Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ultra Low Power Personalizable Wrist Worn ECG Monitor Integrated With IoT Infrastructure

Cardiovascular diseases are the leading cause of death in the U.K., motivating the use of long term wearable devices to monitor the heart in out-of-the-clinic settings. While a wide number of heart rate measuring wearable devices are now available, they are principally based upon photoplethysmography rather than the electrocardiogram (ECG) and are stand-alone devices rather than integrated with Internet-of-Things infrastructures which collect and combine information from a wide range of sensors. This paper presents a wrist worn ECG sensor which integrates with the SPHERE IoT platform—the UK’s demonstrator platform for health monitoring in the home environment, combining a range of on-person and ambient sensors. The ECG device integrates ultralow power consumption electronics with personalizable 3-D printed casings which maintain gold standard Ag/AgCl electrodes to provide measurements of the raw ECG waveform, heart rate, and meanNN and SDNN heart rate variability parameters. The end device allows for more than a month of battery life for a weight of <50 g including the watch straps. The design and heart sensing performance of the device are presented in detail, together with the integration with the SPHERE IoT platform.
CRISPR Gene Perturbations Provide Insights for Improving Bacterial Biofuel Tolerance

Economically-viable biofuel production is often limited by low levels of microbial tolerance to high biofuel concentrations. Here we demonstrate the first application of deactivated CRISPR perturbations of gene expression to improve Escherichia coli biofuel tolerance. We construct a library of 31 unique CRISPR inhibitions and activations of gene expression in E. coli and explore their impacts on growth during 10 days of exposure to n-butanol and n-hexane. We show that perturbation of metabolism and membrane-related genes induces the greatest impacts on growth in n-butanol, as does perturbation of redox-related genes in n-hexanes. We identify uncharacterized genes yjjZ and yehS with strong potential for improving tolerance to both biofuels. Perturbations demonstrated significant temporal dependencies, suggesting that rationally designing time-sensitive gene circuits can optimize tolerance. We also introduce a sgRNA-specific hyper-mutator phenotype (~2,600-fold increase) into our perturbation strains using error-prone Pol1. We show that despite this change, strains exhibited similar growth phenotypes in n-butanol as before, demonstrating the robustness of CRISPR perturbations during prolonged use. Collectively, these results demonstrate the potential of CRISPR manipulation of gene expression for improving biofuel tolerance and provide constructive starting points for optimization of biofuel producing microorganisms.
Design and Experimental Validation of an LTE-Based Synchrophasor Network in a Medium Voltage Distribution Grid

We present and experimentally validate in a real-scale medium voltage (MV) grid a synchrophasor network that exploits the availability of a public 4G LTE communication infrastructure. An 18 buses, 10 kV feeder located in Huissen, The Netherlands, has been equipped with 10 Phasor Measurement Units (PMUs) connected to the MV grid by means of dedicated voltage and current sensors. The PMUs stream synchrophasor data through a public 4G LTE network via an information-centric networking-based middleware, named C-DAX. The measurements are received and time-aligned at a phasor data concentrator and fed to a real-time state estimation application. The paper presents the various field-trial components and validates the feasibility of exploiting the 4G LTE technology for PMU-based applications. Specifically we assess the performance of the adopted wireless telecommunication infrastructure with and without the C-DAX middleware, as well as the accuracy of the real-time state estimation process.
On the Collaborative Inference of DDoS: An Information-theoretic Distributed Approach

Literature contributions have shown that information theoretic techniques can effectively detect various types of Distributed Denial of Service (DDoS) attacks. However, such techniques are often centralized with a limited measurement vantage point and suffer from the issue of single point of failure. Furthermore, with the flourishing of distributed and cloudbased environments, such techniques ought to adapt to such settings for scalability and performance reasons. In this paper, we address the problem of collaborative DDoS detection using information-theoretic techniques. To this end, we propose an entropy-based detection mechanism that supports collaborative agreement to identify suitable tuning network parameters for distributed DDoS inference in real-time. Empirical evaluations with real DDoS attacks demonstrate that the proposed approach is indeed capable of cooperatively inferring DDoS attacks while achieving resiliency and scalability.
Domain Adaptation Tracker With Global and Local Searching

