Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge-based time registers for z-1 implementation

Time-mode signal processing is an advantageous approach in which the time is the processing variable. Time register is used in the construction of time-mode z-1 operator which is the basic building block in the time-mode circuits and systems such as FIR/IIR filters and time amplifiers implementations. In this work, a comparison study between z-1 operators using different types of time register circuit is presented. The circuits of time-registers and z-1 operators are implemented in TSMC 65nm and are verified through simulations in relation to absolute error, signal-to-distortion ratio and current consumption.
Design and Verification of 1X5 ROUTER

A router is a device that forwards the data packets along with the networks, commonly two LANs or WANs. It is an Open Systems Interconnection (OSI) layer 3 routing device. Depending on the address content in the incoming packets, this router routes a packet to an output channel. The packet has 3 parts, namely Header, payload and parity. In this project, a router of packet width 8 bits, payload length ranging from 1 byte to 31 bytes is implemented. This design is implemented in Verilog, verification is done using Universal Verification Methodology (UVM) and QuestaSim is used for functional coverage. This router consists of 4 submodules, namely First In First Out (FIFO), registers, Synchronizer and Finite State Machine (FSM). Five FIFOs are employed here and the final top module design is simulated and synthesized. The advantage of this router design is that it can receive only one packet at a time but five packets can be read out simultaneously.
Effects of the distance among multiple spreaders on the spreading

It is very important to investigate the multiple spreaders' effects since the spreading phenomenon is ubiquitous in many complex systems. In this letter, we investigate the effects of the distance among the initial multiple spreaders for regular networks and WS (Watts-Strogatz) small-world networks based on the SIR (Susceptible-Infected-Recovered) model. Assuming the epidemics can spread over the network, the theoretical and experimental results show that for regular networks the larger the distance between spreaders is, the more effective the spreading is. For WS networks, the spreading efficiency will decrease when the distance exceeds a certain value, and a larger connection probability and average degree will result in a smaller distance of the most effective spreading. A better spreading strategy using n spreaders is to choose either the highest k or ks nodes with the condition that there are not any pairs of the n spreaders linked directly (Kitsak M. et al., Nat. Phys., 6 (2010) 888). However, we find that the spreading will be more effective when the distances among the largest-degree spreaders increase. All these results are independent of the network size for the two initial spreaders case. This work may give new insights to explore more effective methods to inhibit the epidemic spreading or increase the information diffusion.
An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models

Short-term heavy rainfall can have a significant impact on people's production, life and travel. Numerical Weather Prediction (NWP) is complex. It can predict weather conditions for the next week or even two weeks, but cannot predict the weather in the near hours in a timely manner. The TREC and Optical Flow methods used in the meteorological field are only good at translational transformations, and it is difficult to predict the generation and dissipation of cloud systems. ConvLSTM, PredRNN, and other deep RNN-based methods input only one map at each time step, and do not make good use of the spatial information of several neighboring moments. Inspired by schedule sampling, a new sampling scheme is proposed to allow the model to effectively use the spatial information of the past few times. Also, the advantages of MSE and MAE are combined to change the loss function.
An Ensemble Approach for IoT Firmware Strength Analysis using STRIDE Threat Modeling and Reverse Engineering

Internet of Things (IoT) market is growing exponentially and automated smart solutions are revolutionizing a diverse range of areas with innovative technologies. The most critical and vital part of an IoT system that cannot be overlooked at any cost is its security. The security standards for IoT devices are not mature enough to provide foolproof security and there is still a long journey for manufacturers to incorporate stealth in devices. The most vulnerable component of an IoT system is the firmware which controls all the functionality of the device. If subverted by an attacker, the firmware of the IoT device can prove to be a critical attack surface for obtaining enough information to annihilate an IoT device. In this paper, we propose a twofold strategy to critically analyze the security of an IoT firmware. We will first use the STRIDE threat model to identify the security parameters that attackers could exploit to launch attacks. We will then use reverse engineering to examine and evaluate the security of a wide range of firmware being used in the latest and most commonly used IoT devices based on the identified security parameters. The same parameters can then derive security expectations for a secure IoT firmware. The proposed approach provides a powerful strategy to comprehensively analyze an IoT system's security. Our experimental results show that more than 50 percent of the firmware are exposing critical information that can be used to launch attacks. We believe that our findings will also help establish recommendations for developing secure and resilient firmware.
S-index: Significance of Academic Authors to Individual Publication Venues

