Miracle-dz/UniFAR-FTUs huggingface dataset:
FPGA Implementation of the Single Thalamic Neuron Model

Thalamic neurons play an important role as a relay station for the transmission of sensorimotor signal to the cortex. In this study, a Hodgkin-Huxley (HH) type thalamic neuron model was designed and implemented on Field Programmable Gate Arrays (FPGA). In order to reduce hardware resources, the Finite State Machine (FSM) was proposed to control the data flow of Look-Up-Tables (LUTs) modeling nonlinear functions of the thalamic model. Besides, LUTs were controlled to seek different values in different clock periods. The simulation error between hardware and software implementation methods are analyzed. Compared to the original implementation that each nonlinear function is implemented with one LUT, the hardware implementation method proposed in this paper reduces memory resources by about 61 %, but increases the logical resources by 24%. It reduces the overall cost of resource, which lays the foundation for us to realize the large-scale neural network.
Fabric Defect Detection Method Based on Sparse and Dense Mixed Low-rank Decomposition

On account of the issue that there is severe noise in the detection of defects by the traditional low-rank decomposition defect detection method, in this paper, we present an efficient fabric defect detection approach which utilizes the sparse and dense decomposition on the base of low-rank representation. Firstly, the fabric image is uniformly segmented into image blocks. Each image block is spanned into a column vector, which is assembled to constitute the fabric image feature matrix. Then, the sparse and dense mixed low-rank decomposition model is constructed with the introduction of the F norm. The presented model is optimized by alternating direction multiplier method (ADMM) and augmented Lagrange multiplier (ALM), and the low rank array, dense matrix and sparse array are obtained. Finally, a thresholding segmentation approach is employed to detect the defect area by partitioning the salience map. Experimental results demonstrate that the proposed method achieves an efficient detection property, and it is superior to the current approaches.
Bit Allocation based on Visual Saliency in HEVC

As one of the important part in the HEVC reference software, R-lambda model adopts mean absolute difference (MAD) for the coding unit tree (CTU) level bit allocation. However, this optimum method may neglect some important characteristics of human visual system (HVS). In this paper, we propose a novel bit allocation algorithm to process some salient visual information priority. Firstly, an improved video saliency detection algorithm is proposed, which induces temporal correlation into a 2D visual attention model. Secondly, the visual saliency based CTU level bit allocation algorithm is presented by allocating bits for CTUs with their saliency weights. What’s more, with considerations of the temporal quality consistence among Saliency Areas (SAs), a window based weight smoothing model is proposed to achieve better subjective quality. Finally, several experiments are performed on the HEVC reference software, HM16.9, under the low delay P configuration, and the experimental results show that the average BD-Rate of the entire test sequences and of the SAs reduce 1.7% and 6.2%, respectively. The proposed algorithm can also improve subjective quality remarkably.
Estimating Power Consumption of Air-conditioners Using a Sequence-to-sequence Model

We suggest a neural network based power estimation algorithm for air-conditioners exploiting a sequence-to-sequence model which is widely used in the field of natural language processing. Our proposed method exploits both past usage patterns and weather forecasts for better prediction because air-conditioners have high dependency on the environmental factors. Our proposed algorithm is trained and validated on real-world data collected in various regions of the Republic of Korea. For validation, we adopt two evaluation metrics, mean absolute error and mean absolute percentage error. We accordingly suggest two different types of accuracy metrics practical for end users. Applying the proposed method to real-world data from air-conditioners, we have 5.7 kW of mean absolute error and 23 percent of mean absolute percentage error. Accuracy out of each metric is 85.02 percent and 74.65 percent respectively which outperforms the conventional regression algorithms.
A Flexible and Efficient Real-Time ORB-Based Full-HD Image Feature Extraction Accelerator

This article proposes a hardware accelerator for image feature extraction based on the oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (ORB) algorithm. The architecture adopts a hybrid workflow to process three scales in parallel as an octave, and multiple octaves are processed in serial by time-sharing the DRAM. In order to support any image resolution, a block-wise dataflow is used on the serial-parallel architecture. The overlapped data between two blocks is reused. As a result, the on-chip memory is limited to 1.47 Mb, and the DRAM bandwidth is compressed by 33%. Besides, $3 \times 3$ non-maximum suppression with heap sorting is applied to balance keypoint distribution in 2-D. It improves the valid keypoint match ratio by 7.09%. In addition, parallel processing in keypoint detection increases $2 \times $ throughout. Moreover, approximate computing and superscalar processing reduce the timing cost in orientation estimation and descriptor generation by 92.78% and 67% respectively. Compared to the non-optimized baseline architecture, the proposed architecture saves the total timing cost by 82.4%. The accelerator is implemented in a Xilinx MPSoC, and it achieves 108 fps on full-HD images at 200 MHz under 873 mW.
A Ring Oscillator PUF Architecture With Enhanced Challenge-Response Set

