Miracle-dz/UniFAR-FTUs huggingface dataset:
Macroscale White Matter Alterations Due to Traumatic Cerebral Microhemorrhages Are Revealed by Diffusion Tensor Imaging

With the advent of susceptibility-weighted imaging (SWI), the ability to identify cerebral microbleeds (CMBs) associated with mild traumatic brain injury (mTBI) has become increasingly commonplace. Nevertheless, the clinical significance of post-traumatic CMBs remains controversial partly because it is unclear whether mTBI-related CMBs entail brain circuitry disruptions which, although structurally subtle, are functionally significant. This study combines magnetic resonance and diffusion tensor imaging (MRI and DTI) to map white matter (WM) circuitry differences across 6 months in 26 healthy control volunteers and in 26 older mTBI victims with acute CMBs of traumatic etiology. Six months post-mTBI, significant changes (p < 0.001) in the mean fractional anisotropy of perilesional WM bundles were identified in 21 volunteers, and an average of 47% (σ = 21%) of TBI-related CMBs were associated with such changes. These results suggest that CMBs can be associated with lasting changes in perilesional WM properties, even relatively far from CMB locations. Future strategies for mTBI care will likely rely on the ability to assess how subtle circuitry changes impact neural/cognitive function. Thus, assessing CMB effects upon the structural connectome can play a useful role when studying CMB sequelae and their potential impact upon the clinical outcome of individuals with concussion.
Classification of Different Types of Faults in a Photovoltaic System

Over the past several years, the rapid growth of solar industry has expanded the need of photovoltaic systems(PV). Fault analysis in solar PV arrays is necessary because it helps to increase reliability, improve efficiency and also helps to provide safety in PV Systems. If the faults in a PV system are not detected in time, not only there's a reduction in the power generation capability but also the faults are cascaded through the entire system. Health monitoring of the PV system should be done continuously since its very crucial to detect the causes which may ultimately effect the performance of the system. All possible types of faults which occur in a photovoltaic system are classified, discussed and then presented in this paper. All possible types of faults which may be present on both AC and DC side of the system are mentioned in this paper. Besides different types of faults in PV array, simulation model using MATLAB for partial shading as well as for MPPT are also presented in this paper.
Uncertainty and Disturbance Estimator Based Control Methodology for Speed Control of PMSM Drives

In this paper, speed control strategy for Permanent magnet synchronous motor (PMSM) drive based on uncertainty and disturbance estimator (UDE) is proposed. Unlike traditional PI controllers, the control strategy designed is robust to internal parameters variations and external torque disturbances. The controller design is insensitive to different uncertainties and tuning of controller parameters is eliminated. MATLAB simulation is presented to demonstrate the tracking and disturbance rejection capability of UDE based speed control strategy of PMSM drive. The control yields satisfactory results for varied types of disturbances.
Developing an Ethernet Interface Using a General Purpose Microcontroller and Ethernet Controller for Power Amplifier

With the rising trend, internet has become more widespread. A number of devices with microcontroller have been widely used in the industrial field. As all devices don't have the internet interface, the data from them cannot be transmitted in the network. Considering the technological development in the market place, the power amplifier with Ethernet interface options plays a significant role in monitoring and controlling operation in real time. For this purpose the power amplifier needs a physical communication port with the network. This paper presents the design and implementation of such Ethernet interface for power amplifier with microcontroller. The power amplifier communicates with the Ethernet controller through SPI (serial peripheral interface) port. The Ethernet controller ENC28J60 is the main element manufactured by the Microchip company, according to IEEE 802.3 standards along with the MAC and PHY modules. The Ethernet controller ENC28J60, an RJ45 connector and few passive components along with general purpose microcontroller is connected to the network and it works at a speed of 64Mbps.
A 3.4-pW 0.4-V 469.3 ppm/°C Five-Transistor Current Reference Generator

This letter presents a near-zero power constant with temperature (CWT) current reference generator that utilizes only five transistors in standard CMOS technology. By operating an off-state nMOS transistor as a self-biased current source driving an nMOS transistor in the subthreshold region, a temperature stabilized voltage is generated, which is then buffered by a third nMOS and applied to a gate-leakage transistor. To improve line regulation, a fifth nMOS operating in off-state is employed, constructing a five-transistor (5T) <inline-formula> <tex-math notation="LaTeX">$ I_{\text {REF}}$ </tex-math></inline-formula> generator. Measurement results of 13 5T <inline-formula> <tex-math notation="LaTeX">$ I_{\text {REF}}$ </tex-math></inline-formula> generator samples fabricated in 65-nm CMOS revealed an average temperature coefficient of 469.3 ppm/°C from −20 °C to 60 °C when operating at a supply voltage of 0.4 V. Measured with a supply voltage from 0.4 to 1.2 V, the proposed 5T <inline-formula> <tex-math notation="LaTeX">$ I_{\text {REF}}$ </tex-math></inline-formula> generator achieved an average line regulation of 2.5%/V. The 5T <inline-formula> <tex-math notation="LaTeX">$ I_{\text {REF}}$ </tex-math></inline-formula> generator consumed 3.4 pW at 20 °C and occupied an area of 0.008 mm<sup>2</sup>, thereby enabling a near-zero-power, low-cost CWT <inline-formula> <tex-math notation="LaTeX">$ {I}_{\text {REF}}$ </tex-math></inline-formula> generation for next-generation Internet of Things applications.
In Search of Actionable Patterns of Lowest Cost – A Scalable Action Graph Method

