Miracle-dz/UniFAR-FTUs huggingface dataset:
Failure Mode and Effects Analysis for large scale multirotor Unmanned Aerial Vehicle Controlled by Moving Mass System

In this paper Failure Mode and Effects Analysis (FMEA) for a large scale multirotor systems (with moving mass) based on novel system for aircraft control will be presented. This system uses four petrol engines for lift and a moving mass system to control the vehicle. Analysis presented in this paper assesses the vulnerabilities of the system during the vehicle operation. The main objective of the analysis is to understand the cause and severity of the failures that can occur to the petrol engines and the moving mass system. Our unmanned aerial vehicle system is used for environmental monitoring and maritime security developed under MORUS project funded under NATO SPS Program. The ultimate goal of our research and design is to make an unmanned aerial vehicle that can lift larger amount of load (approximately 40kg). During its operation time the unmanned aerial vehicle might fail to complete a certain assignment so failure mode and effects analysis is needed to account for such problems and to find appropriate activities to reduce the overall risk the system faces during the mission.
Adaptive SVR Denoising Algorithm for Fetal Monitoring System

This paper proposes a new fetal monitoring system, including the acquisition and the processing of fetal heart sounds (FHS). Based on the foundation of the stethoscope principle, a single-channel, non-invasive sensor is designed to acquire the fetal heart sounds, in which polyvinylidene fluoride (PVDF) membrane material is used as the core transducer. In the fetal heart sounds processing part, we propose a new method for denoising based on adaptive support vector regression (SVR) which has a good performance on curve fitting and effectively weakens the interference of additive noise. Thus, the clean fetal heart signals extracted from the interfered source can be further utilized to draw the fetal phonocardiogram (FPCG) and calculate the fetal heart rate (FHR).
A Light CNN based Method for Hand Detection and Orientation Estimation

Hand detection is an essential step to support many tasks including HCI applications. However, detecting various hands robustly under conditions of cluttered backgrounds, motion blur or changing light is still a challenging problem. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection yet at a high computational expense. In this paper, we propose a light CNN network, which uses a modified MobileNet as the feature extractor in company with the SSD framework to achieve a robust and fast detection of hand location and orientation. The network generates a set of feature maps of various resolutions to detect hands of different sizes. In order to improve the robustness, we also employ a top-down feature fusion architecture that integrates context information across levels of features. For an accurate estimation of hand orientation by CNN, we manage to estimate two orthogonal vectors' projections along the horizontal and vertical axes then recover the size and orientation of a bounding box exactly enclosing the hand. Evaluated on the challenging Oxford hand dataset, our method reaches 83.2% average precision (AP) at 139 FPS on a Nvidia Titan X, outperforming the previous methods both in accuracy and efficiency.
Learning Multi-view Generator Network for Shared Representation

Multi-view representation learning is challenging because different views contain both the common structure and the complex view specific information. The traditional generative models may not be effective in such situation, since view-specific and common information cannot be well separated, which may cause problems for downstream vision tasks. In this paper, we introduce a multi-view generator model to solve the problem of multi-view generation and recognition in a unified framework. We propose a multi-view alternating back-propagation algorithm to learn multi-view generator networks by allowing them to share common latent factors. Our experiments show that the proposed method is effective for both image generation and recognition. Specifically, we first qualitatively demonstrate that our model can rotate and complete faces accurately. Then we show that our model can achieve state-of-art or competitive recognition performances through quantitative comparisons.
A Fast No-Reference Screen Content Image Quality Prediction Using Convolutional Neural Networks

Image quality assessment (IQA) is an inherent research topic in image processing field for several decades. Recently, machine learning has achieved success in many multimedia tasks and can be applied in IQA. Especially, screen content images (SCIs) is greatly increasing in various applications, but the characteristics of SCIs makes it difficult to directly apply general IQA methods to predict qualities. In this paper, we propose a fast no-reference SCIs quality prediction method. First, we use the convolutional neural networks (CNNs) to predict the quality scores of each patch. Second, we present a SCIs-oriented quality aggregation algorithm for acceleration. Experimental results demonstrate that our method can achieve the high accuracy (0.957) with subjective quality scores, outperforming existing methods. Moreover, our method is computationally appealing, achieving flexible complexity performance by selecting different groups of patches.
Switch based Power Optimization Technique of SPV Arrays with Voltage and Current Compensation

