Miracle-dz/UniFAR-FTUs huggingface dataset:
An I-D Two-State Dynamic Receive Window Adjustment Scheme for Solving Bufferbloat Problems

Bufferbloat is a phenomenon caused by excessive buffering of packets that results in high latency, large jitter, as well as throughput degradation. It happens when a loss-based Transmission Control Protocol (TCP), such as New Reno or Cubic, is used for data transfer. A dynamic receive window adjustment (DRWA) scheme has recently been proposed to solve the bufferbloat problem. It was reported that, with the DRWA scheme, the bufferbloat problem can be effectively solved. However, we found that when there are multiple TCP connections sharing some bottleneck link, the ones with DRWA tend to lose their bandwidth share to those without DRWA. Moreover, when a user equipment establishes multiple TCP connections all implemented with DRWA and all connections pass through the same bottleneck link, the connection which is established last will dominate the bandwidth. In this paper, we present a simple scheme, called I-D (increasing-decreasing) two-state dynamic receive window adjustment, which according to simulation results can effectively solve the bufferbloat problem without the unfair bandwidth-dominating issues.
Practical Test for Closed-Loop Identification and Control on Magnetic Levitation System: A Fractional-Order Approach

Magnetic levitation system is an interesting equipment to demonstrate an intricate control design problem. Because of its non-linearity and exhibit uncertain open-loop properties together critically unstable in nature, identifying such system and then designing an effective controller is very demanding. This paper proposes identification and control strategy for real-time magnetic levitation system based on fractional theory and an orthogonal series. Without removing the controller from the loop, the unknown system is identified in form of low-order fractional model accurately. Then, a new fractional controller namely FOPID (fractional-order-proportional-integral-derivative) is tuned to improve the system performance. Real-time experiment study clearly illustrates the effectiveness of the presented tuning method.
Interpretable Quantitative Description of the Digital Clock Drawing Test for Parkinson's Disease Modelling

Modelling of the fine motor motions during digital clock drawing test is performed within frameworks of the present studies to facilitate computer-aided diagnostics of the Parkinsons disease. Clock drawing test has been used to diagnose and monitor neurodegenerative diseases for a long period of time. It was one of the first tests to be digitalized. Nevertheless, its natural complexity causes many problems for the analysis of test results. Unlike simpler tests clock test drawing consists of many different elements. Therefore, it requires one to identify all the elements of the drawing and then proceed with the analysis of different elements. The presence of different elements, in turn, leads the idea to model test results on different levels. Low-level where modelling is performed on the basis of kinematic, pressure and temporal parameters describing fine motor movements. And higher level, where relative positions of elements, drawing quality and presence of the different elements are analyzed. Low-level analysis constitutes the scope of the present paper. Based on the clock drawing test results obtained for the groups of patients with diagnosed Parkinsons disease and similar, by age and size, healthy individuals. Features describing fine motor motions are constructed, whereas special attention is paid to the so-called set of motion mass parameters. Then features possessing the highest discriminative power to distinguish between Parkinsons disease patients and healthy control individuals are selected. Finally based on the selected subset of features applicability of different machine learning algorithms to support diagnostics process is evaluated.
Optimal Scheduling of Multi-Carrier Energy Networks Considering Liquid Air Energy Storage

Energy storage technologies are crucial to the future integrated energy system (IES) in the context of energy internet (EI). Liquid air energy storage (LAES) technology has been widely researched and implemented in the United Kingdom, since there are no special restrictions on geography compared to other technologies. However, there is little attention on LAES in China. In this paper, a linear programming model for the optimal scheduling of multicarrier energy networks considering LAES is established based on the concept of cryogenic energy hub, that is LAES-HUB. The results show that the daily operation cost of LAES-HUB model is 43735.28 RMB, which is even higher than the energy networks works without LEAS. LAES can achieve load shifting through peak and off-peak operations, providing an auxiliary function for energy balance in multi-carrier energy networks.
A Survey of Random Access Control Techniques for Machine-to-Machine Communications in LTE/LTE-A Networks