Action Rules benefit its users to achieve their goals by extracting actionable information hidden in the large data. Undertaking such actionable recommendations incur some form of cost to users. The actionable recommendation system fails when the recommended actions are cost wise unendurable or non-profitable and uninteresting to the end user. Finding low cost actionable patterns in larger datasets is a time consuming and requires a scalable approach. In this work, we give a representation for Action Rules as graphs called Action Graphs, which uncovers undiscovered relationship between actionable patterns in the recommended action rules. Also, we define three popular graph algorithms: Dijkstra's Shortest Path algorithm, Breadth First Search algorithm and Depth First Search algorithm to search low cost Action Rules from Action Graphs in the distributed scenario using Spark framework. Upto our knowledge, our Depth First algorithm is the first work to be implemented using Spark framework. We apply the proposed algorithms to three datasets in transportation, medical, and business domains. Results show these domains can benefit from the discovered actionable recommendations of low cost, in time efficient way.
The Anatomy of Inference: Generative Models and Brain Structure

To infer the causes of its sensations, the brain must call on a generative (predictive) model. This necessitates passing local messages between populations of neurons to update beliefs about hidden variables in the world beyond its sensory samples. It also entails inferences about how we will act. Active inference is a principled framework that frames perception and action as approximate Bayesian inference. This has been successful in accounting for a wide range of physiological and behavioral phenomena. Recently, a process theory has emerged that attempts to relate inferences to their neurobiological substrates. In this paper, we review and develop the anatomical aspects of this process theory. We argue that the form of the generative models required for inference constrains the way in which brain regions connect to one another. Specifically, neuronal populations representing beliefs about a variable must receive input from populations representing the Markov blanket of that variable. We illustrate this idea in four different domains: perception, planning, attention, and movement. In doing so, we attempt to show how appealing to generative models enables us to account for anatomical brain architectures. Ultimately, committing to an anatomical theory of inference ensures we can form empirical hypotheses that can be tested using neuroimaging, neuropsychological, and electrophysiological experiments.
Line Constrained Estimation With Applications to Target Tracking: Exploiting Sparsity and Low-Rank

Trajectory estimation of moving targets is examined; in particular, quasi-linear trajectories are considered. Background subtraction methods, exploiting low-rank backgrounds, and sparse features of interest are extended to incorporate linear constraints. The line constraint is enforced via a rotation that yields an additional low rank condition. The proposed method is applied to single object tracking in video, wherein the trajectory can be parameterized as a line. The optimization is solved via the augmented Lagrange multiplier method. An average performance improvement of 4 dB is observed over previous background subtraction methods for estimating the position and velocity of the target. Furthermore, about a 6.2 dB gain is seen over previous target tracking methods that do not exploit the linear nature of the trajectory. The Cramér–Rao bound (CRB) for background subtraction with a linear constraint is derived and numerical results show that the proposed method achieves near optimal performance via comparison to the CRB. An aggregated error is shown to converge to zero and a boundedness analysis is conducted which suggests that the iterative algorithm is convergent as confirmed by simulations. Finally, the proposed technique is applied to real video data and is shown to be effective in estimating quasi-linear trajectories.
Detection of Potholes Using a Deep Convolutional Neural Network

Poor road conditions like cracks and potholes can cause inconvenience to passengers, damage to vehicles, and accidents. Detecting those obstacles has become relevant due to the rise of the autonomous vehicle. Although previous studies used various sensors and applied different image processing techniques, performance is still significantly lacking, especially when compared to the tremendous leaps in performance with computer vision and deep learning. This research addresses this issue with the help of deep learning–based techniques. We applied the You Only Look Once version 2 (YOLOv2) detector and propose a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. Despite a limited amount of learning data and the challenging nature of pothole images, our proposed architecture is able to obtain a significant increase in performance over YOLOv2 (from 60.14% to 82.43% average precision).
Assessing Closed Captioning Quality Using a Multilayer Perceptron