Many author-level metrics have been proposed and employed to evaluate the publication performance of academic authors, such as citation count, h-index and other citation-based indices. These metrics are global measures which evaluate the authors’ productivity and impact based on all of their publications without differentiating the publication venues. However, from the perspective of an individual publication venue (journal or conference), it is also important to have a localised approach which can specifically indicate the significance of an author to the particular venue. Identifying these significant authors is valuable for the venues to hire appropriate journal editors, conference chairs and committee members. In this paper, we propose a venue-aware author evaluation metric, S-index, to measure the significance of authors to individual publication venues based on the term frequency – inverse document frequency (TF-IDF). This new metric reveals the differentiated values of the same author in different venues. To verify the effectiveness of S-index in identifying significant authors for venues, we carry out extensive experiments on three datasets of different research fields, and compare the S-index to five classic comparison metrics, including a localised version of h-index. Results demonstrate that the scoring behaviour of S-index is different from that of the other metrics, and it is more effective in identifying significant authors for publication venues.
Proposal: Mall Parking Application for Central Business District

Finding an empty parking slot in shopping mall is one of the common problems faced by people who living in urban area. People do always wish to park nearer to their destination. In the urban central business district (CBD), finding a parking slot in popular shopping malls are a task that is difficult, requires lots of time and energy and it mostly makes drivers to feel frustrated every time everywhere. It is troublesome as for drivers to manually searching for an empty parking slot around the parking area. With the help of the mobile application, the task can become easier for drivers as the process of finding, booking, and making payment for the parking slot can all be done within the application. Methodology entailed with interview approach with the target of 2 interviewers. The development will be kickstarted once the result of analysis is done to cater to user expectations.
Design of a Vision Blind Spot Detection System Based on Depth Camera

Nowadays, according to the report of world health organization, about 1.35 million people die in traffic accidents every year. Among them, because of it is difficult to predict in advance, the frequency of accidents caused by blind spots is relatively high, which means that it is necessary to design a system that can efficiently detect the target situations in which drivers in the area may have blind spots in their vision. Some existing methods, such as vehicle-mounted radar and pressure sensor are used for the blind spots' detection. However, they have some unresolved issues such as insufficient detection distance and limit excessive setting position. Therefore, to prevent misjudgment caused by insufficient information acquisition, this paper tries to present a multi-level object identification model based on face/object recognition and depth detection technologies via depth camera. Based on this, a new design of vision blind spot detection system is proposed by calculate the distance between human and vehicles via the depth camera. The proposed system has the function of determine whether vision blind spots exist and can quickly feedback the situation to drivers and pedestrians. This system will not only put forward a new vision blind spots determination method, but also can contribute to the research of public traffic accident prevention.
Proof-of-Concept of Polar Codes for Biometric Identification and Authentication

In this work, a complete biometrics identification and authentication system considered in [1] is implemented. In the considered system, polar codes are applied and binary symmetric memoryless channels are used for noisy enrollment and observation. The fundamental limits can be achieved with sufficiently long block length for iid binary source sequence. Fingerprints are used as the biometric source and an autoencoder is designed for pre-processing so that images are compressed to nearly uniformly distributed binary sequences with similar correlation and entropy properties to iid binary sequence. The identification and authentication system with generated secret key in [1] is implemented and simulated using pre-processed fingerprints as biometric source and polar code-based design. The proposed system design approach is systematic and flexible in choosing the optimal trade-off. The results show that identification error rates become smaller with longer code length and when the successive cancellation list algorithm is applied. Thus, it is shown by these first promising experiments that polar codes can be used in real identification and authentication systems.
Context-Aware Feature Selection using Denoising Auto-Encoder for Fault Detection in Cloud Environments

