Miracle-dz/UniFAR-FTUs huggingface dataset:
Spike sorting using Superlets: Evaluation of a novel feature space for the discrimination of neuronal spikes

Because of the intricacy of neural data, spike sorting is a challenging problem in neuroscience. Overlapping clusters are one of the hardest issues to solve, along with the similarity of spike shapes, excessive noise, and unbalanced clusters. Here, we focus on analyzing the efficacy and integrating the Superlets Transform as a feature extraction method. Our results indicate that Superlets achieve not only super-resolution in time and frequency, but also provide the conditions for smooth clustering performance. The Superlet Transform was analyzed in conjunction with Principal Component Analysis, Singular Value Decomposition, and Isomap Embedding as dimensionality reduction methods. The Superlet-extracted features were classified using Neural Networks in order to assess their relevance.
3d Self-Aligned Fabrication of Suspended Nanowires by Crystallographic Nanolithography

Known templating procedures mostly create out-of-plane nanowires where individual connections at both ends are complicated. Here we introduce a templating procedure for wafer scale fabrication of in-plane nanowires. The template fabrication process employs two simple interference lithography masking patterns and relies on self-aligned crystallographic processing. In-plane nanowires with diameters down to 10 nm can be fabricated wafer scale through this 3D templating procedure. As a first demonstration arrays of suspended silicon nitride wires have been created.
Novel Wavefront-Splitting Interferometer for Ultra-Compact Broadband FT-IR Spectroscopy Extending to Visible Range

While MEMS interferometers reported so far rely on amplitude-splitting, this paper reports the first wavefront-splitting MEMS interferometer. The interferometer is ultra-compact and is part of a Fourier transform (FT) spectrometer fabricated using Deep Reactive Ion Etching (DRIE) of Silicon-on-Insulator (SOI). It enables the extension of the spectral range from Infrared to the visible range as validated from the experiments. The proposed interferometer architecture is based on spatial splitting and combination of optical beams using wavefront-splitting, where free-space propagation is only through air.
A microfluidic system, utilising image processing methods, for the detection of blood coagulation and erythrocyte aggregation

Hemostasis is a defence mechanism that prevents blood losses in cases of vessel injuries, and other related disorders. In many cases, patients need to frequently monitor their blood coagulation tendency in order to regulate their medication. In addition, red blood cell aggregation (RBCA) is related to blood inflammation, and it appears elevated in many pathological conditions. Blood coagulation and RBCA can be studied by analysing the dynamic changes of light transmittance though a clotting/aggregating sample, and indeed various works in the literature exploit this approach. In this work, blood coagulation and RBCA are examined by utilising single drops of blood in an inexpensive camera-based microfluidic system, designed for low computational and production cost. Results are compared with a microscopy-camera system, with both setups utilizing the same custom made microchannel. Three image processing algorithms are developed to analyze the averaged light intensity, and the local structural chracteristics of blood, through a binarization and region classification method, using logical operations. The results illustrate the repeatability of the technique and the donor-to-donor variation within the proposed approach. Based on the image processing analysis, the developed coagulation and aggregation indices show great potential of utilisation in an inexpensive and robust point of care device.
RailSet: A Unique Dataset for Railway Anomaly Detection

Understanding the driving environment is one of the key factors in achieving an autonomous vehicle. In particular, the detection of anomalies in the traffic lane is a high priority scenario, as it directly involves vehicle's safety. Recent state of the art image processing techniques for anomaly detection are all based on deep learning of neural networks. These algorithms require a considerable amount of annotated data for training and test purposes. While many datasets exist in the field of autonomous road vehicles, such datasets are extremely rare in the railway domain. In this work, we present a new innovative dataset relevant for railway anomaly detection called RailSet. It consists of 6600 high-quality manually annotated images containing normal situations and 1100 images of railway defects such as hole anomaly and rails discontinuity. Due to the lack of anomaly samples in public images and difficulties to create anomalies in the railway environment, we generate artificially images of abnormal scenes, using a deep learning algorithm named StyleMapGAN. This dataset is created as a contribution to the development of autonomous trains able to perceive tracks damage in front of the train. The dataset is available at this link.
A Review of Convolutional Neural Network-based Approaches for Disease Detection in Plants

Around 60.3% of land in India is used for agricultural purposes and the whole population depends on agriculture. That’s why crop yield is very crucial to get high agricultural output. The economical loss will be very high if the agricultural output is low. So, that’s why the diagnosis of disease in plants is very important. And the detection should be in the early stage not in a later stage. Using Deep Learning (DL) i.e. a branch of Artificial Intelligence (AI), a farmer can detect plant diseases very easily. In Deep Learning(DL), Convolutional Neural Networks (CNNs) are a cutting-edge method for image classification tasks. And Plant Disease Detection is an image classification task in which image is given as input and a class of plant disease is obtained as an output. This research study reviews the CNN-based approaches that are used to detect various diseases in plants.
Cryptocurrency Stock Prediction using Deep Learning

