Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal water use mapping of a commercial apple orchard using UAS based spectral imagery

Crop water use estimation at high geospatial resolution is critical for site-specific irrigation management of perennial specialty crops. This study aims to map actual evapotranspiration (ETa) of a commercial apple orchard using unmanned aerial system (UAS) based thermal and multispectral imagery and a widely adopted METRIC (Mapping Evapotranspiration at High Resolution with Internalized calibration) energy balance model (UASM). Four imaging campaigns were conducted during the 2020 growth season and weather data for pertinent days was downloaded from the nearest WSU-AgWeatherNet network station. 24-h ETa was also calculated from the soil water balance (SWB) approach that used soil moisture data from sensors installed at three locations and down to depth of 111 cm. A high linear correlation (r) of 0.84 and non-significant difference (p = 0.5) was observed between UASM derived ETa (5.05 ± 0.8 [Mean ± Std. Dev.] mm day-1) and SWB calculated ETa (5.44 ± 1.81 mm day-1). Notable differences in spatiotemporal water use and crop-coefficients were observed within the orchard. A moderately strong correlation was also observed between the UASM derived crop-coefficients and multispectral imagery derived Normalized Difference Vegetation Index (r = 0.69) that may also be used for estimating actual crop water use. Overall, approach presented in this study may help identify under or over-irrigated areas within the orchard. It may also assist in developing site-specific irrigation prescription maps and schedules.
A Long Short Term Memory Network Based on Surface Electromyography for Continuous Estimation of Elbow Joint Angle

A simple long short term memory (LSTM) network is built to estimate the model which is described the relationship between the elbow joint angle and the surface electromyography (sEMG) signals in this paper. The sEMG time series of biceps and triceps are the inputs of the model, and the elbow joint angle is the output of the model. The sEMG signals while the user is performing flexion and extension movements are collected by Biopac. Elbow joint angle is measured by angle sensor. The results show that for simple flexion and extension movements, the model is able to eatimate the movement intention of the elbow.
A Fault Injection Framework for Real-time Multicore Embedded Systems

In this paper, we present a non-intrusive framework to inject faults in real-time multicore embedded systems without impairing the temporal characteristics of the critical tasks being executed, thus enabling a fine-grain simulation of the behavior of such systems under faulty conditions. The proposed technique consists of injecting specific traces to designated OS variables, hardware counters, and sensor values. The traces to be injected can either be defined at compile-time or generated at runtime. The fault injection framework builds upon a previously developed non-intrusive, real-time monitoring system. The fault injection mechanism actuates whenever a monitored variable is sampled by the monitoring system. The fault injection process is agnostic to hardware platform. The performance of the framework was assessed in terms of execution time overhead and of the impact on the execution of critical tasks. The results show a maximum execution time overhead on the system of 0.1001 % and an average task activation jitter not higher than 5. 0096μs. These results corroborate the suitability of the proposed framework to simulate the behavior of critical systems in the face of failures. After presenting the proposed architecture, the conducted experiments, and the achieved results, we discuss the application of the proposed technique in the scope of Digital Twins.
Discovering Web Things as Services within the Arrowhead Framework

The IoT is spreading heavily around us and many new standard at different architectural layers proliferate. This creates a heavy interoperability gap that many architectural proposals and new standards have tried to cope with over the years. In particular, we take into account the Arrowhead interoperability Framework, a powerful and promising paradigm for interconnecting local clouds and we demonstrate the possibility to integrate it with the emerging W3C Web of Things standard. We study the usage of the Arrowhead Framework as a potential candidate for discovery operation and inclusion of WoT ecosystems within Service Oriented Architectures (SOA), in order to support the integration of legacy and proprietary systems. To this aim, a three-layered architecture is proposed and validated experimentally on a proof-of-concept use case, by highlighting the advantages of the proposed integration.
A Discrete Random Drift Particle Swarm Optimization with Modularity in Community Detection

In the field of complex networks, community detection is one of important research objects. To solve the problem of poor quality and the unstable result with community structure, we propose a community detection optimization algorithm based on random drift particle swarm optimization (RDPSO) algorithm (DRDPSO-net), in which we use discrete method to update the network information. Through the discrete particle evolution process and local greedy strategy with network topology character, DRDPSO-net can obtain a better quality of community division. In addition, several representative real networks are used to verify the performance of DRDPSO-net. By comparing them across several algorithms, DRDPSO-net has more desirable value among those algorithms. Furthermore, the experimental results demonstrated that DRDPSO-net obtain a valid and steady community structure.
Symbolic execution based feature extraction for detection of malware

