonic lanterns have been used as examples for the study.
Analysis of LoRa/LoRaWAN Challenges: Review

In recent years, LoRaWAN has been considered as one of the commonest low power area network technologies and has gained much attention due to the rapid growth of Internet of Things (IoT) market and applications. Its communication is characterized by very low power consumption, low cost, extensive network coverage and so on. However, LoRaWAN is faced with scalability challenge, which has affected its adoption. This challenge has attracted several research attentions and is far from over. Therefore, this paper performed a review on existing works, about 13 papers on LoRa/LoRaWAN were reviewed focusing on several challenges and solution methods for IoT applications. The analysis shows that LoRaWAN is effective for IoT deployment and confirmed the existence of scalability challenge which is still an open issue. Moreover, scalability issue often give rise to other challenges such as network coverage, capacity, and interference.
Dual-Band and Dual-Polarized Antenna for Wearable Application

A novel dual-band antenna is presented for wearable applications in this paper, covering 2.45 GHz (2.4- 2.485 GHz) and 5.8 GHz (5.725- 5.875 GHz) industrial scientific medical (ISM) bands. The proposed antenna applied by stacked structure can meet the demands of different polarization properties. Aims at off-body communication, a shorted circular patch is introduced to form a monopole-like radiation pattern with linear polarization property at 2.45 GHz. At a higher frequency of 5.8 GHz, a truncated patch is utilized for on-body communication at the bottom of the antenna. And circular polarization property is achieved.
Shapelet Feature Learning Method of BCG Signal Based on ESOINN

Ballistocardiogram (BCG) signal is an effective information that can be used to diagnose cardiovascular disease. This paper analyzes a method of learning the Shapelet feature of BCG signal based on ESOINN. Firstly, the original BCG signal is pre-learned using an enhanced self-organizing incremental unsupervised neural network (ESOINN); Then, it's transformed by the shapelet transform algorithm; Finally, the feature selection method is used to select the shapelet feature from the candidate set, and carry out the training of the classifier. The results show that the method can learn the better quality shapelet candidate set, and greatly reduce the number of candidate sets. In addition, the learning time complexity of shapelet features is greatly reduced, and the accuracy of the model is improved.
NOMA-Based Statistical QoS Provisioning for Wireless Ad-Hoc Networks with Finite Blocklength

The non-orthogonal multiple access (NOMA) has been designed to significantly enhance the spectral efficiency for the massive connections of the mobile devices. Moreover, to guarantee the short latency requirements for 5G multimedia wireless services, researchers have designed the statistical delaybounded quality-of-service (QoS) provisioning for supporting the video transmissions over the statistically varying wireless channels. Accordingly, researchers have proposed the finite blocklength coding (FBC) techniques to model the relationship between data transmission rate and channel capacity in the non- asymptotic regime while supporting short-packet communications under the QoS constraints. Due to its potential to significantly improve spectral efficiency and reduce the transmission latency, a NOMA system can be exploited while being integrated with FBC to guarantee the QoS requirements for both the latency and reliability over mobile wireless ad-hoc networks. However, due to the complexity of the effective-capacity maximization problem in the non-asymptotic regime, the FBC based NOMA scheme have imposed new challenges for determining the convexity of the optimization problem and deriving optimal resource allocation policies for NOMA subject to the statistical delay-bounded and error-rate bounded QoS constraints. In order to solve the abovementioned problems, we define a new concept of /spl epsilon/-effective capacity and propose a corresponding system architecture model for NOMA system under FBC. In particular, we characterize the FBC based NOMA system models in wireless ad hoc networks. Considering the statistical delay- bounded and error-rate bounded QoS constraints, we formulate and solve the max- min fairness problem under FBC. Simulation results are included, which evaluate and validate our proposed FBC based NOMA scheme subject to the statistical delay-bounded and error-rate bounded QoS constraints.
Interdependence of Electronic and Thermal Transport in AlxGa1–xN Channel HEMTs

Aluminum gallium nitride (AlGaN) high electron mobility transistors (HEMTs) are candidates for next-generation power conversion and radio frequency (RF) applications. AlxGa1-xN channel HEMT devices (x = 0.3, x = 0.7) were investigated using multiple in-situ thermal characterization methods and electro-thermal simulation. The thermal conductivity, contact resistivity, and channel mobility were characterized as a function of temperature to understand and compare the heat generation profile and electro-thermal transport within these devices. In contrast to GaN-based HEMTs, the electrical output characteristics of Al0.70Ga0.30 N channel HEMTs exhibit remarkably lower sensitivity to the ambient temperature rise. Also, during 10kHz pulsed operation, the difference in peak temperature between the AlGaN channel HEMTs and GaN HEMTs reduced significantly.
Deep Learning Based Preamble Detection and TOA Estimation

Accurate Time of Arrival (TOA) estimation has many use cases, including 5G initial access and localization. However, due to multipath propagation and noise, the correlation-based TOA estimation may not be accurate. In this paper, a deep learning based framework is proposed for preamble detection and TOA estimation without the need of knowing the transmit waveform. Extensive simulations on both synthetic data and real measured data show that the proposed method improves prediction accuracy by about three times while keeping the same computational complexity in comparison to the correlation method. It also provides 1000x computational reduction compared to the template matching method without loss of accuracy.
Joint Optimization of Task Assignment and Power Allocation for NOMA-Aided MEC Systems

