Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Framework of Third Order Time and Frequency Domain Analysis for Neural Spike Trains

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.
Efficiency Optimization of Wirelss Power Transfer System with Traction Motor Load for Modern Tram

Efficiency is an important index for high power wireless power transfer system. Combined with the characteristic that the normal traction demand of the modern tram can be satisfied when the input DC voltage of the traction inverter is in a certain range, an efficiency optimization method of WPT system with inductor/capacitor/capacitor (LCC) compensation structure used for the modern tram power supply is proposed by adjusting the phase shifting angle of the primary inverter according to the traction power. Without increasing the hardware circuit, the proposed method can maintain the transfer efficiency as high as possible under different operating conditions. Theoretical analysis and simulation verify the effectiveness of the proposed method.
An Online Time Warping based Map Matching for Vulnerable Road Users’ Safety

High penetration rate of Smartphones and their increased capabilities to sense, compute, store and communicate have made the devices vital components of intelligent transportation systems. However, their GPS positions accuracy remains insufficient for a lot of location-based applications especially traffic safety ones. In this paper, we developed a new algorithm which is able to improve smartphones GPS accuracy for vulnerable road users’ traffic safety. It is a two-stage algorithm: in the first stage GPS readings obtained from smartphones are passed through Kalman filter to smooth deviated reading. Then an adaptive online time warping based map matching is applied to map the improved new locations to corresponding road segments. The incremental alignment is made in real-time based on two similarity metrics - distance and direction difference. Different online time warping variants are formulated and compared with the naïve dynamic time warping algorithm in terms of accuracy and response time. We tested them on GPS trajectories collected from smartphones and reference points extracted from road network data. Test results show that while the proposed algorithms have comparable accuracy with existing algorithms, they largely outperform in terms of response time. For the dataset used, average ratios of correct matches are 91.4% and 93.2% for newly proposed algorithms and for existing algorithm respectively. Negligible accuracy inferiority of the new algorithms is compensated by large improvement on response times which are meliorated from seconds to instant responses.
An adaptive communication framework for medical wireless body area networks

Medical wireless body area networks (WBANs) will play a crucial role in future smart healthcare systems. Appropriate adaptive communication techniques are needed to support the development and deployment of medical WBANs. In this paper, an adaptive scenario-based communication framework is introduced to support medical WBANs. By dividing the whole working environment into independent scenarios, the adaptive communication problem is divided to be two subproblems, i.e., how to communicate adaptively in different scenarios and how to roam from one scenario to another seamlessly. The structure of the proposed framework and how to implement the framework are discussed.
Use of Polyamidoamine Dendrimers in Brain Diseases

Polyamidoamine (PAMAM) dendrimers are one of the smallest and most precise nanomolecules available today, which have promising applications for the treatment of brain diseases. Each aspect of the dendrimer (core, size or generation, size of cavities, and surface functional groups) can be precisely modulated to yield a variety of nanocarriers for delivery of drugs and genes to brain cells in vitro or in vivo. Two of the most important criteria to consider when using PAMAM dendrimers for neuroscience applications is their safety profile and their potential to be prepared in a reproducible manner. Based on these criteria, features of PAMAM dendrimers are described to help the neuroscience researcher to judiciously choose the right type of dendrimer and the appropriate method for loading the drug to form a safe and effective delivery system to the brain.
Adaptive Remaining Useful Life Prediction Algorithm for Bearings

It is well known that the lifetime of bearings varies a lot even if the bearings are manufactured in the same assembly line. This is because the exposed operating conditions may be very different for each bearing. As a result, an adaptive remaining lifetime prediction algorithm is a more natural choice and is expected to be more accurate than the non-adaptive counterparts. This paper presents a new adaptive approach to predicting remaining useful life (RUL) of bearings. This approach improves an earlier method developed by us. In our earlier approach, it was observed that the prediction estimates depend on the initial conditions of the adaptive estimator. In the new approach, a bank of predictors is run in parallel and their predictions are constantly evaluated. Only the good estimators that meet the criterions are used for RUL output. Evaluations using actual bearing data demonstrated the efficacy of the proposed algorithm.
Dynamic Modelling and Simulation of a Deployable Quadrotor