Cryptocurrencies are a sort of digital currency in which all transactions are carried out through the internet. It is a soft currency that does not exist in hard cash form. We emphasize the difference between a decentralized currency and a centralized currency in that all virtual currency users can acquire services without the intervention of a third party. Using these cryptocurrencies, however, has an influence on international relations and trade because to their severe price volatility. Furthermore, the rapid variations in cryptocurrency prices indicate that a reliable method for estimating this price is urgently required. To anticipate a company’s stock price/ cryptocurrency based on past prices, the methods namely, Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) deep learning architectures are employed. The proposed method is written in Python and tested on benchmark datasets. The results show that the proposed method can be used to make reliable predictions.
How to Evaluate Success Startups: Case of FinTech and Cybersecurity in the GCC Venture Capital Market

FinTech investment will reach hundreds of billion dollars by 2022. Gulf Cooperation Council (GCC) venture capital (VC) firms also invest in FinTech. Bahrain, as a government initiative, established Bahrain FinTech Bay to attract FinTech startups from all over the world. VC investments in FinTech and Cybersecurity startups accounted for more than half of the total in 2018. The area of cybersecurity and financial payments is one of the most significant aspects of FinTech. We seek to answer the following question: What are the key success factors for startups? In this paper, a literature review is conducted in order to identify the main success factors. Research models have been proposed for future research.
Reconfigurable Intelligent Surface Aided DFRC Vehicular Networks

Reconfigurable Intelligent Surface (RIS) has been considered as a promising technology for 6G networks, which is capable of directionally reflecting the incident signal by adjusting phase shift of its reflecting elements, and thus to achieve beamforming. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) vehicular networks. Considering a vehicle-to-infrastructure (V2I) communication in non-line-of-sight(N-LoS) scenario, where RIS is deployed to establish sensing and communication links, we propose a scheme to optimize the beamforming based on the detected vehicle position for the sake of avoiding the relatively large channel estimation overhead. More specifically, we build a codebook to maintain the optimal phase shift of RIS corresponding to each candidate angle offline, which can be used for detecting the direction of vehicle in N-LoS scenario. In addition, the position-based channel state information (CSI) is introduced and used for maximize the downlink rate. Simulation results show that by deploying RIS in DFRC vehicular networks, the base station can detect the vehicle position in N-LoS scenario and provide it with reliable communication rates without channel estimation.
Real-Time Operation of Microcantilever-Based in-Plane Resonators Partially Immersed in a Microfluidic Sampler

This paper reports a real-time liquid-phase quantitative analysis system, where in-plane flexural piezoresistive Si cantilever resonators are utilized as microsensors. The new-developed system comprises a 3D-printed sampler, which enables a controllable setting of the immersion depth of the thermally self-actuated cantilevers of rectangular and triangular shapes into a microfluidic volume. By electromechanical amplitude modulation (EAM) of the supply voltage of the piezoresistive strain gauge, parasitic actuator-detector coupling effects were removed from the read-out signal enabling real-time tracking of resonant-frequency (rf) shifts using a phase-locked loop (PLL) circuit. High quality factors (Q: ~100-680) and low rf reduction (max. ~2.5%) with respect to operation in air were observed with cantilevers triangular-tip immersed into DI water. A real-time limit of detection (LOD) of ~1.0 ng in liquid and quasi-real-time LOD of ~150 pg have been obtained.
Testing Goodness of Fit for Parametric Families of Copulas—Application to Financial Data

ABSTRACT This article suggests a chi-square test of fit for parametric families of bivariate copulas. The marginal distribution functions are assumed to be unknown and are estimated by their empirical counterparts. Therefore, the standard asymptotic theory of the test is not applicable, but we derive a rule for the determination of the appropriate degrees of freedom in the asymptotic chi-square distribution. The behavior of the test under H 0 and for selected alternatives is investigated by Monte Carlo simulation. The test is applied to investigate the dependence structure of daily German asset returns. It turns out that the Gauss copula is inappropriate to describe the dependencies in the data. A t ν-copula with low degrees of freedom performs better.
CyberHelp: Sentiment Analysis on Social Media Data Using Deep Belief Network to Predict Suicidal Ideation of Students