Closed Captioning (CC) is a telecommunication service to provide Deaf or Hard of Hearing (D/HOH) audiences the text equivalent of what hearing audiences experience in TV. The quality of CC is often interpreted as an accuracy and assessed in the empirical measure of counting number of errors. Although the regulators necessitate certain rules in the factors of CC quality, the D/HOH community members, who are the primary audiences to the CC, are not completely satisfied. One solution to solve this issue can be including the perspective of D/HOH audience in the assessment process. This research made an attempt to design an automatic quality assessment system for CC using artificial neural networks-multilayer perceptron trained from the D/HOH audience's subjective ratings, and the representative values extracted from the caption file, and the transcript file. As an initial stage of research, the trained model was then compared with other statistical regression models.
Cooperative Estimation of Primary Traffic Under Imperfect Spectrum Sensing and Byzantine Attacks

Cognitive radio (CR) systems exploit the accurate knowledge of primary traffic statistics to improve the CR performance and reduce the harmful interference on primary network. It is essential for cooperative CRs to operate in an efficient secured manner while estimating the primary statistics. In this paper, a new reporting mechanism for cooperative estimation of primary traffic is proposed to increase the spectrum and energy efficiency. This is achieved by the reduction of the reporting channel overhead from cooperative users to the fusion center. Simulation results show that the proposed scheme reduces significantly the signaling overhead, thus making the system more spectrum and energy efficient. Moreover, the openness of cooperative CR makes it susceptible to data falsification attacks, also known as Byzantine attacks. This attack poses a series of damages on the reliability of the estimation of primary traffic. In this paper, we define the types of malicious coordinated attacks on CRs and analyze the possibility of estimating the primary traffic statistics under these attacks. Moreover, we provide a simple yet effective countermeasure based on the proposed reporting for cooperative estimation. Simulation evaluation shows that the proposed algorithm provides an excellent countermeasure for spectrum sensing data falsification attacks.
In Vivo Electrical Conductivity Imaging of Animal Tumor Model at 7T using Electrical Properties Tomography

Ex vivo studies have shown that various diseases alter the electrical properties of tissues compared to healthy nearby tissues. Therefore, electrical conductivity can be used as a diagnostic parameter for e.g. tumor diagnosis. For in vivo measurements, magnetic resonance electrical properties tomography (MREPT) was used and electrical conductivity was reconstructed from the B1+ phase. The technique was first evaluated using homogeneous and heterogeneous phantoms. Then a mouse with a tumor was scanned and the conductivity is reconstructed from the B1+ phase map. The reconstructed conductivity in the phantom experiments was in good agreement with the target conductivity map and the conductivity map of the animal revealed good agreement with the co-axial probe measurement. Our work confirms the possibility of accurate in vivo conductivity assessment in disease.
One Octave Bandwidth Rectifier With a Frequency Selective Diode Array

In this paper, a high-efficiency rectifier with one octave bandwidth is proposed by proposing a diode array with a frequency selective topology. With the frequency selective topology, the low-and high-band rectifiers are combined together without extra matching circuit at the input leading to a high-efficiency broadband design. For validation, a prototype rectifier was fabricated and characterized. The measured results show that the RF-dc power conversion efficiency (PCE) maintains above 70% from 1.75 to 3.55 GHz at an input power of 10 dBm. The measured PCE remains more than 50% within 1.4–3.7 GHz and the <inline-formula> <tex-math notation="LaTeX">$|S_{\textrm {11}}|$ </tex-math></inline-formula> is lower than −10 dB from 1.7 to 3.6 GHz.
Electrooculography as a Tool For Managing Application

Eye movements are captured using the electrooculography method. This method uses an electrical potential which is able to measure thank to the eye movement and change a dipole of it. The aim was the design and realization of a light target made of LEDs, which illumination is controlled by movements of eyes. It was developed hardware for eye’s movement monitoring to provide analogue signals for signal processing. The connection between the EOG device, the light target and the PC was provided by the NI ELVIS II. The evaluated results were used for the developing of interactive application. The interactive application was developed as a feedback of eye movement. The LabView graphical development environmen was used. It was created an application as a feedback of eye’s movement, the "SNAKE" game.
Novel Concept for Heterogeneously Integrated High-Speed III-V Photodetector on Silicon Nitride Waveguide

A novel design for heterogeneously integrated high-speed III-V photodetectors on the Si3N4 platform is proposed. This new approach offers a new platform to merge III-V and Si3N4 for 3D integration. The design is shown to have a high tolerance to bonding and lithography misalignment.
Biomarker Detection from fMRI-Based Complete Functional Connectivity Networks