This paper for the first time presents a deployable quadrotor based on a deployable hexahedral mechanism. Structure design of the proposed deployable quadrotor is introduced and mobility and kinematics of the associated deployable hexahedral mechanism are briefly addressed. Then kinematics and dynamics of the proposed deployable quadrotor are established and the results are verified through numerical simulations, leading to the demonstration of performance and manoeuvrability of the proposed quadrotor. This paper hence also provides insight into the integration of deployable mechanisms into the development of quadrotors for wider applications.
A Detection Mechanism for Internal Attacks on Pull-Based P2P Streaming Systems

Online streaming is a popular service for data-intensive applications such as video streaming. P2P-based streaming solutions are advocated to help reduce costs for both providers and users. Yet, involving users over data dissemination entails security risks including a variety of denial-of-service attacks. While extensive research exists on mitigating varied attack types, their effectiveness is limited if the attacker can infer information about the topology such as the identity of nodes that have direct connections to the source. The attacker can then leverage the gained insights to place malicious participants in prominent positions. By dropping chunks that should be forwarded, the malicious peers degrade the performance in a stealthy way that does not raise suspicion. We first demonstrate the feasibility of conducting such attacks. Accordingly, we propose a detection mechanism that identifies the attack and removes potential malicious peers from their disruptive positions. We ascertain, theoretically and through simulations, that malicious peers cannot misuse the detection mechanism to gain influence. Our simulation-based study indicates that the proposed detection mechanism is able to detect malicious peers with up to 80–90% accuracy while inducing a small overhead of approximately 8%.
Hardware-Aware Moving Objects Detection in Satellite Image

This paper proposes an effective way to detect the moving objects in the satellite image. Since the moving objects cannot determine first in the satellite, the corner detection algorithm is applied to obtain the feature of objects. Next, the optical flow algorithm is used to estimate motion vector of objects. In order to speed up the computation and reduce the resource of the hardware, Kalman filter is utilized after the optical flow results. According to the result, objects motion can be detected effectively.
Online Multi-Object Tracking via Combining Discriminative Correlation Filters With Making Decision

Multiple-object tracking (MOT) has received an increasing attention due to the rapid development of autonomous driving. However, the MOT problem is still challenging mainly due to the occlusion and scale variation. Motivated by the fact that the discriminative correlation filters-based (DCFB) tracking algorithms can tackle these problems and significantly improve the accuracy of single object tracking, how to exploit the DCFB tracking algorithms for MOT is worthy studying. Moreover, the corrupted training samples due to the occlusion make DCFB tracking methods to update the appearance model of target uncorrected and result in tracking drift. In this paper, we exploit Markov decision process to integrate the DCFB tracking method into our MOT framework and address the update problem of the appearance model in DCFB tracking method. Moreover, in order to overcome the challenges of occlusion and scale variation, to prevent target drift during tracking, we use two DCFB trackers with different update frequencies and a novel update strategy to predict the location of targets. The part-based method is used to extract robust features to tackling the challenges of occlusion and scale change. In order to verify the efficiency of our algorithm, experiments are performed based on KITTI tracking benchmark. The results demonstrate that our method achieves state-of-the-art performance and outperforms the state-of-the-art algorithms in road scenarios.
Stream Processing: The Matrix Revolutions

Analyzing data streams is a vital task in data science. Often, data comes in different shapes such as triples, tuples, relations, or matrices. Traditional stream processing systems, however, only process data in one of these formats. To enable the processing of streams combining different shapes of data, we developed a system that parses SPARQL queries using the Apache Jena parser and transforms them to Apache Flink topologies. With a custom data type and tailored functions, we enabled the integration of matrices in Jena and therefore allowed to mix graphs, relational, and linear algebra in an RDF graph. This provided a proof of concept that queries may be written for static data and – with the usage of the streaming engine Flink – can easily be run on data streams, even if they contain multiple of the aforementioned types.
Predictive Value of Schistocytes in Recurrence of Acquired Thrombotic Thrombocytopenic Purpura With Severe ADAMTS13 Deficiency at Discontinuation of Daily Therapeutic Plasma Exchange