Machine-to-machine (M2M) communications refer to the autonomous interaction between connected devices without the human intervention. Recently, the Third-Generation Partnership Project (3GPP) introduced the Long-Term Evolution (LTE) and Long-Term Evolution-Advanced (LTE-A) as the improved version of the fourth-generation (4G) cellular networks. LTE/LTE-A networks have been considered an appropriate infrastructure for implementing M2M communications. However, the LTE/LTE-A networks were originally introduced for human-to-human (H2H) communications which have different characteristics from M2M communications. Thus, the LTE/LTE-A networks must be adapted to support the special characteristics of M2M communications. This work provides a comprehensive review of M2M communications over LTE networks, including M2M architectures, LTE structure, deployment challenges, and access control requirements. Moreover, this work introduces a novel classification for the current Random Access (RA) techniques that have been proposed for M2M communications in LTE networks. According to the main targeted objective, the current RA techniques are classified into three categories: massive access control techniques, energy efficiency techniques, and performance improvement techniques. Each category is further divided into two subcategories, and the relevant RA techniques are presented for each category. Furthermore, an analytical comparison has been provided among the different techniques according to the parameters for each approach. This work can be considered a good starting point for those who are interested in RA techniques for M2M communications over LTE/LTE-A networks.
An Approach for Reliable End-to-End Autonomous Driving Based on the Simplex Architecture

Over the past decade, autonomous driving has been a subject of continued interest for research. In general, conventional approaches for autonomous driving consists of roughly two parts: perception and motion planning. Recently, an alternative approach based on the deep neural network has been developed, called the end-to-end autonomous driving, that maps raw sensor data directly to driving command without requiring a separate perception process. However, the performance of the end-to-end driving highly depends on the quantity and quality of the datasets used in the learning process and can become unreliable if untrained situation is encountered. To overcome this fundamental drawback of the end-to-end approach, we adopt the simplex architecture for autonomous driving as a mean that combines the end-to-end approach together with the conventional approach to improve the overall driving reliability. The improved driving reliability of the proposed autonomous driving framework is shown through experimentation on a testbed system built on this work.
Understanding Learning Resources Metadata for Primary and Secondary Education

Educational resources can be easily found on the Web. Most search engines base their algorithms on a resource's text or popularity, requiring teachers to navigate the results until they find an appropriate resource. This makes searching for resources a tedious and cumbersome task. Specialized repositories contain resources that are annotated with metadata that aim to facilitate the discovery of quality resources. Nevertheless, the abundance and variety of content make searching a complex task. Recommender systems can assist teachers in finding the proper content by determining clusters of similar users and inferring users interest in a resource, assembling clusters of similar resources, or a mix of both. Probabilistic model-based (PMB) techniques, on the other hand, make it possible to classify resources into more than one cluster with various degrees of probability. In this paper, we used recommender systems and PMB techniques to analyze a dataset produced by primary and secondary level teachers over the course of four years and under natural conditions. We found that a hybrid recommendation, the Collaborative Topic Regression (CTR) technique, performs better than other approaches despite the high sparseness of the dataset. In addition, learning resources annotated with curriculum metadata had a positive impact on recommender's accuracy whereas free-text or other metadata negatively impacted the results.
A 280  $\mu$ W Dynamic Zoom ADC With 120 dB DR and 118 dB SNDR in 1 kHz BW