For the convolutional neural network (CNN)-based trackers, most of them locate the target only within a local area, which makes the trackers hard to recapture the target after drifting into the background. Besides, most state-of-the-art trackers spend a large amount of time on training the CNN-based classification networks online to adapt to the current domain. In this paper, to address the two problems, we propose a robust domain adaptation tracker based on the CNNs. The proposed tracker contains three CNNs: a local location network (LL-Net), a global location network (GL-Net), and a domain adaptation classification network (DA-Net). For the former problem, if we come to the conclusion that the tracker drifts into the background based on the output of the LL-Net, we will search for the target in a global area of the current frame based on the GL-Net. For the latter problem, we propose a CNN-based DA-Net with a domain adaptation (DA) layer. By pre-training the DA-Net offline, the DA-Net can adapt to the current domain by only updating the parameters of the DA layer in one training iteration when the online training is triggered, which makes the tracker run five times faster than MDNet with comparable tracking performance. The experimental results show that our tracker performs favorably against the state-of-the-art trackers on three popular benchmarks.
Core-set based product quantization for large-scale multimedia search

Approximate nearest neighbor (ANN) search has been well-studied and weightily applied in the field of large scale multimedia search. In recent years, Product Quantization (PQ) based methods have achieved greate success in ANN search. However, both the time and space complexity for PQ learning are linear to the data amount, making it infeasible for large-scale datasets. In this paper, we propose a more efficient learning strategy for product quantization. Instead of learning PQ on the whole dataset, we construct a small set of representative elements, from which PQ can be approximated learned with much lower computation overhead. Extensive experiments are conducted on three large-scale datasets. The codebook learning process of our approach can achieve up to tens times speed-up than existing PQ-based methods, while the memory consumption is only logarithmic to the number of data points. At the same time, the ANN search accuracy of our method is still comparable with state-of-the-art methods on all datasets.
Development and Evaluation of Dielectric Elastomer Actuators for Assistive Wearable Devices

Intensive rehabilitation after stroke contributes significantly to recovery from hand stiffness and loss of strength. Assistive wearable devices could be used by patients on a daily basis to assist with the rehabilitation process. Unfortunately, robotic devices are currently too large and heavy. To reduce the size and weight of these devices, novel means of actuation other than DC motors need to be considered. Dielectric elastomer actuators (DEAs) may provide a solution to the actuation problem. The goal of this paper was to evaluate DEAs as a possible solution to the actuation needs of an assistive wearable device for the wrist. DEAs were fabricated and tested to determine their capabilities in terms of force and range of motion. Although the size and weight of the DEA is ideal for wearable devices, the results show that a single DEA strip of reasonable dimensions is not capable of providing the force or range of motion required. However, there is potential for multiple DEAs to provide a viable solution and should be explored in the future.
The Improvement of Web Page Ranking on SERPs

We can easily and quickly use search engines to find what we want. However, search engines tend to place ads or irrelevant web pages in obvious web pages based on business considerations. This result causes the user really need the information is ranked behind them. In addition, for new content providers, their content often be underestimated by search engines and placed it on unobtrusive web pages. In general, to increase the rank of a particular web page to the previous one in search results, the main factors are paid ads, web page click rate and web page with important keywords. Therefore, this study is based on the development of network technology and software functions to improve the ranking of search results on a specific web page.
Symmetric Sparse Linear Array for Active Imaging

Sparse sensor arrays can achieve significantly more degrees of freedom than the number of elements by leveraging the co-array, a virtual structure that arises from the far field narrowband signal model. Although several sparse array configurations have been developed for passive sensing tasks, less attention has been paid to arrays suitable for active sensing. This paper presents a novel active sparse linear array, called the Interleaved Wichmann Array (IWA). The IWA only has a few closely spaced elements, which may make it more robust to mutual coupling effects. Closed-form expressions are provided for the key properties of the IWA. The parameters maximizing the array aperture for a given even number of elements are also found. The near field wideband performance of the array is demonstrated numerically in a coherent imaging scenario.
A High Torque Density Outer Rotor Claw Pole Stator Permanent Magnet Synchronous Motor

This paper proposes a high torque density outer rotor claw pole permanent magnet synchronous motor (CPPMSM). The increased torque density will allow for the development of many low speed actuators without transmission or with small gear ratio, such as the actuators for robotics and alternators used in automobiles. The analytical design method, the structure of the machine as well as the results of a three dimensional (3D) electromagnetic model in ANSYS Maxwell are presented. A complete multi-physics finite element analysis including the electromagnetic and thermal analysis are presented. Moreover, an improved model for continuous operation of this machine has been proposed and analyzed to compare with the proposed results.
Deep hashing with multilevel similarity learning for multimedia similarity search