The comprehensive set of neuronal connections of the human brain, which is known as the human connectomes, have been widely used in the research of neurological and neurodevelopmental disorders. Functional Magnetic Resonance Imaging (fMRI) has facilitated the research by capturing functional activities of the neurons. fMRI-based functional connectomes are used to extract the complete functional connectivity networks, which are edge-weighted complete graphs. In functional connectivity networks, each node represents one brain region or Region of Interest (ROI), and each edge-weight represents the functional similarity of the adjacent ROIs. In order to leverage graph mining methodologies, these complete graphs are often made sparse by applying thresholds on edge-weights. This approach can result in losing discriminative information, while addressing the issue of biomarkers detection, that is, finding discriminative ROIs and connections, given the data of healthy and disabled population. In this work, we present a framework for representing the complete functional connectivity networks in a threshold-free manner and finding the biomarkers by using feature selection algorithms. Additionally, for computing the impact of the biomarkers on the healthy and disabled subjects, we apply tensor decomposition. Experiments on a fMRI dataset of neurodevelopmental reading disabilities show the highly interpretable nature of our approach in finding the biomarkers of the disease.
Embeddedness, Pro-Sociality, and Social Influence: Evidence from Online Crowdfunding

This paper examines how (1) a crowdfunding campaign’s prosociality (the production of a public versus private good), (2) the social network structure (embeddedness) among individuals advocating for the campaign on social media, and (3) the volume of social media activity around a campaign jointly determine fundraising from the crowd. Integrating the emerging literature on social media and crowdfunding with the literature on social networks and public goods, we theorize that prosocially, public-oriented crowdfunding campaigns will benefit disproportionately from social media activity when advocates’ social media networks exhibit greater levels of embeddedness. Drawing on a panel dataset that combines campaign fundraising activity associated with more than 1,000 campaigns on Kickstarter with campaign-related social media activity on Twitter, we construct network-level measures of embeddedness between and amongst individuals initiating the latter, in terms of transitivity and topological overlap. We demonstrate that Twitter activity drives a disproportionate increase in fundraising for prosocially oriented crowdfunding campaigns when posting users’ networks exhibit greater embeddedness. We discuss the theoretical implications of our findings, highlighting how our work extends prior research on the role of embeddedness in peer influence by demonstrating the joint roles of message features and network structure in the peer influence process. Our work suggests that when a transmitter’s message is prosocial or cause-oriented, embeddedness will play a stronger role in determining influence. We also discuss the broader theoretical implications for the literatures on social media, crowdfunding, crowdsourcing, and private contributions to public goods. Finally, we highlight the practical implications for marketers, campaign organizers, and crowdfunding platform operators.
Performance Analysis of Dynamic Re-Clustering and Resource Allocation in Ultra Dense Network

Coordinated multi-point (CoMP) is a key technology to mitigate inter-cell interference, which can significantly improve the cell edge performance and system throughput. In addition, the choice of the cells within cluster will directly affect the effect of CoMP, and especially, it may cause the increase of the number of dissatisfied users in the system. To minimize dissatisfied users in the ultra-dense network (UDN) application scenario, based on the control-data separation architecture model, a clustering algorithm is studied to improve the SINR and throughout. Then, load balancing is developed and the overall percentage of unsatisfied users are reduced obviously. Furthermore, a dynamic resource allocation scheme is proposed by defining a factor $\alpha $ and applied to optimize the performance of system, which is compared with load balancing under the conditions of a different clustered size and density of users. The experiments prove that the performance of load balancing is more sensitive to the change of clustered size than resource allocation, and the load balancing has overwhelming advantage compared with resource allocation in the dense deployment scenario. However, when the density of users becomes sparser, resource allocation performs better, which gives important meanings for future UDN.
Access Characteristic Guided Read and Write Regulation on Flash Based Storage Systems

NAND flash memory is now used in various storage systems, such as embedded systems, personal computers, and web servers. The developments in bit density and technology scaling have reduced its price, but worsen the reliability, leading to shortened lifetime and degraded access performance. This paper proposes to exploit access characteristics of workloads to improve flash performance and lifetime. The basic idea is to regulate the read and write operations based on the identified access characteristics. First, an access cost model is presented, which indicates a tradeoff between read and write time cost on NAND flash memory. Based on the access characteristics of workloads, read-only pages will be written with high cost so that they can be read with low cost, and write-only pages will be written with low cost. Second, the tradeoff between read cost and flash wearing is exploited for lifetime improvement. The write requests on write-only data are processed with reduced wearing by regulating the programm threshold voltage. Finally, as these approaches apply different write operations on write-only data for performance and lifetime improvement respectively, a combined approach is proposed to satisfy both goals. Simulation results show that the proposed approaches can improve performance and lifetime significantly with negligible overhead.