This paper presents a dynamic zoom analog-to-digital converter for use in low-bandwidth (<1 kHz) instrumentation applications. It employs a high-speed asynchronous successive approximation register (SAR) ADC that dynamically updates the references of a fully differential <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma$ </tex-math></inline-formula> ADC. Compared to previous zoom ADCs, faster reference updates relax the loop filter requirements, thus allowing the adoption of energy-efficient amplifiers. Fabricated in a 0.16-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, the prototype occupies 0.26 mm<sup>2</sup> and achieves 119.1-dB peak signal-to-noise ratio (SNR), 118.1-dB peak signal-to-noise-and-distortion-ratio (SNDR), and 120.3-dB dynamic range (DR) in a 1-kHz bandwidth while consuming 280 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>. This results in a Schreier figure of merit (FoM) of 185.8 dB.
Powered Two-Wheeled Vehicles Steering Behavior Study: Vision-Based Approach

This paper presents a vision-based approach to prevent dangerous steering situations when riding a motorcycle in turns. The proposed algorithm is capable of detecting under, neutral or over-steering behavior using only a conventional camera and an inertial measurement unit. The inverse perspective mapping technique is used to reconstruct a bird-eye-view of the road image. Then, filters are applied to keep only the road markers which are, afterwards, approximated with the well-known clothoid model. This allows the prediction of the road geometry such as the curvature ahead of the motorcycle. Finally, from the predicted road curvature, the measurements of the Euler angles and the vehicle speed, the proposed algorithm is able to characterize the steering behavior. To that end, we propose to estimate the steering ratio and we introduce new pertinent indicators such as the vehicle relative position dynamics to the road. The method is validated using the advanced simulator BikeSim during a steady turn.
A High Speed Programmable Ring Oscillator Using InGaZnO Thin-Film Transistors

This paper presents a high speed digitally programmable Ring Oscillator (RO) using Indium-gallium-zinc oxide thin-film transistors (IGZO TFTs). Proposed circuit ensures high speed compared to the conventional ROs using negative skewed scheme, in which each inverter delay is reduced by pre-maturely switching on/off the transistors. In addition, by controlling the load capacitance of each inverter through digital control bits, a programmable frequency of oscillation was attained. Proposed RO performance is compared with two conventional designs under same conditions. From simulation, it has been observed that the proposed circuit has shown a higher frequency of oscillations (283 KHz) compared to the conventional designs (76.52 KHz and 144.9 KHz) under same conditions. Due to the programmable feature, the circuit is able to generate 8 different linearly spaced frequencies ranging from 241.2 KHz to 283 KHz depending upon three digital control bits with almost rail-to-rail voltage swing. The circuit is a potential on-chip clock generator in many real-world flexible systems, such as, smart packaging, wearable devices, RFIDs and displays that need multi frequencies.
The Prehistoric and early iron age pottery and the lithic artifacts

Introduction Curtis Runnels, Daniel J. Pullen, and Susan Langdon 1. The pottery of the Neolithic, Early Helladic I, and Early Helladic II periods Daniel J. Pullen 2. The pottery of the Early Helladic III and Middle Helladic periods Gullog C. Nordquist 3. The pottery of the Late Helladic period P. A. Mountjoy 4. The pottery of the early Iron Age and Geometric periods Susan Langdon 5. The Lithic artifacts: flaked stone and other nonflaked Lithics P. Nick Kardulias, and Curtis Runnels Conclusions Curtis Runnels, Daniel J. Pullen, and Susan Langdon.
Revolving Permanent Magnet for Magnetomotive Ultrasound

In magnetomotive ultrasound (MMUS), solenoids have been extensively used to generate varying magnetic fields, that act to move nanoparticles and their surrounding tissue. Here we show, compare and contrast two magnetic devices, one based on a solenoid, another containing a rotating permanent magnet. Suitable field characteristics for this application include high flux density and gradient, and uniform field laterally in the image plane, and the corresponding design features are highlighted. Field measurements show that both magnets generate suitable fields. The magnetic force potentially exerted by the permanent magnet even exceeds that of the solenoid up to a depth of 4 mm. Considering the substantial heat generation in high inductance electromagnets, a permanent magnet would be preferable as it eliminates the need for cooling. Thus, we suggest the use of permanent magnets in MMUS devices to reduce the size, while maintaining a high field and gradient required to move nanoparticles.
Deliberation of Curvature type Obstacles: A New Approach using FPGA based Robot

