Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Peak-to-Peak Filtering for Networked Nonlinear Systems With Multipath Data Packet Dropouts

In this paper, the peak-to-peak filtering problem is studied for a class of networked nonlinear systems. The nonlinear physical plant is represented by the Takagi–Sugeno fuzzy system. Assume that the data packet dropout phenomenon occurs when the measurement output signal and the performance output signal of the nonlinear systems are transmitted by the digital communication channel. Two stochastic variables satisfying the Bernoulli random binary distribution are used to model this phenomenon. The motive of this paper is to design a peak-to-peak filter such that the filtering error system is stochastically stable and the prescribed peak-to-peak performance index is guaranteed. The developed theoretical results for designing a peak-to-peak filter are expressed in the form of linear matrix inequalities. Finally, a simulation example is presented to illustrate the validity of theoretical analysis.
InsFuzz: Fuzzing Binaries With Location Sensitivity

Fuzzing is a popular technique which is widely used to find the software bugs. However, fuzzing remains limited in finding bugs lying in deep paths since it has difficulty in bypassing the complex checks of the target program. In this paper, we propose a location sensitive fuzzing approach, named InsFuzz, that leverages the light-weight program analysis technique. We use the static analysis and binary instrumentation to infer the bytes that could influence comparison instructions, which we called key bytes, and, then, to infer the relationship between the key bytes and the comparison instructions during execution. This enables a fuzzer to know which bytes are worth mutating and how these bytes should be mutated. In addition, we collect the comparison progress information (i.e., we record the number of matching bytes between the two operands of an instruction) during execution and preserve the mutated inputs with higher comparison progress. Therefore, the fuzzer can break the comparison instructions efficiently. We first evaluated the InsFuzz on the LAVA-M dataset against other fuzzers, including AFL-Dyninst, and then compared InsFuzz with AFL-Dyninst on five real-world programs. The results show that InsFuzz found more bugs than the fuzzers that we compared with on the LAVA-M dataset. In addition, InsFuzz found some new bugs that the author of LAVA-M did not list. On the real-world programs, InsFuzz triggered more unique crashes and covered more code compared with the AFL-Dyninst.
Design and Optimization of Reconfigurable Data Path for Communication Baseband Signal Processing

A Coarse-Grained Reconfigurable Architecture called RASP2.0 is proposed in this paper for communication baseband signal processing. Based on the pipeline bubbles theory, the reconfigurable data path is divided into the data flow between processing elements and the data interaction between reconfigurable arrays and memory structure. To reduce the data transmission delay, the data flow features are summarized based on the locality and lifetime of data. By employing a parallel memory structure combined with the DLT-based data updating strategy, the access performance is improved by 33% on average compared with RASP1.0. As a result, the reconfigurable system presents more performance advantages and flexibility than other similar platforms.
Opportunities and Challenges in Designing and Utilizing Vertical Nanowire FET (V-NWFET) Standard Cells for Beyond 5 nm

Nanowire field-effect transistors (NWFETs) are known to become the emerging transistor type for better performance and low power for future technology nodes beyond 7 nm. Their unique structures allow the transistors to be designed horizontally or vertically, leading to a smaller form factor. However, it is not well studied on how much improvements NWFETs can achieve, especially when vertical FETs (V-NWFETs) are invoked in designs. In this paper, we investigate the advantages that NWFETs provide to standard cell designs. We propose an interconnect structure and a design methodology that optimize design metrics such as area, wirelength, and capacitance of V-NWFET standard cells. Then, we perform comparisons on these design metrics to standard cells between FinFET, horizontal NWFET (H-NWFET), and V-NWFET. This study shows that H-NWFETs achieve significant capacitance reduction compared to the conventional FinFETs (–30.1%). In addition, V-NWFETs achieve even more capacitance reduction (−50.0%), and significant reduction in area (−22.5%) and wirelength (−14.4%) compared to FinFETs. This is possible because (1) fin-to-metal coupling consists of a significant portion in total capacitance and NWFETs contribute to the large reduction of this capacitance and (2) standard cells using V-NWFETs reduce significant area and wirelength compared to conventional FinFET standard cells. However, careful design and proper interconnect structure are required to fully exploit the design advantages.
Wavelet-TSK-Type Fuzzy Cerebellar Model Neural Network for Uncertain Nonlinear Systems

