Miracle-dz/UniFAR-FTUs huggingface dataset:
Resting-State Regional Homogeneity Analysis on Real-Time fMRI Emotion Self-Regulation Training

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) is biofeedback of realtime fMRI signals for self-regulating brain function. Most studies focus on the task-state rtfMRI-NF for the moment. Furthermore, resting-state analysis brings another angle to explore the neural mechanism of emotion regulation and can be an important way to reflect brain function. Regional homogeneity (ReHo) analysis is a method to study the consistency of the spontaneous neuronal activity between the given voxel and its adjacent voxel in the resting-state. It can reveal the functional and anatomical closely linked neural circuits. In this paper, we designed a rtfMRI-NF experiment for human emotion self-regulation and analyzed brain function after rtfMRI-NF training by ReHo method. In the experiment, the subjects divided into two groups, the experiment and the control group. For the experiment group, through the multivariate voxel pattern analysis (MVPA) calculation of the real-time fMRI system, objects are provided with continuous tracking information display of their emotional state. In the control group, it was the same as that of the experiment group, except for that the feedback value was independent to emotion. We performed the method of ReHo analysis on the resting state data collected from the two groups after the self-regulation tasks, and experiment results showed that there were significantly different between the ReHo maps of the two groups in the temporal lobe, prefrontal cortex, parahippocampal gyrus, and fusiform gyrus. These brain regions were closely related to emotional processing, and the results are consistent with previous studies. Furthermore, ReHo can reflect the characteristic changes of spontaneous neural activity in the brain, it makes another way to help discover the mechanism of neurofeedback, and it can be used as a research direction to assist in the prediction and diagnosis of mental illness.
A Machine Learning Approach for Radar Based Height Estimation

This work proposes a new radar-based height estimation approach using machine learning. The approach takes advantage of multipath propagation of radar waves to produce robust features. A random forest regressor is trained using the obtained features for extended objects height estimation. First, data of extended targets with various heights located at different positions is recorded. Subsequently, range, angle, amplitude and phase information is processed using the Fourier based high resolution spectral estimation method RELAX. The processed information is then used to generate features based on a multipath target height equation which assumes the Line of Sight (LOS) and (Non Line of Sight) NLOS peaks are known. Finally acquired height estimation results from a trained random forest regressor are presented and discussed. The results show that the proposed method is capable of predicting target heights with high accuracy, without requiring the LOS and NLOS peaks to be known.
A Compact, Voltage-Mode Type-I PLL With Gain-Boosted Saturated PFD and Synchronous Peak Tracking Loop Filter

Despite their inherent stability, area-efficient loop filters, and insensitivity to phase-frequency detector nonlinearity and dead-zone, type-I phase-locked loops (PLLs) are used infrequently because of two major limitations—limited lock-range and large reference spurs. This paper introduces a type-I PLL that takes advantage of the inherent benefits of the architecture, while tackling its limitations by means of gain boosting in the forward path for lock-range improvement and synchronous peak tracking of the voltage-controlled oscillator control voltage for reference spur reduction. Sampling in the loop filter changes the loop dynamics, and methods to accurately predict the closed-loop response, starting from state-space equations, are provided. A digital-friendly, voltage-mode topology is proposed that does not need charge pumps or opamps. A prototype 2.2–2.8-GHz PLL occupies a core area of 0.12 mm<sup>2</sup> in 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS and achieves −103.4 dBc/Hz in-band phase noise, −65-dBc reference spur, and 2.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> worst-case lock-time while consuming 6.8 mW from a 1.2-V supply.
Chemical and Electrochemical Growth of Hydroxyapatite on 3D Machined Titanium Alloy

In this work was studied the influence of pretreatment and the method of hydroxyapatite (HAp) deposition on Ti6Al4V alloy for its potential application as an implants. А comparative analysis of the morphology and the elemental composition of chemically and electrochemically obtained HAp were presented. Free form samples were pretreated chemically in a strong alkaline solution or were anodized in order to increase adhesion between the deposited HAp layer and the substrate. It has been found that the morphology of HAp crystals depends mainly on the method of deposition and insignificantly on the surface pretreatment methods.
Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors

This paper focuses on bearing fault (BF) detection in induction motors based on a combination of advanced signal processing tools with motor current signature analysis (MCSA). The applied tools consist on two main techniques: the power spectral density (PSD) estimation for spectral analysis and wavelet techniques for time-frequency analysis. In order to identify the signatures related to bearing faults in the current spectrum, the Welch and the Multiple Signal Classification (MUSIC) PSD estimations are applied. Since the PSD techniques are not appropriate for non-stationary conditions, the Discrete Wavelet Transform (DWT), the Wavelet Packet Transform (WPT) and the Stationary WPT (SWPT) are used and compared. The efficiency of the proposed approaches is verified by several experiments corresponding to three types of bearing faults. The wavelet analysis, especially the SWPT, shows its ability to identify the BF signatures more accurately than other wavelet techniques regardless the load level.
Multimodal Priming of Drivers for a Cooperative Take-Over