Traditional signature-based Anti-virus (AV) detectors use signatures unique to known malware. But these signatures are manually extracted and cannot detect variants of the same malware or zero-day malware. For automatically detecting zero-day malware and variants of the known malware, Machine Learning (ML) based approaches are proposed by researchers. For an ML-based malware detector, features are extracted using malware analysis techniques like static and dynamic analysis. While dynamic analysis techniques are useful for observing the runtime behavior of the malware (such as API call sequences), it was widely observed that malware can detect the debugging environment and modify its behavior. Static analysis on the other hand is limited by code obfuscation techniques and packers. In addition, analysis of the disassembled code is primarily a manual process due to indirect jumps/calls presented in the disassembled code. To solve these indirect jumps/calls and to automatically extract API call sequences, we propose a symbolic execution based static feature extraction system. This system emulates the binary symbolically and extracts multiple API call sequences as opposed to a single API call sequence obtained in dynamic analysis. These API call sequences are processed and used as input features for the ML classifier. The resulting system using Artificial Neural Networks (ANN) as a classifier, achieved a classification accuracy of 94.62% with the Area Under ROC Curve (AUC) of 0.9827. Combining the API call sequences with features extracted from headers of the Portable Executable (PE) binary, the accuracy improved to 97.42% with 0.9933 AUC.
AESA Adaptive Beamforming Using Deep Learning

In this work we propose a method for the adaptive beam-forming of an antenna array using Deep Learning. The proposed method is based on a deep Convolutional Neural Network that takes as input an image-like radiation pattern encoding the desired behavior and computes the optimal currents needed to adapt the antenna to the new beam specification. The proposed approach drastically reduces the computation time (up to 1700×) introducing a smart mapping of a classic iterative algorithm to an antenna to reproduce it. After training the model is able to compute optimal currents successfully in a single forward pass, avoiding the need of expensive iterative optimizations to find the needed currents.
A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images

Ship detection of synthetic aperture radar (SAR) images is one of the research hotspots in the field of marine surveillance. Fusing salient features to detection network can effectively improve the precision of ship detection. However, how to effectively fuse the salient features of SAR images is still a difficult task. In this paper, to improve the ship detection precision, we design a novel one-stage ship detection network to fuse salient features and deep convolutional neural network (CNN) features. Firstly, a saliency map extraction algorithm is proposed. The algorithm is applied to generate saliency map by using multi-scale pyramid features and frequency domain features. Secondly, the backbone of the ship detection network contains a two-stream network. The upper-stream network uses the original SAR image as input to extract multi-scale deep CNN features. The lower-stream network uses the corresponding saliency map as input to acquire multi-scale salient features. Thirdly, for integrating the salient features to deep CNN features, a novel salient feature fusion method is designed. Finally, an improved bi-directional feature pyramid network is applied to the ship detection network for reducing the computational complexity and network parameters. The proposed methods are evaluated on the public ship detection dataset and the experimental results shows that it can make a significant improvement in the precision of SAR image ship detection.
State-of-the-Art Technologies in RF Energy Harvesting Circuits – A Review

Nowadays electricity is undoubtedly one of the most important goods. Over the years, the dependence of people on electrical devices has sharply increased. The need for continuous use of these devices has created greater demand for electricity as well as more efficient transmission techniques. Environmental energy scavenging, as well as wireless transmission, is an increasing research field during the last years. The use of Radio Frequency (RF) Energy Harvesting (EH) technique contributes to the development of autonomous energy devices and sensors, to reduce the need of supplying them with power by using batteries or the mains. In this paper, the state-of-the-art technologies of radio frequency energy harvesting are discussed and analyzed.
Reconfigurable Multistage RF Rectifier Topology for 900 MHz ISM Energy-Harvesting Applications

Energy harvesting (EH) offers several benefits for integrating compact low-power wireless nodes into emerging microscale applications, such as the Internet of Things (IoT), wearable and implantable medical diagnosis, and so on. In this study, an optimum approach is developed to design an adaptive high-performance rectifier for EH wireless charging. The rectifier architecture is based on a simple voltage doubler configuration. The structure can be extended to achieve the desired output voltage level without altering the impedance matching network. The voltage doubler stage is duplicated symmetrically to increase the voltage to the desired levels. The matching circuit employs a simple L-network that consists of a series-inductive branch and a shunt LC branch. To verify the logical operation of this approach, two rectifier prototypes were developed, and the performance of each structure was separately evaluated and compared. The comparison shows good agreement between the simulation and measurement results of the two configurations. For example, the peak measured efficiency is 65% at an RF input power of 10 dBm, and the simulated value is 70% with a terminal load of 3.3 $\text{k}\Omega $ . The main objective of the proposed design is to enhance the output voltage without changing the matching network.
Flash LiDAR Imaging and Classification of Vehicles