In this paper, a novel fuzzy neural network structure for uncertain nonlinear systems is proposed. This network is called wavelet Takagi–Sugeno–Kang (TSK) fuzzy cerebellar model neural network, which includes the framework of a cerebellar model neural network (CMNN) and the wavelet-function-based TSK fuzzy inference model. In order to effectively solve the uncertainty problem of nonlinear systems, a new structure is proposed where the wavelet function is used in the consequent parts of TSK-type fuzzy CMNN instead of the linear combination of the input variables in the traditional TSK fuzzy systems. This structure combines the advantages of the wavelet function, the CMNN and the TSK fuzzy inference system; thus, it is a more effective model for the uncertain nonlinear systems. In order to provide fast training, parameter update laws of the proposed model are derived based on the gradient descent method in which the learning-rates are online adapted. Furthermore the Lyapunov function is used to analyze the convergence of the considered systems. Finally, four different types of applications are applied to demonstrate the effectiveness of the proposed model. The simulation comparisons with other neural network models have verified the effectiveness of the new model.
EVALUATION OF DROPLET SPECTRA OF THE SPRAY TIP AD 11002 USING DIFFERENT TECHNIQUES

Knowledge of droplet spectra generated by spray nozzle tips is important to ensure the quality of applications of plant protection products. However, there are different methods for this evaluation, which can cause difficulty in interpreting the results. This study aimed to evaluate the droplet spectra produced by the flat fan spray tip AD 11002 operated under different pressures and using different techniques and equipment. The volume median diameter (VMD), percentage of spray volume in droplets smaller than 100 μm diameter and relative spam (RS), considering pressures of 200, 300, and 400 kPa were determined using two direct measurement equipment (Spraytec and Shadow Sizer), based on laser diffraction and image analysis, respectively, and a measure based on indirect analysis, through the digitization of water-sensitive paper. Thus, the use of different analysis techniques led to variations of the analyzed parameters. Among the direct measurement equipment, a difference of up to 58% was observed in VMD. The use of water-sensitive paper to characterize droplet spectra must be carried out with great discretion because there is an underestimation of fine droplets. Pressure variations of 200 to 400 kPa did not influence VMD and RS.
Fabrication of the Nb3Sn/Cu CICC Coil and Cold Mass for the Radboud University HFML 45 T Hybrid Magnet

The High Magnetic Field Laboratory in Nijmegen has been collaborating with the National High Magnetic Field Laboratory (NHMFL) in Florida on a 45 T hybrid magnet project. The primary scope of the collaboration was the design and manufacture of the hybrid magnet's superconducting cold mass. The 7.5 ton cold mass includes a single 13 T solenoid wound with high JC RRP Nb3Sn/Cu cable-in-conduit conductor. The coil will be forced flow supercritical helium and operated in parallel with a set of Bitter-disk resistive coils. Coil winding, reaction heat treatment, epoxy impregnation, and cold mass assembly has been completed at the NHMFL. The full cold mass has been delivered to Radboud University and will be assembled with the cryostat and interfaced with the system utilities.
A Robust Compressive Sampling Method for MR Images Based on Partial Scanning and Apodization