Physically Unclonable Functions (PUFs) are new hardware solution for low cost and secure communication primitives such as hardware authentication and secure key generation. PUFs use the random properties of a device to produce unique data. Ring Oscillator PUFs (RO PUF) are one of the most applicable PUFs because of its good reliability and easy implementation on FPGAs. One major problem of Ro PUFs is limited Challenge-Response pairs number. We consider this problem and introduce a new architecture of RO PUF that increases Challenge-Response pairs of a typical PUF with using minimum resources. In our architecture, we use an XOR gate to prevent direct response disclosure and increase our response unpredictability. In this manner, we increase Challenge-Response pairs up to $\frac{{n\left( {n - 1} \right)}}{2}$. We also show the result of the unity and uniqueness of our proposed PUF by implementing 110 PUFs on five XC3S400 FPGAs.
Clock Synchronization in Context of the Distributed Co-Simulation Protocol

Today co-simulation methodologies are widely used for automotive system development. This approach supports distributed development and test of subsystems, which are engineered by different suppliers. The Functional Mock-Up Interface (FMI) as an open standard enables seamless integration of simulation models, tools and solvers. The recently published Distributed Co-Simulation Protocol (DCP) is an open standard application layer communication protocol. It represents a solution for the integration of models or real-time systems into simulation environments. While the DCP was designed for low overhead and realtime capable operation, it does not specify the exchange of time information during simulation. The DCP currently relies on external mechanisms for time synchronization. In this paper, we propose an extension to the DCP, enabling time synchronization between DCP slaves and the master. This enables time triggered exchange of simulation data, applicable to the DCP's real-time and non-real-time operating modes. Therefore it becomes possible to synchronize time between participants of a distributed co-simulation scenario. Many applications may benefit from our approach, including hardware- or software-in-the-loop scenarios, or mixed real-virtual prototypes for e.g. automated driving. We demonstrate the functionality of our extension with a use case from the automotive domain, which implements a lane keeping assist system.
Discovering Future Malware Variants By Generating New Malware Samples Using Generative Adversarial Network

Detecting malware sample is one of the most important issues in computer security. Malware variants are growing exponentially by more usage of computer in industries, homes, and other places. Among different types of malware samples, zero-day samples are more challenging. The conventional antivirus systems, which rely on known malware patterns, cannot detect zero-day samples since did not see them before. As reported in [1], in 2018, 76% of successful attacks on organization endpoints were based on zero-day samples. Therefore, predicting these types of attacks and preparing a solution is an open challenge.This paper presents a deep generative adversarial network to generate the signature of unseen malware samples; The generated signature is potentially similar to the malware samples that may be released in the future. After generating the samples, these generated data were added to the dataset to train a robust classifier against new variants of malware. Also, neural network is applied for extracting high-level features from raw bytes for detection. In the proposed method, only the header of the executable file was used for detection, which is a small piece of the file that contains some information about the file. To validate our method, we used three classification algorithms and classified the raw and new representation using them. Also, we compared our work with another malware detection using the PE header. The results of this paper show that the generated data improves the accuracy of classification algorithms by at least 1%.
Image Shadow Removal Based on Residual Neural Network

The removal of image shadows has always been a challenging task that requires us to detect shadows and understand the surrounding scenes. The existing method of shadow detection and removal first locates the shadow area by shadow detection,and then uses some reconstruction algorithms to remove the shadows of the umbra and penumbra. However, detecting shadows is already a very rare task. Based on the traditional physical methods can be applied to a high quality image, and a method based on statistical characteristics must manually tag shadow. In this paper,we use a convolutional residual neural network to train the model. Using the residual neural network, we can prevent degradation due to the excessive number of network layers. The trained model can detect the shadow area by inputting the global image and combining the semantics of the picture. In these two aspects, good shadow area detection and positioning can be obtained, and image shadow removal can be realized.
FBM: A Simple and Fast Algorithm for Placement Legalization

In VLSI physical design, the legalization step has to deal with thousands of logic cells that need to provide a placement solution with all cells aligned within the rows and overlap-free, while preserving the global placement solution, i.e., displacement should minimize. We present an algorithm for VLSI placement legalization that minimizes the impact of wirelength in high-density circuits. Our approach is based first on sorting the cells according to their positions and legalizing each pair of overlapping cells. The algorithm moves the second overlap cell to the right until it finds a legalized placement with the lowest movement. Our experimental results show that our algorithm can reduce the runtime with a minimum impact on wirelength when compared to another legalization tool.
Open-Set Bottle Classifying using a Convolution Neural Network

