Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio

Every year many people lose their lives due to fatal road accidents around the world and drowsy driving is one of the primary causes of road accidents and death. Fatigue and micro sleep at the driving controls are often the root cause of serious accidents. However, initial signs of fatigue can be detected before a critical situation arises and therefore, detection of driver’s fatigue and its indication is ongoing research topic. Most of the traditional methods to detect drowsiness are based on behavioural aspects while some are intrusive and may distract drivers, while some require expensive sensors. Therefore, in this paper, a light-weight, real time driver’s drowsiness detection system is developed and implemented on Android application. The system records the videos and detects driver’s face in every frame by employing image processing techniques. The system is capable of detecting facial landmarks, computes Eye Aspect Ratio (EAR) and Eye Closure Ratio (ECR) to detect driver’s drowsiness based on adaptive thresholding. Machine learning algorithms have been employed to test the efficacy of the proposed approach. Empirical results demonstrate that the proposed model is able to achieve accuracy of 84% using random forest classifier.
Development of a Novel Data Acquisition Technique and a Software Tool for Quantitative Diffusion Imaging

As a specialized Magnetic Resonance Imaging (MRI) technique, diffusion imaging is sensitive to diffusion process of Nuclear Magnetic Resonance (NMR)-active nuclei in biological tissues. Studying the diffusion process of water molecules can reveal structural and environmental differences in normal versus diseased tissues at a microscopic (e.g. cellular) level. Diffusion-weighted magnetic resonance images (DWI) can be analyzed to obtain parameters such as Apparent Diffusion Coefficient (ADC), Fractional Anisotropy (FA), Radial Diffusivity (RD), Axial Diffusivity (AD), and others to be used as imaging markers in disease diagnosis and treatment assessment. To accurately obtain and reliably use these parameters, high spatial resolution with an adequate image quality must be achieved. Currently, the standard techniques for acquisition of diffusion images compromise on either image resolution or acquisition efficiency. The goal of this project is to develop a novel image acquisition technique for producing high-resolution and high-quality DWI within clinically acceptable scan duration (e.g. less than 3 minutes), and demonstrate its impact on the development of imaging markers for clinical applications. In order to accomplish this goal, three specific aims were defined. In the first, we designed and implemented a new gradient and spin-echo (GRASE) based PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) pulse sequence, which we call “Steer-PROP”. Experiments conducted for validating this sequence has proven that it is capable of producing high-resolution T2 and DWI in less than 2.5 minutes. Second, we have identified the different kinds of phase errors that adversely impacted the image quality, and designed the corresponding correction methods to remove these phase errors during image reconstruction. Such phase correction has considerably improved the quality of diffusion-weighted images from the steer-PROP pulse sequence. Lastly, we have developed a software package, which we call Diffusion Imaging Visualization Environment (DIVE), for facilitating the calculation of a set of diffusion parameters that can be used to address clinical problems. The software provides a comprehensive diffusion image processing toolbox, capable of producing conventional parameters such as ADC and FA as well as a novel white matter characterization metric, which we call regional Fiber Coherence Index (r-FCI). The clinical significance of these parameters have been demonstrated through a diffusion tensor imaging study, designed to show the application of r-FCI and FA as markers for tumor cell infiltration along white matter fiber tracts. We have successfully completed this project and accomplished the goals defined in the three specific aims. We now have the necessary technical capabilities to produce high-resolution diffusion images that can be used to develop targeted imaging markers for specific clinical applications.
1# Expressive: A language for text register programs.

Inspired in Lawrence Moss’s 1# language for text register programs, this work documents the theoretical background, design, implementation and usage of 1# Expressive (1#X), a language that provides a friendly syntax and compiles to 1#, facilitating the writing and understanding of 1# programs and hence aiding in the study of theory of recursion and self replication through text register machines. In the process, we explain concepts about text register machines, domain specific languages, and their development using the Xtext framework.
Model-Based Coordinated Control of Four-Wheel Independently Driven Skid Steer Mobile Robot with Wheel–Ground Interaction and Wheel Dynamics