This paper proposes a robust, fast Magnetic Resonance Imaging (MRI) reconstruction algorithm. The method is based on Compressive Sampling (CS), profile of the k-space coefficients and sparsity in the wavelet transform domain. It commences with partial acquisition of the k-space of the image followed by random sampling prior to reconstruction in the wavelet transform domain using a greedy algorithm. The reconstructed wavelet coefficients vector is transformed into the full k-space vector of the image by determining its Inverse Discrete Wavelet Transform (IDWT) domain. The vectorized form of the k-space reveals the reconstruction artifacts which makes it easy to design a denoising filter. The artifacts are then suppressed using an apodization function. The denoised coefficients are then reshaped into a k-space matrix prior to being transformed into the reconstructed image using two-dimensional Inverse Discrete Fourier Transform (2D-IDFT). The Structural SIMilarity (SSIM) and the Peak Signal to Noise Ratio (PSNR) quality metrics are used for quality assessment of the output images. Experimental results show that the proposed method yields an average PSNR improvement of 1.4 dB over the Orthogonal Matching Pursuit (OMP) method at 40% measurements. The improvement implies reduction in scan time by approximately 10% for a given image quality.
Tungsten Disulphide Nanosheets for High-Performance Chemiresistive Ammonia Gas Sensor

The sensing behavior of as-fabricated and annealed WS2 nanosheets towards ammonia (NH3) gas has been demonstrated in this study. The nanosheets were synthesized using liquid-phase exfoliation technique and the effect of the sonication time on gas sensing capability was assessed. It was observed that sonication time affects the ammonia sensing performance and samples sonicated for durations between 3–6 hours show good sensing performance, with the ones sonicated for 5 hours reporting optimal performance. The sensing response towards ammonia was demonstrated in the concentration range of 300 ppb (3.8%) to 3 ppm (79.10%) at an optimized sensing temperature of 250° C. These nanosheets were further annealed for two hours in nitrogen ambient and this significantly enhanced the response by an order of magnitude for the highest ammonia concentration of 3 ppm, i.e. from 79.1% to 1805%. The response and recovery time of the device after annealing were found to be improved in the concentration range of 0.4 ppm - 2 ppm of ammonia. Though the linearity of the device response in high concentration regime is affected by the annealing process, it enables the sensor to push the lowest limit of detection down to 50 ppb. The as-prepared nanosheets were characterized using different material characterization tools and the results are reported here. A plausible mechanism has also been proposed for this sensor based on the detailed chemical composition analysis.
Beam Tracking and Optimization for UAV Communications

Beam tracking for unmanned aerial vehicle (UAV) communications is an enabling technology that maintains the connection between UAV and ground base stations. In this paper, we propose an efficient training-based beam tracking scheme for UAV system. To track the beam, we need to answer how frequently the training should be performed. The proposed scheme uses joint beam training and angular velocity estimation, based on which the beam coherence time can be obtained. Accordingly, the training frequency can be adjusted based on the variation of beam coherence time to save the training overhead. Then, we study the maximization of flying range and speed of UAV. To this end, we optimize the beamwidth by choosing different codebook. For flying range maximization, we first propose an efficient algorithm that can obtain the optimal beamwidth and the corresponding maximal flying range at a given speed of UAV. To gain more insight, we then show that in most practical scenarios, the maximal flying range is achieved with the narrowest beamwidth. Furthermore, we show that when the flying range is small, broad beam should be used to support high speed of UAV, and vice versa.
Crowd Modeling Based Auto Activated Barriers for Management of Pilgrims in Mataf

Tens of millions of people visit every year the Grand Mosque in Makkah for Hajj and Umrah. Making Tawaf (circumambulating the Ka’ba) is an essential rite in Hajj and Umrah. During peak hours, crowd density in Mataf (the area around the Ka’ba) becomes extremely intense. Even when the crowd density in Mataf is less, kissing the Black Stone (a part of the ritual of making the Tawaf) is a daunting task. This is due to the lack of proper queueing of pilgrims wishing to touch and kiss the Black Stone. In this work, we propose a Finite State Machine (FSM) based model for simulating the crowd movement during Tawaf. The model can be implemented in real time crowd monitoring in Mataf. Based on the models outputs, a system for auto-activated barriers is proposed to arrange the pilgrims in queue who want to touch and kiss the Black Stone.
Detection and Cryogenic Characterization of Defects at the SiO2/4H-SiC Interface in Trench MOSFET