Acquired thrombotic thrombocytopenic purpura (TTP) is characterized by microangiopathic hemolytic anemia, thrombocytopenia, and commonly ADAMTS13 deficiency. Patients with TTP and severe ADAMTS13 deficiency have high risk of disease recurrence, yet the ability to predict which patients will have recurrence remains limited. We assessed whether the presence of persistent schistocytes in TTP patients with severe ADAMTS13 deficiency at the time of daily therapeutic plasma exchange (TPE) discontinuation was predictive of disease recurrence. We retrospectively reviewed the electronic medical records of all patients with a diagnosis of TTP treated with TPE at our university medical center between August 1991 and April 2013. Exacerbation was defined as disease recurrence within 30 days of cessation of daily TPE, and relapse was defined as disease recurrence >30 days after cessation of daily TPE. Comparisons were performed with a two‐sided Fisher's exact test or χ2 test. Of the 46 total TTP patients eligible for analysis, nine had residual schistocytosis (20%), four of the nine (44%) had exacerbations, and two of the nine (22%) relapsed. Of the 37 patients without residual schistocytosis, 16 (43%) had exacerbations and 11 (30%) relapsed. There were no statistically significant differences in the exacerbation or relapse rates with or without residual schistocytosis (P = 1.00 and 1.00, respectively). Residual schistocytes after discontinuation of daily TPE were not uncommon. The persistence of schistocytes had poor sensitivity, specificity, and both positive and negative predictive values. After the initial diagnosis of TTP is made, there is no reason to continue documenting the presence or absence of schistocytes.
Video Transmission Artifacts Detection Using No-Reference Approach

In real-time (RT) applications that include transmission of digital video, different artifacts (caused by compression and transmission processes) can appear in video received at the end-user side. In order to ensure high level of end-user Quality of Experience (QoE), video application/service providers have to continuously measure and monitor the quality of perceived video. Since in RT video applications uncompressed video is unavailable at the receiver side, artifacts detection as well as video quality assessment (VQA) are often performed using no-reference (NR) approach. In this paper we present a novel NR algorithm that efficiently detects packet loss (PL) artifacts in received video frames, called Packet Loss Detection Algorithm (PLDA). The proposed PLDA operates only on pixel values of the processed video frame and it requires no additional information about processed video. The performance of the proposed PLDA is compared to that of other existing PL detection algorithm on video sequences of significantly different content, in which distinct error concealment methods are used to conceal errors caused by PL. The results show that PLDA outperforms other tested algorithm when detecting PL artifacts in network transmitted video and that it is very robust in terms of different content types and error concealment methods. Additionally, proposed PLDA is capable of processing up to 25 frames per second (FPS) of Full HD video in RT and thus it is suitable for usage in RT video transmitting applications.
A Demonstration of ShareVR: Co-Located Experiences for Virtual Reality Between HMD and Non-HMD Users

Most current virtual reality (VR) head-mounted displays (HMD) create a highly immersive experience and are currently becoming part of the living room entertainment (e.g. PSVR). However, current VR systems focus mainly on increasing the immersion and enjoyment for the user wearing the HMD (HMD user). This results in all the bystanders (Non-HMD users) in the living room being excluded from the experience and degraded to mainly observing the HMD user. In this demonstration we show ShareVR, a VR system using floor projection and mobile displays in combination with positional tracking to visualize the virtual world for the Non-HMD user, enabling them to interact with the HMD user and become part of the VR experience. Additionally, we implemented several experiences for the asymmetric nature of ShareVR, exploring its design space.
Combiner to Reduce the Time of Processing in Trend Analysis Using Hadoop's MapReduce Framework

Trend means to be one of the words, subjects, or names that is being mentioned most often on a social media website or a news website at a particular time or for a duration of time. It also means that a particular behaviour or method change done or being undertaken or followed by people may be individual or as a group. Taking the advantage of the fast phased processing technology helps in analysing the trends in the current times. Apache Hadoop frame work provides Mapper and Reducer which helps to mine the large amount of data and to help the business to plan their marketing tactics based on the trend. Further, using concept of Combiner, which is an important feature of Hadoop framework, immensely reduces the processing time and gives the mining results in less time compared to the one without Combiner, which reduces the entire processing time in bigger difference as the file size increases. The main motivation behind this research is that need of improved processing time of larger files, utilizing the Hadoop's available features.
Gaussian Process Quadrature Moment Transform

Computation of moments of transformed random variables is a problem appearing in many engineering applications. The current methods for moment transformation are mostly based on the classical quadrature rules, which cannot account for the approximation errors. Our aim is to design a method for moment transformation of Gaussian random variables, which accounts for the error in the numerically computed mean. We employ an instance of Bayesian quadrature, called Gaussian process quadrature (GPQ), which allows us to treat the integral itself as a random variable, where the integral variance informs us about the incurred integration error. Experiments on the coordinate transformation and nonlinear filtering examples show that the proposed GPQ moment transform performs better than the classical transforms.
Multi-focus image fusion for extended depth of field

