Miracle-dz/UniFAR-FTUs huggingface dataset:
Recursive Formulations for N Input Asynchronous First Come First Served Arbiters

A First Come First Served (FCFS) arbiter services requests in order of their arrival, thereby achieving fairness defined by a minimum variance of waiting times. For example, the performance of multiprocessor architectures depends on fair access to shared memory. This paper proposes a new asynchronous FCFS arbiter hardware design based on recursive formulations that result in efficient and fast implementations. Each pair of requests is ordered by a metastable robust mutual exclusion element (ME). When a request arrives before all others, the corresponding acknowledge is asserted. However, close arrivals can cause cycles such as when request 0 is deemed to arrive before 1 and 1 before 2 and 2 before 0. Dike and Ostler in 1992 resolve cycles using groups of 4 requests, known as an M4. One contribution of the current paper is the recursive formulations that lead to more efficient simpler logic than the M4 approach. For example, with N=4 requests, fewer than half the CMOS transistors are required. This recursive approach can still be expressed in logic with delay proportional to log(N). The allocation of cycles to each acknowledge can be configured and this is analysed for the new recursive approach. The paper also discusses the occurrence of hazards in FCFS resolution logic, due to multiple paths to each output. Monotonic resolution logic and a simple gated ME override structure allow removal of glitches and cycles with minimal performance impact.
An Evolutionary Approach Based Optimization of Small Signal Parameters for GSDG MOSFET

This paper present the optimization of small- signal parameters for GSDG (Gate Stack Double Gate) MOSFET in deep submicron analogue circuit design. The evolutionary optimization algorithms taken for this study are CRPSO (Craziness-based Particle Swarm Optimization) and ALCPSO (Aging Leader Challenger based PSO). The small-signal parameters like trans-conductance, OFF-state current and different other design parameters of GSDG MOSFET are optimized in the regions of sub-threshold and saturation from the above mentioned evolutionary techniques to achieve the superior electrical performance of the MOSFET in the analogue domain. The results obtained from ALCPSO and CRPSO techniques are much improved as compared with the results of preceding literature and may be considered for the useful device design.
Interference Management without CSIT: A Broadcast Approach

Effective interference management in the multiuser interference channel strongly hinges on the availability of the channel state information at the transmitters (CSIT). In a broad range of emerging large-scale and distributed networks (e.g., the Internet of Things), however, acquiring the CSIT is prohibitive, due to the extensive information exchange that it imposes. In such circumstances, as a result, the interference management approaches that rely on the CSIT lose their effectiveness. This paper focuses on the two-user interference channel, and proposes a broadcast approach to interference management. Its hallmark is that the transmitters, unlike the receivers, are completely oblivious to instantaneous channel states. Each transmitter splits its message into multiple superimposed encoded information layers, where each layer is adapted to a given possible state for the combined states of all channels. Depending on the relative strengths of the direct and interfering channels, each receiver opportunistically decodes a subset of the received layers from both transmitters. An average achievable rate region is delineated serving as an inner bound on the average capacity region of the Gaussian interference channel in the absence of CSIT. Finally, it characterizes the gap between the achievable average sum-rate and the sum-rate capacity with the full CSIT in the asymptote of high signal-to-noise ratio. Numerical evaluations show that the cost of lacking CSIT is often insignificant.
Channel State Information Based Indoor Localization Error Bound Leveraging Pedestrian Random Motion

Indoor pedestrian motion detection based on the Wi-Fi Received Signal Strength (RSS) has been commonly deployed in recent years. However, the Channel State Information (CSI) based indoor localization methods can be selected to achieve higher localization accuracy since it contains the finer-grained physical-layer information of the signal. Lack of theoretical analysis of the CSI-based error bound that leverages the pedestrian motion posses a challenge to investigate the ideal performance. In this circumstance, this paper proposes the Cramer-Rao Lower Bound (CRLB) concept to derive out the indoor localization error bound leveraging the pedestrian motion that depends on the constructed signal propagation model by considering the relationship between the localization accuracy and the path loss, shadow fading, and multipath effect. Through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, and the impact of different experimental parameters on the localization performance is analyzed, as well as discusses the influence of the asynchronous effect between the transmitter and the receiver on the performance of the proposed localization error bound. The experimental results show that the derived error bound has the same trend as the actual error, which validate our theoretical analysis.
Classification of Mild Cognitive Impairment from multi-domain features of resting-state EEG