We employed the thermal dielectric relaxation current (TDRC) method for the detection and cryogenic characterization of traps at the 4H-SiC/SiO2 interface in n-channel trench MOSFETs and n-MOS trench capacitors. The interface of trench devices on the ( $\textsf {11}\overline {2}\textsf {0}$ )-plane atomically differs from the interface of standard lateral devices [(0001)-plane]. In the MOSFET, two TDRC signal peaks originating from electron traps were found and characterized by parameter variation in TDRC measurements. One peak corresponds to interface states located 0.13 eV below the 4H-SiC conduction band edge EC. The other one is attributed to the near-interface traps (NITs) with an electron emission barrier of 0.3 eV. The NIT peak shows an inverse dependence on the discharging voltage compared to regular interface states. In contrast to the MOSFET, only NITs were measured in n-type MOS capacitors. The results found in trench devices were also confirmed for lateral devices. Therefore, we show that the study of SiC MOS capacitors is not sufficient for the understanding of degradation mechanisms in SiC MOSFETs. The interface states at EC-0.13 eV detected in MOSFETs are assumed to contribute to the degradation of the apparent channel mobility and ON-resistance of SiC MOSFETs. These states are strongly reduced by annealing in NO compared to N2.
A Scaling Study of Excess OFF-State Current in InGaAs Quantum-Well MOSFETs

A scaling study of excess OFF-state current in planar InGaAs quantum-well MOSFETs is reported. We find that pure band-to-band tunneling (BTBT) dominates the drain OFF-state current in deviceswith channel length above <inline-formula> <tex-math notation="LaTeX">$\sim 1~\mu \text{m}$ </tex-math></inline-formula>. In this regime, the drain–gate voltage sets the rate of BTBT. In devices with channel lengths below <inline-formula> <tex-math notation="LaTeX">$\textsf {1}~\mu \text{m}$ </tex-math></inline-formula>, a parasitic bipolar transistor effect becomes relevant. The bipolar current gain is found to scale inversely with the effective channel length for Lg between 70 nm and <inline-formula> <tex-math notation="LaTeX">$\textsf {1}~\mu \text{m}$ </tex-math></inline-formula>, as expected from simple bipolar transistor theory. For Lg below ~7 nm, the OFF-state current increases rapidly due to an enhancement in bipolar current gain as a result of strong short-channel effects and punchthrough. Current gains in excess of 104 have been observed. A reduction in channel thickness decreases both BTBT and the bipolar gain effect.
A Dual Antenna GNSS Spoofing Detector Based on the Dispersion of Double Difference Measurements

This paper presents the development of a dual-antenna GNSS spoofing detection technique based on the analysis of the dispersion of the double differences of carrier phase measurements produced by two GNSS receivers. No synchronization of the receivers is needed for the algorithm to properly work. The algorithm is derived from the idea of the Sum of Squares (SoS) detector, recently presented as a simple and efficient way to detect a common angle of arrival for all the GNSS signals arriving to a pair of antennas. The presence of such a common angle is recognized as an undiscussed indication of non-authentic GNSS signals. Nonetheless, some limitations can be identified in the SoS algorithm. First of all, the assumption that all the signals arrive from the same source; situations are possible in which the receiver tracks only a subset of counterfeit signals, out of the whole signal ensemble. The idea presented in this paper intends to overcome such limitations, properly modifying the SoS detection metric to identify subsets of counterfeit signals. The analysis is supported by several simulation tests, in both nominal and spoofed signal conditions, to prove the effectiveness of the proposed method.
A Real-Time Distributed Deep Learning Approach for Intelligent Event Recognition in Long Distance Pipeline Monitoring with DOFS