Machine learning is expected to play an instrumental role in automating the detection of faults in next-generation cloud networks. The existing machine-learning-based fault detection methods suffer from the following drawbacks: (i) ignoring the issue of missing feature values, (ii) ignoring the impact of each feature on output prediction over other features (measurement of feature importance), and (iii) lack of calculation of the proper number of features for fault detection. To address the above challenges, in this paper, we propose a context-aware feature selection method to improve the performance of fault detection methods in the cloud environment, aiming at maximizing the $F_{1}$-score. Our proposed solution comprises Denoising Auto-Encoder (DAE) stacked with a Discriminative Model (DM). The DAE is applied to handle the missing feature values and encoding features while the DM is responsible for making predictions of system status based on the encoded features. Then, the sensitivity analysis of output prediction with respect to each input feature value is used to measure the feature importance. We compare our work with existing solutions from the literature. Our results reveal that the proposed solution can improve the $F_{1}$-score up to 47 % and 76 % in the scenario where all feature values are known and in the scenario where only 25 % of feature values are known, respectively.
Smart University Development Challenges using Lora or Sigfox technology: A Systematic Literature Review

Efficiency and effectiveness are important keywords in regulating the governance of a work unit entity. In a specific scope, including management of resources in the university environment to provide efficient services. For this reason, the role of IoT technology is an appropriate alternative to be implemented on campus. To operate IoT technology, two communication technology, Low Power Wide Area Network (LPWAN) Technology, namely Lorawan and Sigfox, are examined in this study with a systematic literature study approach. Through this study approach, several relevant and successful studies have been carried out and will be evaluated based on parameters, mechanisms, test settings, and quality. The expected results of this evaluation can be the basis for the application of LPWAN technology for the development of systems that integrate IoT technology to become a smart campus.
Nano-Watt Voltage References for High Supply Voltages

This paper presents two voltage reference circuits that can operate in the supply voltage range of 7-17V and 9-15V while consuming less than 3.5 nA at 17V supply voltage and 8.47 nA at 15V supply voltage. The proposed voltage references consist of only MOSFETs, operated in the subthreshold region since low power consumption is required. The circuits have a mean reference voltage of 2.413V and 2.507V with the best temperature coefficient (TC) being 80 ppm/oC and 24 ppm/oC, respectively. The proposed voltage references were designed in XFAB XH018 180nm technology occupying an area of 0.079 mm2 and 0.038 mm2, respectively.
UVM Discard: Eliminating Redundant Memory Transfers for Accelerators

An increasing number of applications benefit from heterogeneous hardware accelerators. Such accelerators often require the application to manually manage memory buffers on devices and transfer data between host and device buffers. A programming model that unifies the virtual address space across the host and devices is appealing because it enables automatic memory transfers and simplifies application-level programming. However, the automatic memory transfers can sometimes be redundant, which decreases performance. NVIDIA’s UVM (unified virtual memory) driver provides a unified virtual address space for CPU-GPU programming. This paper identifies redundant memory transfers (RMTs) as a common performance issue with UVM. To address this issue, this paper proposes a data discard directive, and evaluates two implementations of that directive, UvmDiscard and UvmDiscardLazy. This directive exploits application-level knowledge to avoid RMTs. The implementations were integrated with NVIDIA’s open-source UVM driver to demonstrate their usefulness on real-world CUDA UVM applications. For example, the use of the discard directive increases training throughput by 61.2% on a large deep learning application that oversubscribes GPU memory.
Robust Passivity-Based Dynamical Systems for Compliant Motion Adaptation

Motivated by human compliant behaviors during interacting with unknown environments and how motions and impedance to are adapted skilfully complete a task, this article develops a motion planning scheme that is capable of generating a compliant trajectory online such that tracking desired contacting forces under a predefined motion task. First, an improved dynamical system (DS) is designed to generate an adaptive compliant scanning trajectory online from the original DS in terms of the contact forces and the desired scanning forces. Inspired by passivity analysis for the robot control system, a robust term is formulated to guarantee stability by considering the balance between environmental and robotic energy. Furthermore, we develop a state-constrained controller based on barrier Lyapunov function to track the compliant DS motion and to ensure safety during scanning for the patient. Finally, comparative simulations are conducted to validate the general compliant capability of the proposed framework. We also instantiate our methodology through a use case of liver ultrasound scanning to demonstrate the stable and dynamic force tracking performance.
A Pose Detection based Continuous Authentication System Design via Gait Feature Analysis