This paper discusses the work on 3D image collection and classification of vehicles at Defence Science and Technology (DST) Edinburgh using a flash LiDAR system. The LiDAR uses a DST designed 32 by 32 Single Photon Avalanche Diode (SPAD) array to image various vehicles. The experimentally collected vehicle data was then extracted from the LiDAR images and various classification techniques were applied to them. Despite having a fairly small number of vehicle LiDAR images a classification accuracy of 86.3% was achieved using a convolutional neural network classifier. This initial approach offers the potential to eventually operate real-time classifiers from drones.
Gradient-Index-Based Frequency-Coded Retroreflective Lenses for mm-Wave Indoor Localization

This article introduces retroreflective lenses for millimeter-wave radio-frequency indoor localization. A three-dimensional (3D) gradient-index Luneburg lens is employed to increase radar cross section (RCS) of photonic-crystal high-Q resonators and its performance is compared to conventional radar retroreflectors. A classic Luneburg lens with and without a reflective layer is realized with 25 mm diameter ( $6.7~\lambda _{0}$ ), showing a realized gain of 24.6 dBi and a maximum RCS of −9.22 dBm2 at 80 GHz. The proposed Luneburg lens with embedded high-Q resonators as frequency-coded particles in a photonic crystal structure, operating as a reflective layer, achieved a maximum RCS of −15.84 dBm2 at the resonant frequency of 76.5 GHz and showed a repeatable response each 18° over ±36° in two perpendicular planes. With this high RCS of the Luneburg lens, a maximum readout range of 1.3 m could be achieved compared to 0.15 m without the lens at 76.5 GHz for the same transmit power, receiver sensitivity, and gain of the reader antenna.
A Comprehensive Analysis of the Android Permissions System

Android is one of the most essential and highly used operating systems. Android permissions system is a core security component that offers an access-control mechanism to protect system resources and users’ privacy. As such, it has experienced continuous change over each Android release. However, previous research on the permissions system has employed static analysis techniques. Furthermore, most of these studies are outdated, covering older versions of Android. This paper aims to discuss the permissions system intensively to provide a nutshell overview of the Android platform’s access-control mechanism. The paper presents a comprehensive analysis of the Android permissions system since it was introduced in 2008 until now, accompanied by a formal model of its components. The results of the analysis reveal a continuous growth in the number of permissions since the original release—a growth of seven times in some permission categories. A case study has been conducted for the last five years’ versions of the top Android apps to examine the permissions system’s evolution and its attendant security issues from the applications’ perspective. Some apps showed an increase in permissions usage of 73.33% by the 2020 release. Additionally, the results of the case study contribute to the understanding of permissions deployment by both vendors and developers. Finally, a discussion of the permission-based security enhancements discloses that the Android permissions system faces various security issues. In general, this paper provides researchers and academics an up-to-date, comprehensive, self-contained reference study of the Android permissions system.
H-Tree Clock Synthesis in RSFQ Circuits

Rapid single flux quantum (RSFQ) technology promises high performance, energy efficient supercomputers to achieve DoE exascale computing. An important challenge in VLSI complexity RSFQ circuits is ultra-high speed clocking and synchronization. A methodology to support the automated design of clock distribution networks for SFQ circuits is the focus of this paper. Global clock synthesis of SFQ circuits is applied to produce a near zero skew clock network. An algorithm is presented that produces an SFQ H-tree network with asymmetric leaves to propagate the clock signal to all SFQ gates. A Python-based system with location information for the interconnects, splitters, and leaves is described. The algorithm determines the physical locations and connections within the SFQ clock network and is applicable to automated routing and clock tree synthesis.
Holistic Control Approach for the Grid-Connected Converter of a Battery Energy Storage System

This paper introduces a holistic control approach applied to a grid-connected converter of a Battery Energy Storage System (BESS). The BESS is mainly used for power peak shaving, frequency supporting, and islanded operation mode which implies multiple control transitions depending on the operation mode. One existing challenge is related to the multiple tuning controllers for the grid-connected converter required for each operation mode, including the rectifier operation. Moreover, when the rectifier operation is running the system is naturally nonlinear which motivates the use of nonlinear controllers. From this standpoint, a strong motivation for this research is the seeking of a holistic control to face all the operation-mode changes seamlessly. Hence, the merit of the proposed holistic control approach consists of using a unique tuning process based on the linear technique of state feedback with integral action for all operation modes, which are: 1) grid-connected mode as an inverter, 2) grid-connected mode as a rectifier, and 3) islanded mode. Of special attention is the grid-connected mode as rectifier since the proposal avoids the introduction of the PWM rectifier model which increases the model order and is inherently nonlinear. A thorough analysis of eigenvalues in open loop and closed loop with the same tuning gains is presented to demonstrate the stability and feasibility of the proposal. An affordable tuning methodology is proposed considering the physical restrictions of the grid-connected converter related to the LCL-filter bandwidth and the switching frequency. To demonstrate the merits of the holistic control approach, several simulations are presented using PSCAD/EMTDC on a 100 kW, 480 V rated BESS using a delta-connected LCL filter for interconnecting with the grid. Simulation results show that the seamless capability with steady-state and transient state operation runs smoothly.
In-FPGA Instrumentation Framework for OpenCL-Based Designs