In recent years, electroencephalography (EEG) has emerged as a low-cost, accessible and objective tools for the early diagnosis of Alzheimer's disease (AD). AD is preceded by Mild Cognitive Impairment (MCI), typically refers to early-stage AD disease. The purpose of this study is to classify MCI patients from the multi-domain features of their electroencephalography (EEG). Firstly, we extracted the multi-domain (time, frequency and information theory) features from resting-state EEG signals before and after a cognitive task from 15 MCI groups and 15 age-matched healthy controls. Then, principal component analysis (PCA) was used to perform feature selection. After that, we compared the performance between SVM and KNN on our EEG dataset. The good performance was observed both from SVM and KNN, which demonstrates the effectiveness of multi-domain features. Furthermore, KNN performs better than SVM and the EEG signals after the cognitive task works better than those before the task.
A faster approach to ECG analysis in emergency situations

Every day, a substantial number of people need to be treated in emergencies and these situations imply a short timeline. Especially concerning heart abnormalities, the time factor is very important. Therefore, we propose a full-stack system for faster and cheaper ECG taking aimed at paramedics, to enhance Emergency Medical Service (EMS) response time. To stick with the golden hour rule, and reduce the cost of the current devices, the system is capable of enabling the detection and annotation of anomalies during ECG acquisition. Our system combines Machine Learning and traditional Signal Processing techniques to analyze ECG tracks to use it in a glove-like wearable. Finally, a graphical interface offers a dynamic view of the whole procedure.
A Visual Distortion Sensing Model for Early Detection of Macular Disorders

This paper presents a novel method to model the deformations that occur in the retina due to macular disorders such as Age-related Macular Degeneration (AMD). In this model, the retinal pigment epithelium (RPE) covered with cones and rods is considered as a uniform layer of known pixels. The projected image on these pixels is perceived as per this model. Furthermore, this model can efficiently be used to generate the test patterns for an accurate and efficient method for monitoring macular disorder. In this proposed model, two major geometric shapes of retinal deformation are taken into account. Both colourful and grayscale images are employed to estimate the perceived images under various circumstances and inputs. Based on these results, the proposed model can be used for the assessment of the progression of macular disorders.Clinical relevance—Retinal Pigment Epithelium (RPE), Field of view (FOV), Visual Distortion (VD)
Validation of Numerical Simulation for Transcranial Direct Current Stimulation with Spherical Phantom

Transcranial direct current stimulation (tDCS) is a promising brain modulation technique in clinical application. Computational models of brain current flow have been used to provide better insights into determining the stimulation parameters, but there are only a few studies to validate the numerical simulation model. The purpose of this study is to validate the simulation model of tDCS. A one-/three-layered spherical phantom model was constructed to mimic the human head. The tDCS-induced voltages were measured at different depth in the spherical phantom model with stereotactic-EEG (s-EEG) electrodes. Comparing the measured values with the simulation data from the computational models, we found that the computational and empirically measured electric field distributions on the brain surface is similar and that the deviation between the predicted and measured electric field value becomes larger near the electrode.
Mathematical Modelling of Drain Current Enhancement in High-k FD MOSFET

Novel MOSFET structures have been investigated extensively by researchers in recent years. The present paper investigates techniques for enhancement of drain current in FD (Fully Depleted) MOSFETs through use of novel materials to substitute the standard silicon dioxide gates. Consequently, a mathematical model is formulated to compare the change in drain current characteristics when titanium dioxide gate is used. The results obtained are in agreement with the hardware modelling and software simulation results obtained by other researchers in the field.
Cyber-phiscial Systems with Petri Nets to Model Hydropower Control

Cyber-physical systems (CPS) is a modeling method that integrates embedded computing technologies into the physical world providing interconnections and interdependencies between the physical and cyber components. In order to model such system a class of formalism is need to describe the structure and behavior of cyber and physical components. This work provides an overview on how the Petri nets formalism can be used for modeling and analyzes hydropower control modeled as CPS system. It is presented an implementation of the start and stop sequence control of an electric power generating unit to exemplify the CPS methodology, the advantage and benefits of using Petri net as a model formalism.
SK Channel Block and Adrenergic Stimulation Counteract Acetylcholine-Induced Arrhythmogenic Effects in Human Atria