Maximum power extraction from a Solar Photo Voltaic (SPV) array under non-optimal conditions like Non-Uniform Illumination (NUI) is an important requirement. Distributed Maximum Power Point Tracking (DMPPT) architecture offers relatively higher power yield with voltage and current compensation compared to DMPPT without voltage compensation. In this paper, a new switch based distributed power optimization technique with voltage and current compensation is proposed. This technique uses switches in the DMPPT architecture and operates each module at its exact Maximum Power Point (MPP). Bidirectional flyback converters are used for voltage and current (shunt-series) compensation. The proposed scheme is validated using simulation studies. The simulation results are presented for a 2x2 array with different NUI conditions.
Performance of Nested vs. Non-Nested SVM Cross-Validation Methods in Visual BCI: Validation Study

Brain-Computer Interface (BCI) is a technology that utilizes brainwaves to link the brain with external machines for either medical analysis, or to improve quality of life such as control and communication for people affected with paralysis. The performance of BCI systems depends on classification accuracy, which influences the Information Transfer Rate. This motivates researchers to improve their classification accuracy as best possible. A bias problem in reporting accuracies by using non-nested cross-validation methods was thought to increase accuracy. The aim of this paper was to validate and quantify such a concept by using a low-cost commercial EEG recorder to classify visually evoking face vs scrambled pictures, and report high accuracy using non-nested cross validation. The algorithm employed Independent Component Analysis followed by feature extraction with sample covariance matrices. The data were then classified using Support Vector Machines. The accuracy was tested with nested and non-nested cross-validation methods; accuracies obtained were 63% and 76%, respectively.
A Useful Multi-Window Monitor Design

Conventional monitors can display only one single video input at one time which limits the flexibility of monitors. The concept and the prototype design of a new useful multi-window monitor is introduced, in which several video inputs can be displayed simultaneously on the monitor screen and their positions and sizes can be easily adjusted by user himself to achieve flexibility and customization of the video display system. Display interface including VGA, DVI HDMI DP and network video stream can all be used as the input of the monitor. GPU parallel computing is utilized to accelerate the decoding and color conversion process in the prototype design. The results show that it can effectively use the graphics card parallel computing capabilities to achieve high resolution multi-window display performance.
Algorithm for Bengali Keyword Extraction

We present algorithm for keyword extraction from a Bengali document. In natural language processing (NLP), keyword extraction is the automated process to identify a set of terms that represent the information discussed in a document. A lot of research works have been done for keyword extraction in resource rich languages. Some of those works followed supervised approach using specific corpus whereas the latest techniques use unsupervised approach. Keyword extraction procedure already achieved state-of-the-art performance for the resource rich languages. Only a few works have been done on the keyword extraction for documents in Bengali but none of them could achieve > 70% accuracy. In this article, we discuss the methods for extracting Bengali keywords from a specific document collection following unsupervised learning approach. Generally, Bengali keyword extraction is difficult in terms of words parsing, stemming, excluding stop words etc. The accuracy of those modules also impact the performance of the keyword extraction procedure. However, we obtained 87% accuracy to identify the correct Bengali keywords from a document. The procedure we have discussed for keyword extraction can also be applied to any language; but here we have provided all of our experimental results specifically for Bengali language.
Multi-View Egocentric Video Summarization

With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content. Nonetheless, the spread of social and egocentric cameras tends to create an abundance of sparse scenarios captured by several devices, and ultimately required to be jointly summarized. In this paper, we propose the problem of summarizing videos recorded simultaneously by several egocentric cameras that intermittently share the field of view. We present a supervised-learning framework that (a) identifies a diverse set of important events among dynamically moving cameras that often are not capturing the same scene, and (b) selects the most representative view(s) at each event to be included in the universal summary. A key contribution of our work is collecting a new multiview egocentric dataset, Multi-Ego, due to the lack of an applicable and relevant alternative. Our dataset consists of 41 sequences, each recorded simultaneously by 3 cameras and covering a wide variety of real-life scenarios. The footage is annotated comprehensively by multiple individuals under various summarization settings: (a) single view, (b) two view, and (c) three view, with a consensus analysis ensuring a reliable ground truth. We conduct extensive experiments on the compiled dataset to show the effectiveness of our approach over several state-of-the-art baselines. We also show that it can learn from data of varied number-ofviews, deeming it a scalable and a generic summarization approach. Our dataset and materials is publicly available.1
Price Management in Resource Allocation Problem with Approximate Dynamic Programming