The productivity achieved when developing applications on high-performance reconfigurable heterogeneous computing (HPRHC) systems is increased by using the Open Computing Language (OpenCL). However, the hardware produced by OpenCL compilers in field-programmable gate arrays (FPGAs) can result in severe performance bottlenecks that are challenging to solve. The problem is compounded by the fact that the generated netlist details are disorganized, making them mostly unreadable and only partially visible to designers. This paper proposes an in-FPGA instrumentation method and a new framework for extracting the FPGA-cycle-accurate timing performances of OpenCL-based designs. The results clearly show that the chosen execution model for OpenCL-based designs strongly affects the timing performance when it is not properly implemented. Our framework is implemented on an HPRHC platform that contains a CPU and two Arria10 FPGAs, and it is evaluated with a wide variety of benchmarks with different complexities. After testing on the reported benchmarks, the average logic overhead for one inserted instrument is 0.2 % of the total amount of adaptive look-up tables (ALUTs) and 0.1 % of the total registers in an FPGA. This resource utilization is between 1.5 and six times lower than those reported in the best previously published works. The scalability of the framework is also evaluated by inserting up to 50 instruments. The experimental results show that the average logic utilization per instrument is 0.19 % of the ALUTs and 0.17 % of the registers in the FPGA when 50 instruments are inserted.
Tracking Oxygen Consumption in 3D Cell Cultures with Phosphorescent Biosensors

To increase the reproducibility of cell culture studies, parameters such as local oxygen concentration should be reported. Moreover, the oxygen information provided by continuous monitoring allows for more control and can be used to change the dynamics of cell culture systems rapidly, even alter stem cell fates. We have measured dissolved oxygen concentrations inside standard tissue culture plates containing hydrogel scaffolds with primary human intestinal epithelial stem cells or human umbilical vein endothelial cells. We also used a microphysiological system to allow for continuous low oxygen, or hypoxic, culture of human stem cells. We found that the local oxygen concentration at the cell-media interface was much lower than the oxygen available in normobaric incubators, and that the cells may adjust to local oxygen level by altering their metabolic consumption rates.
Comparison and Analysis several Wavelet from Daubechies Family in Image Compression

The need for quality images affects the large storage requirements and difficulties in transmission. Compression is a solution to the efficiency of storage and transmission. The Wavelet technique has the potential of providing high-quality compression. In this study, several wavelets from the Daubechies family were applied in image compression. Measurement of compression ratio, Mean Square Error (MSE), and peak signal-to-noise ratio (PSNR) determine the ‘acceptability’ of the compression. Our test with the ‘Lena’ image found that the compression ratio based on all wavelets gave almost identical results, but the db2 wavelet gave the smallest compression ratio and db36 gave the largest compression ratio. In terms of fidelity, DB5 gives the best results.
Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

Highway tunnels have a higher risk of crashing than open roads, which require a systematic approach to tunnel safety. However, previous research had the following problems: 1) Studies have largely focused on open roads, with very little research on tunnels. 2) The collected crash contributing factors involve narrow ranges, with very little tunnel crash data including both tunnel design features, traffic conditions and pavement conditions. 3) None of the studies considered both excess zero observations and unobserved heterogeneity with its interactions. To address these issues, this paper first established an appropriate tunnel dataset containing 3 to 5 years of crash data from several highways in China and the influence factors of tunnel design features, traffic conditions and pavement conditions. A correlated random parameters negative binomial Lindley (CRPNB-L) model that considers both excess zero observations and unobserved heterogeneity with its interaction effects was then proposed. Compared to the uncorrelated random parameters negative binomial Lindley (URPNB-L) model, fixed parameters negative binomial Lindley (FPNB-L) model and fixed parameters negative binomial (FPNB) model, the CRPNB-L model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity with its interactions by introducing correlated random parameters. In the comparisons, the CRPNB-L model achieves the best effects in the goodness-of-fit. Furthermore, the estimated results of the CRPNB-L model showed that segment length, traffic volume, proportion of class 5 vehicle (heavy trucks and trailers), tunnel entrance and exit segments, and steep uphill and downhill segments were associated with higher crash frequency, while curvature, tunnel length, pavement damage condition index (PCI) and skid resistance index (SRI) were associated with lower crash frequency. In addition, the random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and SRI were identified and their intercorrelations were analyzed.