Four-wheel independently driven mobile robots are widely used in industrial automation, intelligent inspection, and outdoor exploration. The traditional kinematic control is usually applied for them, where only the chassis kinematics is taken into account and the robot dynamics (especially the wheel dynamics) is normally ignored. It may lead to some performance limitations such as the chattering phenomenon during robot rotating, because of the overactuation characteristic by four driving wheels. To address these problems, the integrated dynamic model is proposed, which includes chassis kinematics, chassis dynamics, wheel–ground interaction, and wheel dynamics. Subsequently, different from kinematic control, a model-based coordinated adaptive robust controller is developed, which generally consists of three-level designs for different parts of robot dynamics, and directly generates the motor driving torque commands for four wheels. The stability and tracking performance are theoretically guaranteed. Comparative experiments are carried out, and the results show the better performance of our proposed scheme.
Integration and Evaluation of QUIC and TCP-BBR in longhaul Science Data Transfers

Two recent and promising additions to the internet protocols are TCP-BBR and QUIC. BBR defines a congestion policy that promises a better control in TCP bottlenecks on long haul transfers and can also be used in the QUIC protocol. TCP-BBR is implemented in the Linux kernels above 4.9. It has been shown, however, to demand careful fine tuning in the interaction, for example, with the Linux Fair Queue. QUIC, on the other hand, replaces HTTP and TLS with a protocol on the top of UDP and thin layer to serve HTTP. It has been reported to account today for 7% of Google’s traffic. It has not been used in server-to-server transfers even if its creators see that as a real possibility.Our work evaluates the applicability and tuning of TCP-BBR and QUIC for data science transfers. We describe the deployment and performance evaluation of TCP-BBR and comparison with CUBIC and H-TCP in transfers through the TEIN link to Singaren (Singapore). Also described is the deployment and initial evaluation of a QUIC server.We argue that QUIC might be a perfect match in security and connectivity to base services that are today performed by the Xroot redirectors.
A Novel Approach for Reducing Energy Consumption in Routing Protocol for Clustered Wireless Networks

: Due to the vast potential of the sensor networks to enable applications that connect the physical world to the virtual world, the Efficient design and implementation of wireless sensor networks has become a hot area for researchers in recent years. Energy efficiency is a key issue in WSN as the sensor nodes are equipped with limited resources. Clustering based protocols have been suggested in reducing the energy consumption while packet forwarding to the base station. In this research paper, a novel technique to reduce energy consumption has been proposed. The algorithm has been implemented using MATLAB and compared to existing LEACH protocol. The results obtained in terms of number of dead nodes, number of alive nodes and throughput suggest that the proposed technique achieves better results than the LEACH protocol.
Two Area Load Frequency Control Using DE Tuned PID Controller with Nonlinearity

A coordinated operation of two or more than two power area is essential today for fulfilling the end consumers load demand for which this kind of structure in power system is termed as interconnected area. Their proper and successful operation requires minimal changes in the frequency and tie line power flow among their tie lines. Hence, keeping the frequency and tie line power flow close to constant is of key significance here. Normally, for a single area system load frequency control (LFC) is quite simple, but for such an interconnected area it is quite a task. Like in single area system too, the LFC in case of two area interconnected system can be bettered with the inclusion of controller. Therefore, PID controller is used in the presented work in LFC loop. Its performance could further be enhanced by the use of some optimization technique for which Differential Evolution (DE) algorithm has been put to use and it will help to reduce the time domain objective of the work. In the process of analysis the generation rate constraint (GRC) is also considered to account for the non linearity present in the interconnected system going closer to real practical scenarios. For the LFC PID controller is used here with the optimization provided by DE (Differential Evolution) and BOFA (Bacteria Foraging Optimization). Later both these techniques were compared with each other and with conventional controller and with Genetic algorithm (GA) based controller. The simulation of the two area non reheat thermal interconnected system is carried out in MATLAB/SIMULINK under different cases. The cases falls under two category of saturation limit one with α=±0.05 and other with α=±0.025. The case are, 5% step change in load of both the areas, change in the system parameters by 50% (either increasing it or decreasing the particular parameter) and observing the settling time for deviation in frequency and tie line flow for both the areas. To evaluate the usefulness of the proposed method, we compare the answer of this method Differential Evolution (DE) with the Bacteria Foraging Optimization (BFOA) method of the PID controller technique for the same composite system. Investigations show that the proposed DE algorithm is superior to the BFOA technique. Simulation results show that the differential evolution-based tuning of the PID controller performs better than the BFOA optimization-based PID controller.
STATISTICAL ANALYSIS OF AIRBORNE IMAGERY COMBINED WITH GIS INFORMATION FOR TRAINING DATA GENERATION