There is increasing evidence on the role of the autonomic nervous system in the pathogenesis of atrial fibrillation. Interventions targeting autonomic modulation of atrial electrical activity have been shown to reduce the incidence of atrial arrhythmias. Additionally, recent investigations have proved that pharmacological therapies inhibiting small-conductance calcium-activated potassium (SK) channels are able to lessen cholinergic effects in the atria.In this study we use computational modeling and simulation to test individual and combined effects of SK channel block and adrenergic stimulation in counteracting detrimental effects induced by the parasympathetic neurotransmitter acetylcholine (ACh) on human atrial electrophysiology. Cell and tissue models are built that incorporate descriptions of SK channels as well as of isoproterenol (Iso)- and ACh-mediated regulation of the atrial action potential (AP). Three different cellular AP models, representing a range of physiological AP shapes, are considered and both homogeneous and heterogeneous ACh distributions in atrial tissue are simulated.At the cellular level, SK channel block is demonstrated to partially revert shortening of AP duration (APD) mediated by ACh at various doses, whereas 1 µM Iso has a variable response depending on the AP shape. The combination of SK block and Iso is in all cases able to take APD back to baseline levels, recovering between 82% and 120% of the APD shortening induced by 0.1 µM ACh. At the tissue level, SK block and Iso alone or in combination do not exert remarkable effects on conduction velocity, but the combination of the two is able to notably prolong the ACh-mediated APD shortening, thus increasing the wavelength for reentry.In conclusion, the results from this study support the combination of SK channel block and adrenergic stimulation as a potential option to counteract parasympathetically-mediated proarrhythmic effects in the human atria.
A Novel Non-parametric Two-Sample Test on Imprecise Observations

In kernel non-parametric two-sample test, we aim to determine whether two sets of precise observations (i.e., samples) are from the same distribution based on a selected kernel. However, in real world, precise observations may be unavailable. For example, readings on an analogue measurement equipment are not precise numbers but intervals since there is only a finite number of decimals available. Hence, we consider a new and more realistic problem setting—two-sample test on imprecise observations. We show that the test power of existing kernel two- sample tests will drop significantly if they do not take care of the vagueness of the imprecise observations, and to this end, we propose a fuzzy-based maximum mean discrepancy (F-MMD), a powerful two-sample test on imprecise observations. F-MMD is based on a novel fuzzy-based kernel function that can measure the discrepancy between two imprecise observations. This novel kernel function takes care of the vagueness of the imprecise observations and its parameters are optimized to maximize the approximate test power of F-MMD. Experiments demonstrate that F-MMD significantly outperforms competitive two-sample test methods when facing imprecise observations.
Data Deduplication With Random Substitutions

Data deduplication saves storage space by identifying and removing repeats in the data stream. Compared with traditional compression methods, data deduplication schemes are more computationally efficient and are thus widely used in large scale storage systems. In this paper, we provide an information-theoretic analysis of the performance of deduplication algorithms on data streams in which repeats are not exact. We introduce a source model in which probabilistic substitutions are considered. More precisely, each symbol in a repeated string is substituted with a given edit probability. Deduplication algorithms in both the fixed-length scheme and the variable-length scheme are studied. The fixed-length deduplication algorithm is shown to be unsuitable for the proposed source model as it does not take into account the edit probability. Two modifications are proposed and shown to have performances within a constant factor of optimal for a specific class of source models with the knowledge of model parameters. We also study the conventional variable-length deduplication algorithm and show that as source entropy becomes smaller, the size of the compressed string vanishes relative to the length of the uncompressed string, leading to high compression ratios.
A deep spatiotemporal graph learning architecture for brain connectivity analysis

In recent years, the conceptualisation of the brain as a "connectome" as summary measures derived from graph theory analyses, has become increasingly popular. Still, such approaches are inherently limited by the need to condense and simplify temporal fMRI dynamics and architecture into a purely spatial representation. We formulate a novel architecture based on Geometric Deep Learning which is specifically tailored to the one-step integration of spatial relationship between nodes and single-node temporal dynamics. We compare different spatiotemporal modelling mechanisms and demonstrate the effectiveness of our architecture in a binary prediction task based on a large homogeneous fMRI dataset made publicly available by the Human Connectome Project (HCP). As the idea of e.g. a dynamical network connectivity is beginning to make its way into the more mainstream toolset which neuroscientists commonly employ with neuroimaging data, our model can contribute to laying the groundwork for explicitly incorporating spatiotemporal information into every association and prediction problem in neuroscience.
Measurements of Harmonic Voltages at Multiple Locations in LV and MV Networks

This paper presents harmonic voltage measurements for a part of a distribution network including low-voltage (400 V) and medium-voltage (10 kV). Measurements were performed during 2017 and 2018 in the north of Sweden. This paper shows correlations between different harmonic orders in low voltage and the correlation between low and medium-voltage harmonics. This information can be used for harmonic propagation studies in distribution networks. The results showed significant correlation between the 5th and 7th harmonics for low-voltage side and very high correlation between 5th harmonic order in low-voltage and medium-voltage. It is also observed that 5th harmonic has almost the same values for all transformers connected to the same 40 kV network.
Detecting Education level using Facial Expressions in E-learning Systems