In this paper, we address the extended depth of field method, which appropriately applicable to a set of stack images. The proposed fusion algorithm improved a conventional discrete wavelet transform using spatial frequency and sum-modified-Laplacian. Both methods are applied to the approximation coefficient and the detail coefficients (horizontal, vertical and diagonal) for calculating in-focused regions. After that, the sharp areas of each stack images are combined with the fusion image process. Finally, the inverse wavelet transform is utilized to obtain a final result image. The performance of the proposed method is conducted and compared with conventional fusion methods. Experiment results can demonstrate that the proposed method outperforms other reference methods.
Eddy Current Loss Analysis of Underwater Wireless Power Transfer System

Underwater Wireless Power Transfer (WPT) has attracted much attention in recent years. In an underwater WPT system, the eddy current loss tends to be non-negligible as the frequency and current increase. Thus, it is crucial to analyze the eddy current loss in an underwater WPT system. The analytical model of the eddy current loss of a coreless WPT system in seawater is established with Maxwell’s equations. The expressions of the electric field intensity and the eddy current loss are derived and the eddy current loss is analyzed in different circumstances. An underwater WPT prototype is set up and the experimental results verify the theoretical analysis.
New Theory for Unmixing ILL-Conditioned Hyperspectral Mixtures

Hyperspectral unmixing (HU), a blind source separation problem, aims at unambiguiously identifying the spectral signatures of the materials, as well as their abundances, from the measured hyperspectral mixtures. In real hyperspectral scenes, high correlation between the spectral signatures is commonly observed, making HU quite challenging. Although such ill-conditioning is critical for effective HU, it is often ignored in existing HU literature. To the best of our knowledge, existing preconditioning techniques, for reducing the condition number of the signature matrix, were developed based on the pure-pixel assumption, which can, however, be seriously violated in remote sensing. Under a relaxed purity assumption, with respect to the pure-pixel one, this paper proposes novel theory for unmixing ill-conditioned hyperspectral mixtures. Specifically, we exactly identify the John's ellipsoid (i.e., the maximum ellipsoid inscribed in the convex hull of the hyperspectral data vectors) via split augmented Lagrangian shrinkage algorithm (SALSA), and transform this ellipsoid into an Euclidean ball. This transformation brings the data vectors into a new space wherein the corresponding material signature vectors form a regular simplex, which is a very strong prior information. Based on this prior, we design an HU criterion, and prove its perfect identifiability under a very mild sufficient condition. Then, we demonstrate the feasibility of realizing our criterion via non-convex optimization and guarantee a stationary point solution.
CAMP@FLASH: an end-station for imaging, electron- and ion-spectroscopy, and pump–probe experiments at the FLASH free-electron laser

Beamline BL1 at the FLASH free-electron laser facility at DESY was upgraded with new transport and focusing optics for the installation of the new permanent CAMP end-station, a multi-purpose instrument optimized for electron- and ion-spectroscopy, imaging and pump–probe experiments. An overview of the layout, beam transport, focusing capabilities, and experimental possibilities of this new end-station, as well as results from its commissioning and first experiments, are presented.
Heuristic Placement of PMU in Power System State Estimation

This paper implements heuristic PMU placement (HPP) of Phasor measurement units (PMU's) in power system state estimation. PMU meters are expensive when compared to traditional Supervisory Control and Data Acquisition (SCADA) meters. Due to such economic constraint, power companies placed PMUs in gradual phases in power system. Efficient placement is required in order to allocate the minimum number of PMU's to improve estimation accuracy. Weighted least absolute value using iteratively reweighted least square (LAVIRLS) algorithm is used in this paper. The proposed algorithm is used to verify the efficient PMU placement on IEEE 14 test system.
A Comparative Study of the Learning Outcomes and Experience of VR in Education

Virtual Reality (VR) is believed to have a pivotal role in transforming teaching and learning in higher education. The novelty factor and full immersion in a virtual environment can undoubtedly improve students' attention. However, it is still unclear how the use of VR would impact the learning experience and outcomes with respect to knowledge acquisition. We conducted a comparative study on students' performance in a standardised assessment when course content is delivered using VR and conventional lecture slides. Results show that improvement in social interaction and productivity tools in VR are essential for its greater impact in higher education.