In recent years, with the increasing demand for public safety and the impact of pneumonia (Corona Virus Disease 2019, COVID-19), long-distance, contactless authentication has become a hot topic. Gait recognition technology has broad application prospect in computer vision field because of its ability of long-distance gait recognition and identification verification. On the other hand, with the development of big data, cloud computing, 5G, IoT and other technologies, which makes the Continuous authentication based on cameras is already possible. Therefore, we propose a continuous authentication system based on human pose estimation framework by analyzing and extracting gait characteristics. This system not only has the advantages of easy acquisition, long distance, contactless, and hard to disguise gait recognition, but also has the functions of dynamic authorization and continuous authentication, This method will bring a new development direction for the research of human pose estimation and gait recognition and other related fields.
TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites

Several computational methods have been developed to perform promoter classification in prokaryotic genomes. However, those methods were mostly designed to classify promoters only under the condition that the TSS must be located at a predefined anchor position in each input sequence. Hence, this study aims at developing a new method that can classify promoters without assuming a fixed location of TSS. We draw an analogy between TSS identification and object detection, which is a well-known task in image analysis. Thus, TSSNet, a deep neural network model, is developed in this study and it can scan for the TSSs by taking windowed regions in a prokaryotic genome. The benchmark reveals that TSSNet has a recall rate higher than 80% in TSS identification when analyzing the genomic sequences. Our results suggest that an object detection model can be applied to the analysis of genomic sequences for finding the regulatory elements and key points that are important in functional genomics.
Genetic profiling of olives for location of origin and variety discrimination using Machine Learning

Genetic profiling via biomarkers in the food industry is a technology that gains momentum in the context of quality assurance and protection against fraud, as well as securing commercial assets like designation of origin. However, current solutions are based on methods that require significant computational resources and management of large data volumes, making them unsuitable for applications in the context of Internet-of-Things (IoT), edge computing and microcontrollers (MCU). This study presents a novel, computationally efficient and robust approach for fully field-integrated, low-complexity and high-accuracy classification of olives variety and location of origin, based on genetic ‘fingerprinting’ via a minimal set of information-rich features. The method is tested with real-world datasets, achieving accuracy rates above 96% and 99%, respectively, using various instance-based and tree ensemble classification models.
The absolute solar soft X ray flux in the 20‐ to 100‐Å region

In the present work we have used the best available solar spectral data together with our absolute neon ionization cell data to estimate the absolute solar soft X ray flux and its variation with solar activity in the 20- to 100-A region. Since the neon chamber data cover the integral spectral region from 20 to 575 A we have constructed a composite spectrum covering that region so that the fraction of solar flux in the 20- to 100-A region could be determined. Both sounding rocket and satellite observations obtained over the past two decades have been used in the present analysis. Most of these prior data were acquired during low and moderate solar activity levels in contrast with the neon ionization cell data which were obtained during moderate and maximum solar activity levels. Based on these combined data sets we have shown the dependence of the integrated solar soft X ray flux between 20 and 100 A on the daily 10.7 cm radio flux observed at 1 AU and have obtained an estimate of the maximum flux that can be expected over the solar cycle.
On the solutions of one-class-one-outputed feedforward neural network classifiers using backpropagation training

This paper studies the solutions of the outer-supervised batch-style learning based one-class-one-outputed (OCOO) feedforward neural network (FNN) classifiers. It is proven that if the condition of the hidden neuron number is greater than the total training sample number, which is sufficient but not necessary, is satisfied, the network would uniquely converge to the global minima with null costs, and that the condition of the range space of the outer-supervised signal matrix being included in the range space of the hidden output matrix is a sufficient and necessary condition of ensuring local free minima. Furthermore, in the case of the hidden neuron number being smaller than the total training sample number and greater than the output neuron number, it is shown that the global minimum solutions with null costs may also be derived by adequately adjusting the first layer weights. In the case of the hidden neuron number being smaller than the output neuron number, however, it is shown that there would not exist any global minima with null costs in the error surface.