The problem of managing the price for resource allocation arises in several applications, such as purchasing plane tickets, reserving a parking slot, booking a hotel room or renting SW/HW resources on a cloud. In this paper, we model a price management resource allocation problem with parallel Birth-Death stochastic Processes (BDPs) to account for the fact that the same resource can be possibly purchased by customers at different prices. In addition, customers can hold the resource at the purchase price to the necessary extent. The maximization of the revenue in both the finite and infinite time horizon cases is addressed in this paper with Stochastic Dynamic Programming (DP) approaches. To overcome the difficulty in solving the corresponding optimization problem due to the state space explosion, Approximate Dynamic Programming (ADP) techniques (in particular, the Least Square Temporal Difference method along with Monte Carlo simulations) are adopted. Furthermore, a MATLAB Toolbox is developed with the aim of solving stochastic DP/ADP problems and supporting probabilistic analysis. Extensive simulations are performed to show the effectiveness of the proposed model and the optimization approach.
Propagation Loss Reduction Between On-Body Antennas by Using a Conductive Strip Line

This letter proposes a very simple solution to reduce the propagation loss between two antennas/sensors on body at 2.45 GHz by utilizing a flexible conductive strip line (CSL). The performance of the CSL is comparable to that of the free-access transmission line in the work by Tran et al. in some certain cases. The transmission improvement by the CSL is verified by measurement results on a perfect electrical conductor plane, on a phantom, and on a real body more than 20 dB for non-line-of-sight scenarios. Dimension parameters of the CSL are optimized so that the highest improvement by the CSL can be achieved and it is easy to be integrated into clothes. Moreover, the specific absorption rate is satisfied when the CSL is deployed near body.
Efficient ML-Estimator for Blind Reverberation Time Estimation

A new maximum likelihood (ML) estimator for the blind estimation of the reverberation time (RT) is derived. In contrast to previously proposed ML-based reverberation time estimators, the RT estimate is obtained by a simple closed-form expression, which leads to significant computational savings. Moreover, it is shown that the new estimator is unbiased and reaches the Crámer-Rao lower bound. The proposed RT estimator achieves a similar estimation accuracy but involves a significantly lower computational complexity compared to an ML-based RT estimator that scored among the best at the ACE Challenge.
Scalable Semi-Supervised Learning by Graph Construction with Approximate Anchors Embedding

Semi-supervised learning (SSL) generalizes and improves supervised learning using labeled data and unlabeled data. With the rapid development of the Internet as well as the increasing availability of data in the open web, collecting tremendous amount of unlabeled data has became more feasible. As the central notion in SSL, smoothness is often defined on a graph representation of the data. However, only few researches up to date adapt graph-based SSL approaches into the large scale Solution. Even if approximation approaches are commonly used in methods of the large-scale SSL, it is difficult to outperform original methods. In this paper, we propose an efficient method to construct a graph based on anchors. There are two major concerns in the anchor graph generally: how to learn better anchors and to present the input both in a more efficient and effective fashion. Intuitively, compared with sparse representation (e.g. local anchor embedding), a more straightforward approach is marginal regression with non-negative constraint. Rather than using clustering algorithms, the anchors are trained using dictionary learning with sparsity constraints. And thus in our approach, not only the relation between anchors and data is taken into consideration, the relation between anchors is also regarded. Beyond that, in the evaluation section, we demonstrate that our method outperforms other large scale SSL methods as well as traditional ones in classification performance according to several classical datasets. Further more, the proposed method solves the large scale SSL problem more efficient than current methods. Therefore, our method is an efficient and effective alternative to handle large scale SSL problem.
Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven Optimization

Interactive multi-agent simulation algorithms are used to compute the trajectories and behaviors of different entities in virtual reality scenarios. However, current methods involve considerable parameter tweaking to generate plausible behaviors. We introduce a novel approach (Heter-Sim) that combines physics-based simulation methods with data-driven techniques using an optimization-based formulation. Our approach is general and can simulate heterogeneous agents corresponding to human crowds, traffic, vehicles, or combinations of different agents with varying dynamics. We estimate motion states from real-world datasets that include information about position, velocity, and control direction. Our optimization algorithm considers several constraints, including velocity continuity, collision avoidance, attraction, direction control. Other constraints are implemented by introducing a novel energy function to control the motions of heterogeneous agents. To accelerate the computations, we reduce the search space for both collision avoidance and optimal solution computation. Heter-Sim can simulate tens or hundreds of agents at interactive rates and we compare its accuracy with real-world datasets and prior algorithms. We also perform user studies that evaluate the plausible behaviors generated by our algorithm and a user study that evaluates the plausibility of our algorithm via VR.
To Disguise or To Disclose? Effect of Mimicking News Articles on Sponsored Content Engagement