Recent research has revealed the potential for using remaining capabilities of a driving automation in the form of information processing for driver support in take-over scenarios. As a next step, progressing technical development could enable support in form of partly active control leading to situations of cooperative shared control between automation and driver. To maximize performance in cooperative takeovers, drivers should be informed sufficiently about the automation's intent for the shared control phase. Priming a driver for an automated control action shares basic concepts with directional yet conventional take-over requests. Based on results from the literature concerning directional take-over requests, we designed three different modality configurations. In contrast to previous studies and because of the background of a later cooperative take-over, we focused on perception, taskload and criticality assessment of the directional information instead of evaluating purely human take-over performance. Results indicate better perception for configurations with an auditory component, yet taskload and criticality ratings were not affected by the different modalities.
Hardware-In-The-Loop Testing of Connected and Automated Vehicle Applications: A Use Case For Cooperative Adaptive Cruise Control

Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods that allow physical test vehicles to interact with virtual vehicles from traffic simulation models. This provides an evaluation environment that can replicate deployment conditions at early stages of CAV implementation-without incurring excessive costs related to large field tests. In this study, a hardware-in-the-loop (HIL) testing system for CAV applications is proposed. Cooperative adaptive cruise control (CACC) is one of the key CAV applications that has received a lot of attention. This study develops a HIL proof-of-concept testing prototype for CAV applications in a vehicle-to-vehicle (V2V) environment. The contributions of this study include developing a HIL testing architecture for V2V-based CAV applications; implementing the proposed HIL architecture; developing a prototype; and testing CACC as the selected use case to observe HIL system performance. The results of this effort also contribute to a better understanding of CACC string performance.
A Concomitant Muscle Injury Does Not Worsen Traumatic Brain Injury Outcomes in Mice

Traumatic brain injury (TBI) often involves multitrauma in which concurrent extracranial injury occurs. We previously demonstrated that a long bone fracture exacerbates neuroinflammation and functional outcomes in mice given a TBI. Whether other forms of concomitant peripheral trauma that are common in the TBI setting, such as skeletal muscle injury, have similar effects is unknown. As such, here we developed a novel mouse multitrauma model by combining a closed-skull TBI with a cardiotoxin (CTX)-induced muscle injury to investigate whether muscle injury affects TBI outcomes. Adult male mice were assigned to four groups: sham-TBI + sham-muscle injury (SHAM); sham-TBI + CTX-muscle injury (CTX); TBI + sham-muscle injury (TBI); TBI + CTX-muscle injury (MULTI). Some mice were euthanized at 24 h post-injury to assess neuroinflammation and cerebral edema. The remaining mice underwent behavioral testing after a 30-day recovery period, and were euthanized at 35 days post-injury for post-mortem analysis. At 24 h post-injury, both TBI and MULTI mice had elevated edema, increased expression of GFAP (i.e., a marker for reactive astrocytes), and increased mRNA levels of inflammatory chemokines. There was also an effect of injury on cytokine levels at 35 days post-injury. However, the TBI and MULTI mice did not significantly differ on any of the measures assessed. These initial findings suggest that a concomitant muscle injury does not significantly affect preclinical TBI outcomes. Future studies should investigate the combination of different injury models, additional outcomes, and other post-injury time points.
Optimal Path for a Car-like Robot to Reach a Given Straight Line

This paper presents a closed form solution for the optimal path for a car to reach a given straight line with a specified direction, i.e., the shortest distance between an arbitrary pose $(x, y,\theta_{0})$ and a target line ($d, \theta$) with the constraint of a maximum curvature of the path. This shortest path metric does not consider collision with obstacles. The result can serve as a heuristic to solve path planning problems for autonomous vehicles, especially for parking and pull-out scenarios, where a vehicle needs to drive from a reference line to a given final pose or vice versa. The distance metric is verified with the shortest path obtained from an exhaustive sampling along the target line. The comparison shows that the explicit metric function provides the optimal path length in an instant time frame. As a result, the distance metric can be used in online planning applications.
Transmit Power Minimization for Downlink Multi-Cell Multi-Carrier NOMA Networks