Suicide is a very critical and important issue in modern society. Suicide is the third-leading cause of death for college and high school students. Social media allows students in the digital environment to share their suicidal ideas and thoughts with others. Accurate and early detection and prevention of suicidal ideation in students can save the students' lives. To identify the risk factor for suicidal attempts, a suitable method of analysing the suicidal behaviour of students using their sentiment text posted on social media can be used. This paper presents an optimized Dragonfly algorithm (DFA) using a Deep Belief Network (DBN) for the automatic detection of suicidal ideation in students. In our CyberHelp Solution, the proposed DFA-based DBN model analyses student social media data, predicts suicidal behavior, and treats students appropriately. The sentiment analysis performs automated categorization of online messages and makes accurate predictions of the student’s suicidal behaviors. The dragonfly heuristic optimization algorithm is used for tuning the hyperparameter in the deep belief network. The proposed DFA-DBN technique has been implemented to predict suicidal ideation in students with a higher accuracy of 95.5% compared with other classification models.
Asymmetric Quad Leg Orthoplanar Spring for Wideband Piezoelectric Micro Energy Harvesting

Piezoelectric energy harvesters (EH) generate their highest energy levels at the resonant frequencies of the transducer devices. To provide a wide-band EH solution, nonlinear mechanical resonators with multiple resonant modes can be used. We present a new asymmetric quad-leg orthoplanar spring (QOPS) EH microstructure to increase the harvesting bandwidth. The proposed design is implemented in CMOS compatible microfabrication processes. Finite element analysis show that the asymmetric designs increases the bandwidth by maximum 27% compared with symmetric designs. In order to measure the electrical output, one symmetric and three asymmetric piezoelectric devices implemented on the same microchip are exposed to mechanical vibrations over a wide frequency bandwidth. Experimental results approve the increase in the frequency bandwidth of resonators introduced by asymmetries added to the spring, as compared to the symmetrical configuration.
Automotive Mixed Criticality DAG Function Scheduling Optimization Based on Edge Computing

As the high level autonomous vehicle has come to be regarded as the typical mixed-criticality cyber-physical system, the optimization approach of job scheduling has drawn more and more attention. When the conventional mixed-criticality theory is used to handle the scheduling problem, the low criticality functions are frequently degraded or abandoned at high system criticality levels, decreasing service satisfaction. This paper proposes an optimization method using edge computing to improve the performance of low criticality functions on the presumption that high criticality functions can meet the deadline requirements. The optimization method is based on the scenario of the future prospect of intelligent transportation and the new electronic/electrical information architecture of network connection. This research also provides some mixed-criticality function models to validate our methods. Weighted completion index is proposed to measure the scheduling effect of this situation, which also quantifies the level of improvement of edge computing-based scheduling over the conventional local scheduling method, in order to address the lack of evaluation of passengers' perception when vehicle soft real-time DAG functions are unable to meet the deadline.
Submillimeter-Wave Spectroscopy of HNC Produced in an Extended Negative Glow Discharge

Abstract Submillimeter-wave transitions of HNC are observed in an extended negative glow discharge. The measurements of the rotational lines in the ground state, as well as in the vibrational fundamentals, are extended to higher J values (up to J=9←8 in the 830-GHz region). The lines in the higher vibrational excited states ν2+ν3, 2ν2, and 2ν2+ν3 are also observed and assigned, using the frequency–magnetic field double modulation technique. Dramatic enhancement of the signal intensity strongly suggests that HNC is predominantly produced in the extended negative glow discharge through the dissociative recombination of HCNH+ with electrons.
A MEMS-Based Gravimeter for Simultaneous Vertical and Horizontal Earth Tides Measurements

This paper reports a MEMS gravimeter enabling simultaneous gravity measurements in both horizontal and vertical directions by combining two MEMS gravity sensors with an identical low-frequency linear spring structure. The proposed paired MEMS gravity sensors both experimentally demonstrate ultra-low self-noise floors of < 2 μGal/√Hz@1 Hz. The assembled gravimeter successfully measures long-term Earth tides signals with a high sensitivity and stability, showing the potential for three-axis gravity measurements.
Analysis of Existing ML Techniques for Students Success Prediction

Providing good academic environment, infrastructure and various learning facilities is not sufficient for Higher Educational Institutions in this competitive era. HIEs have to adopt more trending technologies that can help students to achieve best professional growth. Students’ performance prediction in advance using Supervised and Unsupervised Machine Learning techniques is very much trending for learning contexts in HIEs as it helps administrators to design strategies for improving final results. This is possible as high-volume data about student’s personal, academic performance at school level and at the admission time is available in school, college and universities. Historical data can be utilized to analyze various learning capabilities of different students in different environment and that analysis can be used to predict performance of a newly admitted student. This research mentions an analysis of existing ML techniques in higher education for prediction of students’ success based on previous work done in the related area. We have studied a total of 40 relevant papers in which ML techniques are being proposed or implemented for students’ success prediction. A systematic analysis was done to synthesize and report the main results.
Towards A Strategy for Developing a Project Partner Recommendation System for University Course Projects