With the growth rate of modern technologies, Computer-based learning environment receives attention for academic goals. In this environment, a computer provides learners with a set of learning contents divided into learning levels. Usually, Computer-based learning environment research efforts detect the next level of the learner automatically based on the correct responses of the learner on a test at the end of every learning level. Different efforts use fuzzy approaches to handle the uncertainly in the learning environment. In this paper, a machine learning approach is proposed to detect the current education level of the learner based on a recorded facial expressions of the learners as well as important features of the learning environment. Several classifiers are employed to recognize the education level. The evaluation of the proposed approach on a real dataset shows that Support Vector Machine (SVM) outperforms the other classifiers and achieves accuracy of 87%. The paper also presents a regression method to detect the learning level as a continuous value. The evaluation of the regression methods shows that the Linear Regression with mean squared error of 0.0048 outperform SVR.
Optimization of Electric Vehicle Battery Size and Reduction Ratio Using Genetic Algorithm

A battery electric vehicle (BEV), known as a pure electric vehicle, exclusively uses chemical energy stored in rechargeable battery units (BUs), with no secondary source of propulsion. In this study a genetic algorithm (GA) based optimization study has been performed in order to find the optimum battery size or capacity and the final drive ratio (FDR) for the given driving profiles of an electric vehicle to find maximum range and minimum acceleration time for a BEV. Numerous startups and conventional automotive companies offer a variety of electric vehicles to customers from small electric motorcycles to full-size passenger vehicles. Battery sizing is very critical as it is the main factor for driving range. However excessive battery allocation will increase vehicle cost and weight significantly. Optimized input parameters of FDR and battery capacity were calculated to obtain maximum value of composite objective function which requires maximization of range and minimization of acceleration time of the vehicle at the same time, in the given interval of input parameters.
Selection of Spatial, Temporal and Frequency Features to Detect Direction Changes During Gait*

This paper studies the direction changes during the gait by means of two different distributions of electrodes located in the motor, premotor and occipital areas. The objective is analyzing which areas are involved in the detection of the intention of turning while the person is walking. The signals in both options are characterized with frequency and temporal features and classified following a cross-validation process. A 95% of success rate is achieved when the electrodes are disposed along the motor, premotor and occipital areas.Clinical Relevance— The objective of this study is applying the acknowledgements obtained in the designing of a brain-machine interface (BMI) based in the detection of the intention of the direction change during the gait. This BMI has clinical relevance in the rehabilitation of the gait in patients with motor injuries, assisting the patient to perform the movements as realistic as it is possible.
Analog Building Blocks of dc-dc Converters: Examining Fundamental Concepts

Dc-dc conversion plays the central part in the growing field of power management, which converts input voltages from various different energy sources into circuit-level voltages. At the system level, input voltages may range from a few millivolts (in the case of energy harvesting) to a few volts (in the case of low-voltage batteries). There is a trend toward higher voltages driven by the need to minimize dc-dc conversion losses. For this reason, servers in data centers utilize an intermediate voltage of 48 V [1]. Likewise, hybrid and electric cars are equipped with a 48-V battery in addition to the conventional 12-V board net and the ~400-V high-voltage battery that supplies the drive train [1]. Other applications with higher voltages include USB, e-bikes, or drones.
Time-Encoding Analog-to-Digital Converters: Bridging the Analog Gap to Advanced Digital CMOS?Part 2: Architectures and Circuits

The scaling of CMOS technology deep into the nanometer range has created challenges for the design of highperformance analog ICs: they remain large in area and power consumption in spite of process scaling. Analog circuits based on time encoding [1], [2], where the signal information is encoded in the waveform transitions instead of its amplitude, have been developed to overcome these issues. While part one of this overview article [3] presented the basic principles of time encoding, this follow-up article describes and compares the main time-encoding architectures for analog-to-digital converters (ADCs) and discusses the corresponding design challenges of the circuit blocks. The focus is on structures that avoid, as much as possible, the use of traditional analog blocks like operational amplifiers (opamps) or comparators but instead use digital circuitry, ring oscillators, flip-flops, counters, an so on. Our overview of the state of the art will show that these circuits can achieve excellent performance. The obvious benefit of this highly digital approach to realizing analog functionality is that the resulting circuits are small in area and more compatible with CMOS process scaling. The approach also allows for the easy integration of these analog functions in systems on chip operating at "digital" supply voltages as low as 1V and lower. A large part of the design process can also be embedded in a standard digital synthesis flow.