Media companies are incorporating native advertisements to their websites, placing them as sponsored articles in the news feeds which used to be dedicated to the editorial content. Despite the high similarity in the format, limited studies have investigated whether the ad content should be similar to the news in order to attract consumer attention and increase purchase intent. Using a novel and large-scale dataset from a leading news media company, we investigate whether and to what extent the writing style of the article title affects the user engagement with the sponsored articles. We employ a combination of human evaluation, natural language processing, and binary classification models to extract the linguistic features of sponsored articles. After accounting for endogeneity via a holistic set of econometrics models, we find evidence that mimicking the style of news title could double the click probabilities of sponsored articles. Moreover, it also boosts the conversion probability by over 40%. Sub-analyses show that the impact is moderated by the size of device screen. The effect is especially larger on the mobile devices, indicating that contextual congruity might be a major driving force of the engagement lift. We also compare the linguistic characteristics of ads-alike sponsored articles with news-alike sponsored articles. This study shows that many advertisers could improve the content strategy on the news media by matching the news writing style. It also alleviates regulators’ concern on the deceptiveness of native advertising, especially in the form of sponsored articles.
Consensus Speed of Static Pinning Consensus Control of Multi-Agent Systems

This paper is concerned with the pinning consensus problem of multi-agent systems with single integrator agents in general directed networks. In this problem, the consensus speed is critical for the analysis of the convergence rate of the system. We show that the upper bound of the consensus speed of static pinning control is the eigenvalue with the smallest real part of the matrix reduced by deleting rows and columns corresponding to indexes of pinned agents from the Laplacian matrix. We also investigate the limit of eigenvalues of the controlled multi-agent system as the control gains go to infinity. In examples, we discuss the two cases where the network topologies are a directed small-scale and a directed scale-free graph.
Establishing Data Integrity in Networks of Cyber-Physical Systems

Cyber-physical systems are pervasive in today’s society with applications ranging from critical infrastructure systems such as the power grid or water distribution network to industrial control systems. It is clear that the reliability, safety and security of such systems is imperative. In this paper we propose utilizing tools borrowed from cryptography to ensure that false data injection attacks affecting the communications of cyber-physical systems are effectively and reliably detected. A practical example is presented to both motivate and validate the developed tools.
A Comparative Study of Subgraph Matching Isomorphic Methods in Social Networks

With the fast development of social networks, more and more data has been generated. Finding useful information among these data is important. Subgraph matching is the method that can be used in social networks for social search, recommender systems, and so on. In this paper, we choose five typical graph matching methods which are VF2, SPath, TurboISO, BoostIso, and RI to value their performance and scalability in social networks. These methods are verified by three social network data sets whose node’s number vary from thousands to millions. According to the experiments, we can find that VF2 and RI is applicable for rather small graphs while SPath performs better in large graph when average degree of graph is small. When graph with high average degree, TurboISO and BoostIso performs better. What is more, we also compare the efficiency of different matching order when searching. The order choosing strategy proposed by RI is better.
Experimental Demonstration of Subband Index Techniques for  $m$ -CAP in Short-Range SI-POF Links

In this letter, novel spectrally efficient subband index (SI) techniques have been developed for multiband carrierless amplitude and phase modulation (<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-CAP). The SI techniques transmit additional information bit on the index of the conventional <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-CAP subbands. Experimental demonstrations are performed in a short-range optical data link using step-index plastic optical fiber. The demonstrations show that the proposed techniques enable flexibility in design and significantly improve the power/spectral efficiency of the conventional <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-CAP.
Cell Library Design for Ultra-Low Power Internet-of-Things Applications

A wide range of applications in the field of the Internet-of-Things (IoT) possess only strongly limited energy resources. This rises the need for Ultra-Low Power (ULP) designs that enable processing with lowest energy consumption. This, however, comes at considerable performance costs. Consequently, there is a need for flexible design flows that enable the designer to select the best tradeoff between energy consumption and performance for the destined application. Towards this end, we present in this paper a complete flow for the design of a ULP standard cell library based on the subthreshold methodology. The flow considers both the selection of the appropriate technology parameters, like supply voltage and base sizing, as well as the actual design and characterization of the standard cells. Simulation results for an ARM-based processor and ITC’99 and EPFL benchmark circuits realized in a commercial 130 nm technology indicate a possible reduction of power consumption of several factors, while performance degrades between two and three orders of magnitude.