In this work, we propose a novel deep multimodal hashing method, termed as Deep Hashing with Multilevel Similarity Learning (DHMSL), which learns discriminative hash functions with deep neural networks by exploiting multilevel semantic similarity correlations of multimedia data. Firstly, we construct multilevel similarity correlation by jointly exploiting the local structure and semantic label information. Then, the unified binary codes are learned by preserving the multilevel similarity correlations as well as incorporating the bit balance and quantization error properties. Besides that, two deep neural networks are jointly trained to learn two sets of nonlinear hash functions by minimizing the errors of unified binary codes and outputs of the networks. We conduct experiments on two widely-used multimodal datasets, and the proposed DHMSL method can achieve the state-of-the-art performance compared with the baselines for both image-query-text and text-query-image tasks.
Integration of Image Feature and Word Relevance: Toward Automatic Image Annotation in Cyber-Physical-Social Systems

Image annotation is challenging due to the explosive increase of image data in cyber-physical-social systems. Because of the semantic gap between images and corresponding labels, it has attracted extensive attentions in recent years. However, most existing research neglects the imbalanced distribution of different classes and the internal relevance of image labels. Besides, the weak image labeling affects the annotation performance to some extent. To address these issues, we propose a learning model for image annotation through integrating deep features and label relevance of images. Specifically, we first employ a convolutional neural-network approach to extract the deep features of images and utilize the synthetic minority oversampling technique to deal with the problem of class imbalance. Furthermore, we exploit the correlations, including symbiotic and semantic relationships of labels, to compute the relevance of label sets. Then, we incorporate this relevance into one classifier to reconstruct the complete label sets, and learn the mapping from image features to the reconstructed label sets by the other classifier. In addition, a joint convex loss function is proposed, which combines the two classifiers via co-regularization and compels them to be consistent. We evaluate the proposed method on two benchmark data sets. The experimental results demonstrate that our method outperforms several state-of-the-art solutions.
Electric Field Transmitted into a Lossy Medium Radiated by a GPR Antenna - Comparison of Different Approaches

The paper deals with the calculation of the electric field radiated by a GPR antenna and transmitted into a lossy medium by using different approaches. Realistic bow-tie GPR antenna is modeled using gprMax software (featuring FDTD method) as well as equivalent dipole antenna with corresponding current distribution governed by Hallen integral equation. The magnitudes of the electric field transmitted into a lossy medium at different depths obtained via different approaches are compared.
A Dynamic Current Feature Map for Condition Monitoring of Rotating Machinery

This paper is devoted to proposing an efficient and visual method for engineers to evaluate the current signals of rotating machinery. Feature extraction methods based on time and frequency domain are applied to define the features of one-phase current signal. In order to dynamically estimate the stability and discover the potential hazards within three-phase current signals, Amplitude Variation Rate (AVR), Phase Fluctuation Rate (PFR) and Shape Inconsistent Rate (SIR) are proposed in this paper as conjoint phase features. A well-organized and strongly related visualization way to indicate the status and deviation degree of rotating machinery called Dynamic Current Features Map (DCFM) is established, where an unsupervised machine learning method is adopted to describe the healthy area. The new map expresses character including rotating speed, time track and the deviation degree from normal condition simultaneously, which can be an appropriate fashion for quick and accurate decision making. Historical data of current signals from healthy motorized spindle, current signals from aged sample and fault signals of motorized spindle are collected to test the availability and efficiency of DCFM and the result proves the map's potential to fulfill the condition monitoring task.
Properties and Applications of Gromov Matrices In Network Inference

In this paper, we review the commonly used breadth-first search (BFS) tree heuristic for network inference and estimation. The BFS method usually randomly chooses a BFS spanning tree in a graph to perform an estimation task. However, there are an intractable number of spanning trees in a dense graph. We represent a BFS tree with a Gromov matrix, and propose a method that constructs a parametric family of Gromov matrices for inference and estimation instead of a randomly selected BFS tree. This procedure increases the size of the candidate set and hence enhances the performance of the classical BFS heuristic. On the other hand, our new scheme is based on simple algebraic constructions using matrices, and hence is still computationally tractable. We discuss two applications on network inference and estimation to demonstrate the usefulness of the proposed method in handling signal and information over networks.
A Distributed Transmission Scheduling Algorithm for Wireless Networks Based on the Ising Model

Message scheduling in wireless networks addresses the question of which links should transmit at what time, in order to maximize the transmitting data volume without interference. Many algorithms have been proposed to solve this problem, both centralized and distributed. The distributed approach is more attractive since the networks usually lack a centralized processor. Aiming to solve the message scheduling problem for wireless networks, in this paper, we present a fully distributed scheduling policy based on the Ising model.
OMP with Grid-Less Refinement Steps for Compressive mmWave MIMO Channel Estimation