In this letter, to minimize the total transmit power with constraints of the user data rate requirements, the radio resource allocation problem for multi-cell and multi-carrier non-orthogonal multiple access (MCMC-NOMA) network, which proves to be an NP-hard problem, is investigated. Therefore, to deal with this optimization problem, a centralized minimum power control algorithm with the fixed user assignment is developed first, upon which a greedy user clustering and power allocation scheme is proposed. The Monte Carlo simulation results demonstrate that the proposed resource allocation algorithm can dramatically reduce the total power consumption compared with ones in the existing literatures. Furthermore, NOMA really outperforms OMA networks in terms of transmit power consumption.
A New Kind of Conditional Proxy Re-Encryption for Secure Cloud Storage

Secure cloud storage has important applications in our big data-driven society, and to achieve secure cloud storage, we need to enforce strong access control mechanism. Proxy re-encryption (PRE) has been shown to be an effective tool of constructing cryptographically enforced access control schemes. In a traditional PRE scheme, a semi-trusted proxy can convert all ciphertexts for a delegator to ciphertexts for a delegatee once the proxy obtains the relevant re-encryption key from the delegator. In many practical applications, however, a fine-grained delegation of decryption abilities may be demanded, and thus, the notion of conditional PRE (C-PRE) is introduced, which allows only the ciphertexts satisfying a concrete condition to be converted by the proxy. In this paper, we introduce a special kind of C-PRE, sender-specified PRE (SS-PRE), which enables the delegator to delegate the decryption right of the ciphertexts from a specified sender to his/her delegatee. We give a formal definition of SS-PRE and its security model. We also provide the concrete constructions of an IND-CPA secure SS-PRE scheme and an IND-CCA secure SS-PRE scheme with the properties of unidirectionality and single-use and prove the security of both schemes in the standard model. The detailed analysis shows that our new IND-CCA secure SS-PRE scheme achieves a higher efficiency in computation cost and ciphertext size than the conventional C-PRE schemes.
ROCK Inhibitor Y27632 Induced Morphological Shift and Enhanced Neurite Outgrowth-Promoting Property of Olfactory Ensheathing Cells via YAP-Dependent Up-Regulation of L1-CAM

Olfactory ensheathing cells (OECs) are heterogeneous in morphology, antigenic profiles and functions, and these OEC subpopulations have shown different outcomes following OEC transplantation for central nervous system (CNS) injuries. Morphologically, OECs are divided into two subpopulations, process-bearing (Schwann cells-like) and flattened (astrocytes-like) OECs, which could switch between each other and are affected by extracellular and intracellular factors. However, neither the relationship between the morphology and function of OECs nor their molecular mechanisms have been clarified. In the present study, we first investigated morphological and functional differences of OECs under different cytokine exposure conditions. It demonstrated that OECs mainly displayed a process-bearing shape under pro-inflammatory conditions (lipopolysaccharide, LPS), while they displayed a flattened shape under anti-inflammatory conditions [interleukin-4 (IL-4) and transforming growth factor-β1 (TGF-β1)]. The morphological changes were partially reversible and the Rho-associated coiled-coil-containing protein kinase (ROCK)/F-actin pathway was involved. Functionally, process-bearing OECs under pro-inflammatory conditions showed increased cellular metabolic activity and a higher migratory rate when compared with flattened OECs under anti-inflammatory conditions and significantly promoted neurite outgrowth and extension. Remarkably, the morphological shift towards process-bearing OECs induced by ROCK inhibitor Y27632 enhanced the neurite outgrowth-promoting property of OECs. Furthermore, as the downstream of the ROCK pathway, transcriptional co-activator Yes-associated protein (YAP) mediated morphological shift and enhanced the neurite outgrowth-promoting property of OECs through upregulating the expression of the neural adhesion molecule L1-CAM. Our data provided evidence that OECs with specific shapes correspond to specific functional phenotypes and opened new insights into the potential combination of OECs and small-molecule ROCK inhibitors for the regeneration of CNS injuries.
I Know You Better Than You Know Yourself: Estimation of Blind Self Improves Acceptance for an Agent

After analyzing almost all programs pertaining to winning complete information games, game study started to focus on the challenge of programming acceptable game players. A key factor required to create an acceptable player agent is the knowledge of the user's internal state. People like those who want to know themselves, especially those who want to know their unconscious self-state, also called the "blind self. " This study proposes an approach to analyze an agent's ability for estimating a user's blind self as an improvement in human-agent interaction. The authors chose a card game called Hanabi as an evaluation of this type of recursive intelligence. Hanabi is a cooperative and an incomplete information card game in which each player's cards are unknown to other players. A player decides whether to build a set of cards or discard them while providing hints to the other players. Previous research has demonstrated that the imitation of human behavior that corrects incomplete information increases the score in a Hanabi game between agents. The authors evaluated the agent's function to modify incomplete information based on the behavior of the players in the game between a human and an agent. The authors experimented with humans and two types of agents. The latter differed according to the need to imitate and modify incomplete information as a cooperator among the experiment's participants. Afterward, the game results were analyzed, and the impressions of the participants were evaluated. The results demonstrated that while the ability of estimation of the blind self is ineffective for increasing the score, the user developed a good impression of the agent when incomplete information was modified properly, resulting in a similar feeling in the case of a human collaborator.
Redundancy Elimination for Data Aggregation in Wireless Sensor Networks