A multi-class image classification application plays a vital role in our lives. Traditional approaches focus on a close-set classification problem. However, an open-set classification problem often occur in the real-world applications. This paper focuses on the convolution neural network based image classification for beverage bottle image classification under the open-set environment, in which the input image may not appear in any known classes during training time. The proposed models explore the approaches based on the N-Binary, N+unknown, and N+combination models. The results show that N+unknown approach perform better than that of the N+combination and N-Binary approach in terms of accuracy and time.
DA-Capnet: Dual Attention Deep Learning Based on U-Net for Nailfold Capillary Segmentation

Automatic nailfold capillary segmentation is a challenging task owing to noise and large variabilities in images caused by insufficient focusing and low visibility of the capillaries. This task can be useful to detect and estimate the severity of autoimmune diseases of connective tissues or learning the status of white blood cells based on the cells’ blood flow on the nailfold capillary. Previous studies have addressed this task using manual, semi-automated, and automated segmentation method. However, further improvement is still required. With the recent progress of deep learning on medical imaging, we herein propose dual attention deep learning based on U-Net for nailfold capillary segmentation, named DA-CapNet. Our DA-CapNet improves the U-Net architecture by integrating a dual attention module that can capture a better representation of feature maps from input images. Furthermore, DA-CapNet is compared with three baselines: adaptive Gaussian algorithm, SegNet, the original U-Net. We experimentally demonstrate that our proposed method outperforms these baselines.
Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users’ queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy.
Conceptualising Green Awareness as Moderator in Technology Acceptance Model for Green IS/IT

Green Information System/Technology adoption is one of the key solutions sought by organisations, policy makers and governments to promote sustainability and deal with environmental issues. Surprisingly, in the research discipline of management information systems measuring the intention of decision maker to adopt Green IS/IT is ignored while only a few studies address the issue of Green IS/IT adoption. But these studies are mostly done in organisational manner and consistently lack to conceptualise the role of Green Awareness or environmental literacy of the end user that may play the role of the facilitator to such adoption models and can significantly moderate the relationship of users' cognitive and behavioural intention factors in decision making process of adopting Green IS/IT. To fill this gap in the Green IS/IT literature, this paper conceptualise the role of Green Awareness as a facilitator by incorporating a subjective green awareness rating scale as a moderator in Technology Acceptance Model. This paper contributes to the existing knowledge in the science of information systems, mapping users' intention to adopt Green IS/IT and sustainability by conceptualising green awareness rating scale for users and a theoretical framework of incorporating the scale in Technology Acceptances model to map its role as a moderator.
Inversion-Coefficient-Aware Yield Optimization of Analog Circuits

An automated circuit sizing approach is presented, that uses the inversion coefficient to constrain each transistor in the desired inversion region. An analytical expression for the inversion coefficient based on the BSIM4 model is presented. The inversion coefficient of each transistor is calculated from the simulation results, transistor design parameters and technology model parameters. The optimization approach is illustrated on a folded-cascode operational amplifier. Each individual transistor is constrained in a specific inversion region, based on its requirements for gain, speed and matching. The consideration of inversion constraints speeds up the yield optimization.
Thread-Level Locking for SIMT Architectures

As more emerging applications are moving to GPUs, thread-level synchronization has become a requirement. However, GPUs only provide warp-level and thread-block-level rather than thread-level synchronization. Moreover, it is highly possible to cause live-locks by using CPU synchronization mechanisms to implement thread-level synchronization for GPUs. In this article, we first propose a software-based thread-level synchronization mechanism called lock stealing for GPUs to avoid live-locks. We then describe how to implement our lock stealing algorithm in mutual exclusive locks and readers-writer locks with high performance. Finally, by putting it all together, we develop a thread-level locking library (TLLL) for commercial GPUs. To evaluate TLLL and show its general applicability, we use it to implement six widely used programs. We compare TLLL against the state-of-the-art ad-hoc GPU synchronization, GPU software transactional memory (STM), and CPU hardware transactional memory (HTM), respectively. The results show that, compared with the ad-hoc GPU synchronization for Delaunay mesh refinement (DMR), TLLL improves the performance by 22 percent on average on a GTX970 GPU, and shows up to 11 percent of performance improvement on a Volta V100 GPU. Moreover, it significantly reduces the required memory size. Such low memory consumption enables DMR to successfully run on the GTX970 GPU with the 10-million mesh size, and the V100 GPU with the 40-million mesh size, with which the ad-hoc synchronization can not run successfully. In addition, TLLL outperforms the GPU STM by 65 percent, and the CPU HTM (running on a Xeon E5-2620 v4 CPU with 16 hardware threads) by 43 percent on average.
Noisy Reading Correction in Low Power MPPT using Kalman Filter