Millimeter-wave (mmWave) communications will play an important role in future wireless communication systems. Due to the high omni-directional path loss present at mmWave bands, mmWave channels are dominated by a few reflection paths, only. The resulting sparsity of mmWave channels in the angular domain can be exploited in order to obtain channel estimates from compressed measurements. We propose mmWave multiple-input multiple-output (MIMO) channel estimation for compressive measurements using extensions of the conventional grid-based orthogonal matching pursuit (OMP) algorithm with grid-less refinement steps based on the maximum likelihood principle. Numerical examples show that the proposed estimators are able to significantly outperform, e.g., grld-based OMP.
Image captioning based on deep reinforcement learning

Recently it has shown that the policy-gradient methods for reinforcement learning have been utilized to train deep end-to-end systems on natural language processing tasks. What's more, with the complexity of understanding image content and diverse ways of describing image content in natural language, image captioning has been a challenging problem to deal with. To the best of our knowledge, most state-of-the-art methods follow a pattern of sequential model, such as recurrent neural networks (RNN). However, in this paper, we propose a novel architecture for image captioning with deep reinforcement learning to optimize image captioning tasks. We utilize two networks called "policy network" and "value network" to collaboratively generate the captions of images. The experiments are conducted on Microsoft COCO dataset, and the experimental results have verified the effectiveness of the proposed method.
Genome Editing of Pigs for Agriculture and Biomedicine

Pigs serve as an important agricultural resource and animal model in biomedical studies. Efficient and precise modification of pig genome by using recently developed gene editing tools has significantly broadened the application of pig models in various research areas. The three types of site-specific nucleases, namely, zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein, are the main gene editing tools that can efficiently introduce predetermined modifications, including knockouts and knockins, into the pig genome. These modifications can confer desired phenotypes to pigs to improve production traits, such as optimal meat production, enhanced feed digestibility, and disease resistance. Besides, given their genetic, anatomic, and physiologic similarities to humans, pigs can also be modified to model human diseases or to serve as an organ source for xenotransplantation to save human lives. To date, many genetically modified pig models with agricultural or biomedical values have been established by using gene editing tools. These pig models are expected to accelerate research progress in related fields and benefit humans.
Lateral control approach of powered parafoils combining wind feedforward compensation with active disturbance rejection control

Accurate lateral trajectory tracking control of powered parafoil system with high capacity of resisting wind disturbance still remains a challenging problem. To reduce the influence of variable wind disturbance on the lateral yaw angle control system of the powered parafoil, a novel control approach combining wind compensation with active disturbance rejection control (ADRC) is proposed. The wind dynamic feedforward compensation controller and ADRC are respectively designed. The wind disturbance can be directly compensated in the control process, such that the trajectory tracking precision and disturbance-rejection capacity can be enhanced simultaneously. Mathematical simulations show that, the proposed control approach possesses better control performances and robustness compared with classical ADRC and PID.
The Expression of Brain-Type Fatty Acid Binding Protein (FABP7) in Rainbow Trout (Oncorhynchus Mykiss)

The Fatty acid-binding proteins (FABP) is a 14-15kDa intracellular protein which belong to large multigene family of intracellular lipid-binding proteins and play a important role in lipid uptake, transport lipid homeostasis. In this study, the rainbow trout FABP7 (GenBank accession No. JN413683) and actin (GenBank accession No. AF330142) were used to design primers for analyzing the expression during temperature challenge using qRT-PCR approach. The FABP7 are detected in liver, intestine and spleen in triploid, golden and diploid rainbow trout. After the temperature dropped from 10°C to 3°C, the relatively expression peaks at 6hr(triploid), 12hr (golden) in liver and 2hr (diploid) in intestine, the FABP7 reduced from 2hr and 6hr to 24hr in diploid and triploid rainbow trout in intestine, the FABP7 increased from 0hr to 24hr in diploid rainbow trout. After the temperature raise from 10°C to 18°C, the relatively expression peaks at 24hr (triploid) in intestine and 24hr (golden) in spleen, the FABP7 reduced from 2hr to 12hr (diploid and triploid) in intestine and 2hr to 12hr (diploid) and from and 0hr to 6hr (triploid) in spleen, the FABP7 increased from 2hr to 12hr (golden) in intestine. Overall, these results demonstrate the expression of FABP was received impact by changes of temperature.
Study on the Tutorial Efforts to Scratch Learning

Tutorial is an important tool for programming teaching. In this paper, we design a moderate learning tutorial based on the Scratch programming platform. In addition, we tested our proposed lessons in two Grade 6 classes of primary school. Then, we utilized the process data and submitted projects of students during the tutorial study to analyze the teaching effectiveness of tutorial and discover the relationship between learning process and final score. Experimental results and performance analysis show that effective learning in the learning process has a significant impact on the final score and different modules have different effective learning time.