Energy consumption is a critical issue affecting the lifetime of wireless sensor networks (WSNs). Data aggregation approach surfaces as an important method enabling to reduce the energy consumption of sensor nodes and improve the bandwidth utilization. This paper proposes a Redundancy Elimination Data Aggregation algorithm, called REDA, based on pattern generation approach. The proposed pattern is specific to the sensed data and it employs differential data collected from sensor nodes in consecutive iterations. Thus, the transmission of redundant data from sensor nodes, within the same cluster, to the relative cluster head (CH) is avoided during all iterations. Performance evaluation proves that energy consumption using REDA algorithm is saved up to 44 % compared with protocol without data aggregation methods. Moreover, compared with existent data aggregation algorithms specifically, ESPDA and SRDA, the simulation results demonstrates that the proposed technique is efficient in terms of bandwidth occupancy.
A Prioritized Routing Assistant for Flow of Traffic

Traffic management during emergency evacuation throws up a different set of challenges than a regular traffic management. We focus here on one particular challenge, namely prioritized routing. Prioritized routing may need to happen even during normal times but stands out during emergencies, since emergency vehicles, police vehicles and vehicles (such as buses) that carry a lot more people may need to have a higher priority in terms of evacuation. It is also reasonable to assume that traffic police may need to perform a centralized control of traffic since they typically have a global view of the emergency and possibly have accurate real-time updates. We therefore make the following contributions in this paper: (a) We map the prioritized routing problem to the minimum-cost maximum-flow problem, a standard problem formulation in network flow theory. (b) We then develop the Prioritized Routing Assistant for Flow of Traffic (PRAFT) that casts the prioritized routing problem which includes a notion of priority of vehicles and priority of routes into the minimum cost maximum flow problem. (c) Through a series of experiments performed using the well-known traffic simulator SUMO, we could establish that PRAFT indeed maps higher priority vehicles to better quality routes and is monotonic in the sense that decreasing priority order of vehicles maps to a decreasing route quality.
Social Media Data Analysis Using MapReduce Programming Model and Training a Tweet Classifier Using Apache Mahout

Twitter, a micro-blogging service, has been generating a large amount of data every minute as it gives people chance to express their thoughts and feelings quickly and clearly about any topics. To obtain the desired information from these available big data, it requires high-performance parallel computing tools along with machine learning algorithms' support. Emerging big data processing frameworks (e.g. Hadoop) can handle such big data effectively. In this paper, we, firstly introduce a novel approach to automatically classify Twitter data obtained from British Geological Survey (BGS), collected using some specific keywords such as landslide, landslides, mudslide, landfall, landslip, soil sliding, based on tweet post date and the countries where tweets are posted using MapReduce algorithm. We then propose a model to distinguish the tweets if they are landslides-related using Naïve-Bayes machine learning algorithm with n-Grams language model on Mahout. This paper also describes an algorithm for the pre-processing steps to make the semi-structured Twitter text data ready for classification. The proposed methods are useful for the BGS and other interested people to be able to see the name and number of the countries where the tweets are sent, the number of tweets sent from each country, the dates and time intervals of the tweets, and to classify the tweets whether they are related to landslides.
Progress Maximization Model Predictive Controller

This paper addresses the problem of progress maximization (i.e., traveling time minimization) along a given path for autonomous vehicles. Progress maximization plays an important role not only in racing, but also in efficient and safe autonomous driving applications. The progress maximization problem is formulated as a model predictive controller, where the vehicle model is successively linearized at each time step, yielding a convex optimization problem. To ensure real-time feasibility, a kinematic vehicle model is used together with several linear approximations of the vehicle dynamics constraints. We propose a novel polytopic approximation of the $^{\prime}g-g^{\prime}$ diagram, which models the vehicle handling limits by constraining the lateral and longitudinal acceleration. Moreover, the tire slip angles are restricted to ensure that the tires of the vehicle always operate in their linear force region by limiting the lateral acceleration. We illustrate the effectiveness of the proposed controller in simulation, where a nonlinear dynamic vehicle model is controlled to maximize the progress along a track, taking into consideration possible obstacles.