Kalman filter is designed to predict the correct values for noisy measurements, using a probabilistic model. However, the chosen system model has a huge impact on the outcome of this algorithm, which makes it very important for the model to be as representative of the system as possible. A new approach to handle the noisy measurements in low power Maximum Power Point Tracking (MPPT) algorithms is presented in this paper. The proposed filtering proved beneficial in terms of algorithm stability.
Exploring the Impact of Node Correlation on Transmission Reuse in MANETs

The main advantage of multicast over multi-unicast is the existence of transmission reuse, i.e., the cooperation among destinations. This intrinsic characteristic benefits the multicast regarding the enhancement of transmission efficiency. Consequently, multicast is extensively studied under various wireless environments. Yet people still have limited understanding on the impact of node mobility on the transmission reuse when multicast is applied instead of multiple unicast. In this paper, we focus on the correlated mobility which captures the feature of real mobility processes, to study its influence on transmission reuse in mobile ad hoc networks (MANETs). Specifically, we quantify the transmission reuse as multicast gain, i.e., the capacity ratio of multicast and multi-unicast under certain delay constraint. We design a multi-layer routing protocol and propose different kinds of causal scheduling policies, under which the overall multicast capacity-delay tradeoff is derived by exploring various correlation degrees of node mobility. Compared with the capacity-delay tradeoff in unicast case, we calculate the multicast gain. Results show that the correlation of node mobility greatly influences the multicast gain, and in certain cases network can achieve the upper bound of multicast gain regardless of the logarithmic factor.
WEED-MC: Wavelet Transform for Energy Efficient Data Gathering and Matrix Completion

Compressed sensing based data gathering is in-efficient in small scale wireless sensor networks because of uncorrelated observations at the sink. Failure to exploit the low rank and suitable transform domain to explore the correlation structure of sensor data, results in significantly low recovery accuracy in such matrix completion algorithms for small scale networks. Targeting the spatio temporal correlation structure of the data, a novel data gathering and matrix completion scheme, which exploits the low rank property and compactness of sensor data which exists in wavelet transform domain, is proposed in this work. The compactness of the sensor data in wavelet transform domain is used for recovering the missing entries of the matrix. Experiments and simulations, for single-node and multi-node scenarios, prove the efficacy of the proposed approach over existing schemes significantly in terms of recovery accuracy even at extremely low sampling rate.
Proposal and Evaluation of Pin Access Algorithms for Detailed Routing

Routing is a crucial step in the physical design of integrated circuits (ICs). Modern electronic design automation tools delve deep into detailed routing algorithms and pin access methods. The need for fast and efficient algorithms is an emerging challenge for physical design as design rules become more complex. The use of a pre-processing step to find pin access points helps to reduce the problem of detailed routing. This work proposes three different methods for access point generation and presents possible pitfalls when approaching the problem.
Boosting the Energy Efficiency of a Nanosecond Pulsed Corona Plasma System With a Multiple-Wire Plasma Reactor

Transient plasma generated by high-voltage nanosecond pulses in a pulsed corona configuration has proven to be an efficient medium for e.g., air purification applications. In this article, we present a study on boosting the energy efficiency of such a system by using a special type of plasma reactor: the multiple-wire plasma reactor. The basic principle of such a reactor is that it has a lower transmission-line impedance than a normal wire-cylinder reactor while at the same time still producing the required high electric fields necessary for plasma generation. The lower impedance of the reactor results in a smaller impedance mismatch between the high-voltage pulse source and the reactor, thereby minimizing pulse reflections on the source-reactor interface, increasing the energy efficiency of the system. We calculate the impedance of the multiple-wire reactor and assess both the electrical energy efficiency as well as the chemical efficiency of the plasma by measuring electrical energies and ozone concentrations generated by the plasma when energized by a fast solid-state nanosecond pulse source (with 7-ns rise time pulses of up to 20kV) for a range of multiple-wire plasma reactor parameters. We show that with the multiple-wire reactor we can almost double the energy efficiency of the reactor (from 45% to 85%), reduce the voltage stress in the system by 29%–31% and increase the system efficiency by 95%–98% when using a multiple-wire electrode geometry (as compared to a single-wire system).