The Robot navigation system ushering with autonomous as part of Industry revolution 4.0. In this view, An Obstacle avoidance mechanism is integral of navigation algorithms. The proposed method venerates in analyzing the curvature type obstacles and strives to implement for the real time avoidance mechanism. This method is amenable in learning curve type obstacle and computing its size efficiently and same is implemented with hardware efficient scheme using FPGA. The different deliberations are groomed in solving obstacle avoidance. In this regard, the proposed method using FPGA based robot achieves the full fledged imperative results in avoidance mechanism. It is validated using Xilinx Vivado 17.2 and deployed architectures on Zybo board (xc7Z010clg400 -1).
Application of MatLab for Solid Surface Analysis by Means of X-Ray Photoelectron Spectroscopy

Efficiency of using the MatLab package to solve the problems of analysis of solid by means of X-ray photoelectron spectroscopy. A high-level programming language, fast matrix libraries MatLab, Signal Processing Toolbox and Curve Fitting Toolbox enabled to use more complicated, physically grounded calculation models for chemical and phase depth profiling of solid and calculate thicknesses of sub-surface layers with a submonolayer accuracy. The approach is used in educational and scientific students' research.
Short-Term Traffic Flow Forecasting by Selecting Appropriate Predictions Based on Pattern Matching

Forecasting short-term traffic flow is one critical component in traffic management to improve operational efficiency. Data driven method, which trains the predictor with historical data across a given past period, have been proved to perform well. However, days which experience significantly different traffic flow patterns, negatively influence forecasting results. This paper proposes an advanced method, making use of appropriate prediction based on pattern matching. First, historical data is divided into several groups, according to their patterns, by clustering algorithms. Then the predictor is trained for each group based on a convolutional neural networks and long-short-term-memory model. For each time point, the degree of similarity between the target day and each group is measured, and the predictor trained by the group possessing the highest degree of similarity is selected to be appropriate. Based on a case study from Seattle, we show that selecting an appropriate predictor can significantly improve the accuracy of predictions. In addition, we demonstrate that the new method can, in general, outperform alternative methods in terms of prediction accuracy and stability.
Hierarchical Optimal Operation for Integrated Energy System Based on Energy Hub

The integrated energy system(IES) has the characteristic of energy integrated/multi-energy coupling which involves heat, electricity, natural gas, and other various energy forms, which can realize maximize the synergistic effects and complementary benefits among various energy and the comprehensive utilization. In this paper, the day-ahead hierarchical optimal operation for IES coupling natural gas system, electricity transmission system and district heating system based on energy hub(EH) is studied. Firstly, the model architecture of EH with diversified storage devices, renewable energy and different energy conversion equipment is proposed and the steady state mathematical model of different energy networks in IES is developed, respectively. Secondly, the day-ahead operating cost of EH is minimized by optimizing strategy to maximize the benefits of all kinds of energy demand users, where different types of energy power input into EH can be obtained. Then, the day-ahead optimal operation mode for IES considering minimization of operating fuel cost index is proposed via energy management system giving various energy power data which are uploaded from EH. The numerical case verifies the validity of the day-ahead hierarchical optimal operation and steady state calculation analysis for IES.
SA-VFI: the IEEE IUS Challenge on Synthetic Aperture Vector Flow Imaging

High frame rate vector flow imaging represents an attractive alternative to implement velocity estimation modes on clinical systems. Several approaches to obtain such imaging modes are possible especially using plane or diverging waves. The SA-VFI (Synthetic Aperture-Vector Flow Imaging) challenge is a competition organized during the IEEE International Ultrasonics Symposium 2018 in Kobe that aimed at bringing together all groups willing to work on synthetic aperture based high frame rate vector flow imaging This document describes the framework and objectives of the challenge as well as the data-sets and metrics used in the competition. The paper concludes with a discussion of the experiences from organizing this challenge.
Development of an Ultrasound and Photoacoustic Endoscopy System for Imaging of Gynecological Disorders