Abstract. In recent years, the task of land cover classification from airborne image and elevation data advanced considerably due to enhanced applicability of CNNs (Convolutional Neural Networks). Nevertheless, CNNs require a huge amount of training data. Traditionally, few essential feature values, such as elevation or vegetation index, had been chosen to provide a coarse distinction of classes, but very often these values have to be adapted depending on the imagery. To improve this process, freely available GIS data are combined with spectral and spatial features (and their variations) following the K-Means and Mean-Shift algorithm. Based on cluster assignments to pixels, statistical analysis for extracting plausible values for distinguishing between land cover classes is applied. The resulting labeled databases are evaluated using ground truth data, and will form the basis for the training data required for CNNs.
Clinical Predictors of the Evolving IschemicStroke According to the Tree-Structured Model

Methods and Materials: The objects of the study were 1421 patients with ischemic stroke (IS), hospitalized within 48 hours from the development of the initial symptoms. Patients with IS were admitted to the 5th Minsk City Clinical Hospital and the Minsk Emergency Hospital (Belarus) in 2002-2014 years. Evolving clinical course of the stroke is defined as an increase in the severity of neurological deficit by 2 or more points on the NIHSS scale or the death of the patient during the first seven days of hospitalization. The research is characterized due to the prospective-data-collection, and the retrospective evaluation design. The statistical method of decision trees and an algorithm of the conditional inference trees were used to create the prognostic model of EIS. Statistical data analysis was carried out applying the software packages of R V.3. 2.5 and IBM SPSS Statistics 26.0.
Comparison Between TDMA and Random Multiple-Access Energy-Harvesting Networks Under Security Constraints

We study the maximum average energy transfer rate at a wireless charging energy-harvesting receiver when multiple transmitting nodes communicate their confidential information to a data receiver. We investigate both the time-division multiple-access (TDMA) and the random multiple-access (RMA) schemes and compare their performance. We address the question of which multiple-access scheme can maximize the average energy transfer rate under secrecy constraints. We also investigate the cases of the RMA scheme with equal access probabilities and the TDMA scheme with equal time-slot assignment probabilities. mymargin
Inter-Client Interference Cancellation for Full-Duplex Networks With Half-Duplex Clients

Recent studies have experimentally shown the gains of full-duplex radios. However, due to its relatively higher cost and complexity, we can envision a more practical step in the network evolution is to have a full-duplex access point (AP) but keep the clients half-duplex. Unfortunately, the full-duplex gains can hardly be extracted in practice as the uplink transmission from a half-duplex client introduces <italic>inter-client interference</italic> to another downlink client. To address this issue, we present the design and implementation of <monospace>IC2</monospace> (Inter-Client Interference Cancellation), the first physical layer solution that exploits the AP’s full-duplex capability to <italic>actively</italic> cancel the interference at the downlink client. Such active cancellation not only improves the achievable capacity, but also better tolerates imperfect user pairing, simplifying the MAC design as a result. We build a prototype of <monospace>IC2</monospace> on USRP-N200 and evaluate its performance via both testbed experiments and large-scale trace-driven simulations. The results show that, without <monospace>IC2</monospace>, about 60% of client pairs produce no gain from full-duplex transmissions, while, with <monospace>IC2</monospace> the median gain of the achievable rate over conventional half-duplex networks can be <inline-formula> <tex-math notation="LaTeX">$1.65\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.47\times $ </tex-math></inline-formula> for 1- and 2-antenna scenarios, respectively, even when clients are simply paired randomly.
Incremental Graph Pattern Based Node Matching with Multiple Updates

<italic>Graph Pattern based Node Matching</italic> (GPNM) has been proposed to find all the matches of the nodes in a data graph <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq1-2942294.gif"/></alternatives></inline-formula> based on a given pattern graph <inline-formula><tex-math notation="LaTeX">$G_P$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>P</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq2-2942294.gif"/></alternatives></inline-formula>. GPNM has been increasingly adopted in many applications such as group finding and expert recommendation, in which data graphs are frequently updated over time. Moreover, many typical pattern graphs frequently and repeatedly appear in users’ queries in a short period of time, e.g., social graph searches on Facebook. To deliver a GPNM result in such applications, the existing GPNM methods have to perform an incremental GPNM procedure for each of the updates in the data graph, which is computationally expensive. To address this problem, in this paper, we first analyze the elimination relationships between multiple updates in <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq3-2942294.gif"/></alternatives></inline-formula> and the hierarchical structure between these elimination relationships. Then, we generate an Elimination Hierarchy Tree (EH-Tree) to index the elimination relationships and propose an EH-Tree based GPNM method, called EH-GPNM, considering the elimination relationships between multiple updates in <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq4-2942294.gif"/></alternatives></inline-formula>. EH-GPNM first delivers the GPNM result of an initial query, and then delivers the GPNM result of a subsequent query, based on the initial GPNM result and the multiple updates of <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math><alternatives><mml:math><mml:msub><mml:mi>G</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="sun-ieq5-2942294.gif"/></alternatives></inline-formula> that occur between those two queries. The experimental results on five real-world social graphs demonstrate that our proposed EH-GPNM is much more efficient than the state-of-the-art GPNM methods.
Characterizing undersized damage using nonlinear features of guided ultrasonic waves : from theoretical modeling, numerical simulation and experimental validation to applications