Intelligent event recognition along the fiber is still a challenging problem in long distance pipeline monitoring with distributed optical fiber sensors (DOFS), because the complicated burying environments are changing from time to time, and the interference sources are unpredictable at different fiber locations. The fixed hand crafted feature extraction is always time-consuming and laborious, and the update of algorithm always lags behind the environmental change, which restricts its practical scale applications. Thus in this paper, we propose a real-time distributed deep learning model by using the efficient 1-D convolutional neural network (1-D CNN), to learn the distinguishable features of different disturbances and identify them automatically by training the raw event data (signal), which also can be updated easily. The experimental results from the real field data for the safety monitoring of oil pipelines demonstrate the effectiveness of the proposed method, which performs better than the 2-D CNN in terms of both recognition metrics and speed.
Modeling the Anomalous Flux of Proteins in the Human Kidney with the Diffusion Equation

One of the first signals of the so-called diabetic nephropathy is attributed the anomalous flux of albumin through the zone of urine formation. This can be sustained on the hypothesis that the glucose’s dipole moment would cancel the negative charges located over the inner layers of the renal glomerulus, leaving theses areas unprotected for stopping proteins and other charged compounds. In this paper, the diffusion’s equation treated in cylinder coordinates is used to calculate the albumin excretion rate (AER) considering numerical inputs that feature those patients whose diagnosis of type-2 diabetes was done in more than 10 years. Our estimate is in agreement with the ones obtained with standard techniques commonly used in nephrology, with a 7% of error of model and numerical approximations.
Characterizing Full-Duplex V2V Broadcast Performance through Stochastic Geometry

Broadcast traffic in IEEE 802.11 vehicular networks is known to suffer from poor performance due to the lack of recovery mechanisms from packet losses, based on the rules of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. This may have a detrimental impact on cooperative vehicular safety applications that build on the reliable regular broadcasting of status messages by vehicles in a local neighborhood. Full-Duplex (FD) techniques can improve the broadcast CSMA/CA performance by letting a sending vehicle sense the channel while transmitting, thus enabling “collision detection”. The vehicle, consequently, can abort the packet prone to collision and reattempt a later transmission. In this paper, we define a stochastic geometry model that captures the collision detection capability of FD-enabled vehicles, while accurately characterizing the interference power generated by other vehicles on a road segment, and the dynamics of the backoff mechanism used for broadcast packet retransmissions. The model provides helpful insights into the FD broadcast CSMA/CA behaviour, highlighting a clear relationship between the settings for the carrier sense threshold and the collision detection threshold and the number of covered receivers on the road.
Deep Learning-Based Classification of Weld Surface Defects

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.
Efficient Four-way Row-splitting Layered QC-LDPC Decoder Architecture

This paper presents a four-way row-splitting architecture for layered low-density parity-check (LDPC) decoder. Based on the proposed method, an efficient partially parallel pipelined QC-LDPC decoder is proposed. The synthesis results using TSMC 40-nm standard cell CMOS technology show that the proposed decoder achieves the maximum required throughput of 7.05 Gb/s and outperforms its predecessors in terms of area efficiency.
Back Surface Plasmonic Grating for Increased Quantum Efficiency of nBn Photodetectors With Ultra-Thin Metamorphic InAs0.8Sb0.2 Absorber

Using a 2D metallic grating, we demonstrate the resonant enhancement of the external quantum efficiency of an nBn structure with a 0.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick-absorber. A total of two structures were studied with different barrier materials: ternary AlInSb and an InAs/AlInSb superlattice (SL). The device using an SL barrier had a diffusion-limited dark current of 6.7 <inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula>/cm<sup>2</sup> at 150 K, which was four times lower than the ternary-barrier device. The surface plasmon polariton (SPP) resonance wavelength for devices with six different grating periods varied as predicted by simulations. The quantum efficiency (QE) was enhanced by up to 56% by coupling to the SPP mode. A peak external QE of 39% was achieved at <inline-formula> <tex-math notation="LaTeX">$4~\mu \text{m}$ </tex-math></inline-formula> with a 1100 nm grating period.