In this paper, task assignment and power allocation are investigated for the the non-orthogonal multiple access (NOMA)-aided mobile edge computing (MEC) system. Based on the different channel conditions and central processing units (CPUs), users can offload computational tasks to the MEC server or process tasks locally. In order to minimize the energy consumption of the proposed NOMA- aided MEC system, the task assignment and power allocation optimization problem is formulated. Based on the insight derived from the delay constraint, the closed-form expressions of task ratios and transmit power are derived. Furthermore, the optimality of the derived closed-form solutions is analyzed, which shows that the optimal task assignment of any user is based on the energy consumption efficiency (ECE) of offloading and local computing. Simulation results indicate that: i) the derived closed-form solutions can significantly reduce the energy consumption of the NOMA-MEC system, and ii) the analysis of the derived closed-form solutions is confirmed.
High-Resolution Hierarchical Beam Alignment with Segmented Beams

We propose a high-resolution hierarchical beam alignment algorithm that learns millimeter wave channel space using segmented beams obtained from feasible phase shifted sum of given hierarchical basis. To be specific, at each stage, the segmented beam is designed using a linear combination of given training beam basis instead of just choosing a beam among them. Then, the designed beam is used to determine the subset of higher resolution training beam candidates for the next stage which is referred to as the over-complete dictionary embracing even partially overlapped beams as elements. At the next stage, the proposed beam alignment can focus only on the most promising directions associated with finer grids. The simulation results demonstrate that the proposed beam alignment framework can remedy the limitations of the existing grid-of-beams method and achieve favorable beam alignment performance with reduced training time.
High-Precision Symmetric Weight Update of Memristor by Gate Voltage Ramping Method for Convolutional Neural Network Accelerator

Memristor emerges as the key enabler for neural network accelerator. Here, we demonstrate high-precision symmetric weight update in a one transistor one resistor (1T1R) structure Ti/HfO2/TiN memristor using a gate voltage ramping method, with over 120-level states and low variation (< 4%). Incorporating all experimental non-idealities, the proposed mixed hardware-software convolutional neural network demonstrates over 92.79% online learning accuracy (against software equivalent 98.45%) for MNIST recognition task. The network also shows robustness to input image noises, array yield, and retention issues.
Minimum Separation Clustering Algorithm with High Separation Degree in Ultra-Dense Network

Ultra-dense network (UDN) can effectively improve the network throughput by increasing the deployment density of base stations. However, due to the randomness of the large number of base station deployments, UDN will lead to huge computational complexity and signaling overhead. The existing clustering algorithms cannot obtain effective clustering results in extremely dense scenarios. In this paper, we propose a minimum separation clustering (MSC) algorithm, which selects the split base stations (SBSs) to connect the multiple dense cluster base stations (CBSs). SBSs can reduce the interference between CBS clusters by using different radio resources from CBSs, because it has higher priority in the resource allocation procedure. Furthermore, the traditional clustering evaluation indexes such as the sum of square error are not applicable to UDN where the base stations are deployed randomly, and hence we design the separation degree function, which evaluates the clustering effect from the compactness within a cluster and the dispersion between different clusters. Simulation results show that the proposed algorithm can not only reduce the proportion of SBSs so as to improve the spectral efficiency, but also reduce the inter-cluster interference and network scale.
Power Allocation for UAV Swarm-Enabled Secure Networks Using Large-Scale CSI

Unmanned aerial vehicle (UAV) swarm-enabled aerial network has emerged as an effective solution to ondemand communications, especially in unexpected scenarios. Due to the broadcast nature of the air-to-ground link, UAV swarm-enabled wireless communications are inherently prone to eavesdropping. The paper investigates power allocation for UAV swarm-enabled secure networks. To depict air-to-ground link, a composite channel consisting of small-scale and large-scale fading is taken into account. Because of the difficulty in acquiring the time-varying small-scale fading, we use the large-scale channel state information (CSI). An optimization framework in a whole- trajectory-oriented manner is proposed to maximize secrecy throughput with the constraints on the transmission power and the transmission durations as well as the overall transmission energy per UAV over a given flight period. The formulated problem is not convex. To deal with that, we first derive a closed form of secrecy throughput in the form of high-order fixed-point equations. Then, we propose an iterative algorithm with successive convex approximation technique by alternately optimizing the variables. Numerical results validate the effectiveness of the proposed scheme and show that our proposed scheme can achieve a good secrecy performance.
Joint Calibration of Inertial Sensors and Magnetometers using von Mises-Fisher Filtering and Expectation Maximization

Microelectromechanical-systems-based inertial sensors and magnetometers are low-cost, off-the-shelf sensors that are widely used in both consumer and industrial applications. However, these sensors suffer from biases and effects such as axis misalignment or scale errors, which require careful system design and periodic sensor calibration. In this paper, we propose a fast calibration method for jointly calibrating inertial sensors and magnetometers based on discrete-time von Mises-Fisher filtering and expectation maximization. We evaluate the method on built-in smartphone sensors and show that the proposed method efficiently estimates the sensors' parameters and reduces the overall error.
Bubble: Lightweight Core Sharing in NFV

Many researches have revealed the requirement of enabling multiple network functions (NFs) to share a CPU core in Network Function Virtualization (NFV) to support fine-grained NF models, efficient resource utilization, and chain consolidation. However, these works usually enable core sharing via kernel-level threads, which incurs significant performance degradation. In this paper, we present Bubble to enable lightweight core sharing in NFV. Bubble leverages user-level threads to eliminate the performance overhead introduced by kernel-level thread scheduling. Bubble is designed to satisfy unique requirements in NFV by providing accurate and low-overhead scheduling, in support of on- demand resource allocation, and accurate NF load measurement. Evaluations over a Bubble prototype implementation demonstrate that Bubble can improve the performance by 1. 6Ã- to 6. 2Ã- for co-located NFs and by 3. 7Ã- to 68. 8Ã- for a consolidated Service Function Chain (SFC) in a core against two state- of-the-art solutions.