Visualization and detection of early-stage gynecological cancers is currently a challenging endeavor due to the difficulty in accessing to these tissues especially through the cervical canal. In this work, a miniaturized phased-array US and photo acoustic (P A) endoscopic probe is introduced, which is capable of augmenting the morphological data of US transducers with the functional and molecular information of P A imaging for the diagnosis of gynecologic diseases. The fabricated probe is 7.5 mm in width allowing for intimate contact with pathological tissues and provides the user with realtime information regarding tissue morphology as well as function, as determined by molecular absorption spectra. The multi-modal image generated by this probe is expected to aid clinicians in diagnosing early stage gynecological cancers, such as cervical, ovarian and uterine cancers.
Compressed Randomized UTV Decompositions for Low-Rank Matrix Approximations

Low-rank matrix approximations play a fundamental role in numerical linear algebra and signal processing applications. This paper introduces a novel rank-revealing matrix decomposition algorithm termed compressed randomized UTV (CoR-UTV) decomposition along with a CoR-UTV variant aided by the power method technique. CoR-UTV is primarily developed to compute an approximation to a low-rank input matrix by making use of random sampling schemes. Given a large and dense matrix of size <inline-formula><tex-math notation="LaTeX">$m\times n$</tex-math></inline-formula> with numerical rank <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$k \ll \text{min} \lbrace m,n\rbrace$</tex-math></inline-formula>, CoR-UTV requires a few passes over the data, and runs in <inline-formula><tex-math notation="LaTeX">$O(mnk)$</tex-math></inline-formula> floating-point operations. Furthermore, CoR-UTV can exploit modern computational platforms and, consequently, can be optimized for maximum efficiency. CoR-UTV is simple and accurate, and outperforms reported alternative methods in terms of efficiency and accuracy. Simulations with synthetic data as well as real data in image reconstruction and robust principal component analysis applications support our claims.
Inverse of Affine Radon Transform for Light Field Reconstruction From Focal Stack

The light field can be applied to computational imaging methods, such as digital refocusing, depth reconstruction, and all-in-focus imaging. In this paper, the affine Radon transform of generating the focal stack by the light field is proposed. Then, we derive the inverse formula of the affine Radon transform for reconstructing the light field from the focal stack. We analyze the ill-posedness of the reconstruction problem by the inversion formula and the incompleteness of the focal stack data. The inversion formula reveals the instability of the solution. The focal stack can be regarded as the incomplete data for light field reconstruction in the spatial domain, while it corresponds to the limited support of light field in the Fourier domain. The numerical solution of light field reconstruction is realized by approximating the inverse of the affine Radon transform. Based on the approximated inverse affine Radon transform of light field reconstruction, the high-precision light field data reconstruction method and the computational imaging method can be established via the focal stack data. The experimental results show that the high-precision light field can be reconstructed from focal stack based on the approximated inverse affine Radon transform.
Wind Power Load Forecasting Based on Incremental BP Neural Network

Wind power load forecasting is an important part of energy internet, which is the basis of rational arrange the power grid operation plan. As a kind of time series data, wind power load data has the characteristics of time series and large quantity. To overcome the deficiency that the latest information of time series has great influence on the accuracy of wind power load forecasting, this paper proposes a method of adding and deleting hidden nodes of BP neural network to realize the incremental learning of BP neural network training. By dynamically adding and deleting the number of hidden layer neurons and training the latest time series information in BP neural network, this method can improve the effect of network training and avoid the convergence of traditional BP neural network to local minimum. By comparing the wind power load forecasting results of batch training BP neural network and incremental BP neural network with real wind power load, the results show that the wind power load forecasting accuracy based on incremental BP neural network is higher.