Undersized damage, as typified by small cracks and pitting corrosion with their characteristic dimensions being remarkably smaller than the wavelength of a probing wave, is ubiquitous in engineering structures. It imposes immense threat to the health, integrity and durability of the engineering structures which are subjected to cyclic loading or thermal variation. It is fairly challenging to monitor and characterize the undersized damage using traditional non-destructive evaluation (NDE) and structural health monitoring (SHM) techniques. With this motivation, a SHM method based on the use of nonlinear features in guided ultrasonic waves (GUWs) is developed in this study, with an objective to evaluate undersized damage in a precise, quantitative and real-time manner. To start with, a dedicated analytical model is proposed, and in conjunction with a modal decomposition method and a variational principle-based algorithm, the model analytically depicts the generation of contact acoustic nonlinearity (CAN) induced owing to the interaction of probing GUWs with a “breathing” crack in a two-dimensional (2-D) scenario. On this basis, a set of damage indices, linked with CAN embodied in acquired GUW signals, is proposed to evaluate the severity of the crack quantitatively. The method, in principle, does not entail a benchmarking process against baseline signals. In virtue of a 2-D finite element model, the generation of second harmonics due to the “breathing” crack is numerically explored, and good coincidence between the results from the simulation and those
Achieving Mechanical Versatility in Robots and Structures Through Laminar Jamming

There are two major physical paradigms in robotics—soft robots and traditional rigid robots. Soft robots are made of compliant materials and have excellent adaptivity, robustness, and safety, whereas traditional rigid robots are made of stiff materials and have outstanding resolution, precision, speed, and load capacity. Building a single system that can selectively behave like either a soft or traditional rigid robot has been a grand challenge of the field. In this thesis, we rigorously investigate a promising mechanism that can help unite these paradigms. The mechanism is laminar jamming, in which a stack of flexible layers can exhibit dramatic changes in its mechanical properties (e.g., stiffness) when a pressure gradient is applied. When laminar jamming structures are integrated into soft robots, they can begin to exhibit the form, and consequently the function of traditional rigid systems. The mechanism was first reported in the robotics literature in 2000 [1]. However, a surprising number of fundamental questions have been left unexplored: For instance, what is the physical mechanism behind the phenomenon? How can the deformation of laminar jamming structures be predicted for both small and large loads, as well as during dynamic motions? Beyond stiffness, what other mechanical properties can the phenomenon change? In this thesis, we demonstrate how the laminar jamming phenomenon works; how laminar jamming structures can transform the stiffness, damping, kinematics, and dynamic response of robotic systems; how designers can relate design parameters to performance metrics; and how the performance of laminar jamming structures can be pushed well past the state-of-the-art. In doing so, we aim to foster robots and structures that cannot simply be classified as “soft” or “rigid,” but instead exhibit highly versatile mechanical behavior.
Transcending the brain: is there a cost to hacking the nervous system?

Abstract Great advancements have recently been made to understand the brain and the potential that we can extract out of it. Much of this has been centred on modifying electrical activity of the nervous system for improved physical and cognitive performance in those with clinical impairment. However, there is a risk of going beyond purely physiological performance improvements and striving for human enhancement beyond traditional human limits. Simple ethical guidelines and legal doctrine must be examined to keep ahead of technological advancement in light of the impending mergence between biology and machine. By understanding the role of modern ethics, this review aims to appreciate the fine boundary between what is considered ethically justified for current neurotechnology.
Enhanced Authentication System Performance Based on Keystroke Dynamics using Classification algorithms

Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.