Project Partner preferences, and the breakdown of team projects has been a challenge both in industry, and university settings. While the consequence is severe in industrial settings, the loss of time, and invested effort in scenarios where university course project breaks down leads to distrust among members and eventual failure in some cases. Thus, it becomes necessary, the acquisition of Colaboratory skills which according to literature is acquired via collaboration with partners whose behaviors cancel out each other’s eccentricities. To this end, industry stakeholders have invested enormous resources on the development of user predictive models that optimally predict the outcome of a collaborative engagement. While this strategy is effective, government policies restrict its implementation in institutions of higher learning, thus making collaboration modelling challenging. Ergo, the objective of this endeavor investigation of noninvasive strategies for eliciting individual preferences that affect collaboration and the development of study recommendation partner system. Consequent on the literature review, we have employed a big five-oriented questionnaire input which is passed through a personality based similarity system based on collaborative filtering and utility-based recommendation system. Findings show that generated teams are academically balance which is the main objective of the study recommendation partner system.
Estimation of accuracy in localizing externally attached markers in multimodal volume head images

We recently examined the registration of multimodal volume head images using extrinsic markers. We analyzed the problem into the three separate problems of finding the positions of at least three non-collinear markers in the two images, matching corresponding markers in the two image spaces, and estimating the translation and rotation parameters of the rigid body transformation that maps one space into the other. We call the calculation of the geometric centers fiducial localization. Knowing the fiducial localization error is important since it is an important determinant of target registration error, and because it provides a useful way to compare localization algorithms. In the present work we present a technique for experimentally estimating the error associated with localizing a fiducial in a tomographic image. Our method involves acquiring a volume image of a large number of markers with precisely known positions in the physical space of a phantom containing the markers, localizing the fiducials in the image, and registering the localized fiducial positions in image space to the precisely known positions in physical space. Fiducial localization error is estimated from the fiducial registration error, i.e. the distance between corresponding fiducial positions after registration.
High Performance Scalable Recursive Block Matrix Inverse for Multicore Architectures

The inversion of a matrix, which is computationally intensive and involves numerous operations, is extensively used in various scientific and engineering applications. The block matrix inverse formula for n x n matrices requires the inverse of two half-sized matrices and six multiplications between two half-sized matrices. In this work, we proposed high performance scalable recursive block matrix inverse algorithm for multicore architectures based on block matrix inverse that performs recursively and transforms two half-sized matrices into a minimal 2 x 2 matrix inverse. We performed extensive experimental performance evaluation and analysis of recursive block matrix inverse on shared memory multicore system and achieved scalability and throughput (Double Precision GFLOPS) for large matrix inversion. We compared recursive block matrix inverse with MKL LAPACK and found it to be far superior. We proposed a combination of recursive block matrix inverse and MKL LAPACK matrix multiplication routines for multicore architectures, which outperforms MKL LAPACK matrix inverse.
Sliding Mode Control of Energy Storage Systems for Reshaping the Accelerating Power of Synchronous Generators

The rotor angle stability has been a key concern in the stable operation of power systems, especially in the renewable energy age. However, to improve the power system rotor angle stability, none of the existing methods considered achieving it through reshaping the accelerating power of synchronous generators (SGs) and decoupling them after grid disturbances. In this paper, the sliding mode control (SMC) theory is utilized to tackle the coupling among SGs. After careful modeling and controller design, a SMC-based energy storage system (ESS) controller is proposed to temporarily decouple the interacted SGs through reshaping the accelerating power of SGs to the desired value. The proposed controller can: 1) drive the SG’s accelerating power to follow the reference in infinite time; 2) decouple the interacted SG dynamics and stabilize the disturbed SG exponentially. The proposed controller achieves the above two goals without using the communication and hard-to-estimate SG rotor angles. Case studies are carried out on the 2-Area 4-Generator System and the Australian 14-Generator Equivalent System to demonstrate the effectiveness of the proposed controller. The results also show that the proposed controller stabilizes the disturbed power systems with better performance over existing controllers.
Leveraging Deep Learning to Spot Communities for Influence Maximization in Social Networks

Groups play a crucial role in affecting decisions of individuals who are part of the group. When it comes to social networks the group here may be small with some 10-15 members or very big contacting more than 100 members. Thus, there is high possibility of individuals belonging to one or more groups in social networks. It thus becomes important to activate influential members of a group to ensure maximum information propagation. This work proposes a community-based seed selection algorithm. The communities are first identified node embedding which performs graph clustering. After which proportionate distribution of seed nodes is carried out to ensure fair selection. Mapping node features to lower dimensional space and similar nodes getting placed closer to each other proves a better technique for community detection and is also expandable if new nodes get introduced in the network.