Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator

This paper investigates the localization of multiple near-field narrowband sources with a symmetric uniform linear array, and a new linear prediction approach based on the truncated singular value decomposition (LPATS) is proposed by taking an advantage of the anti-diagonal elements of the noiseless array covariance matrix. However, when the number of array snapshots is not sufficiently large enough, the “saturation behavior” is usually encountered in most of the existing localization methods for the near-field sources, where the estimation errors of the estimated directions-of-arrival (DOAs) and ranges cannot decrease with the signal-to-noise ratio. In this paper, an oblique projection based alternating iterative scheme is presented to improve the accuracy of the estimated location parameters. Furthermore, the statistical analysis of the proposed LPATS is studied, and the asymptotic mean-square-error expressions of the estimation errors are derived for the DOAs and ranges. The effectiveness and the theoretical analysis of the proposed LPATS are verified through numerical examples, and the simulation results show that the LPATS provides good estimation performance for both the DOAs and ranges compared to some existing methods.
An importance sampling method based on a one-step look-ahead density from a Markov chain

We propose a new importance sampling method that constructs an importance sampling density which approximates the zero-variance sampling density nonparametrically as follows. In a first stage, it generates a sample (possibly approximately) from the zero-variance density using, for example, Markov chain Monte Carlo methodology. In a second stage, the method constructs a kernel density estimator of the zero-variance density based on the sample in the first stage. The most important aspect of the method is that, unlike other kernel estimation methods, the kernel of the estimator is defined as the one-step transition density of a Markov chain whose stationary distribution is the zero-variance one. We give examples where this one-step transition density is available analytically and provide numerical illustrations in which the method performs very well.
Wireless Technologies for Emergency Response: A Comprehensive Review and Some Guidelines

Disaster situations require a prompt emergency response. The failure of the legacy (wired) emergency response systems to efficiently cope in such time-critical environments has led to a great interest in wireless-based emergency response systems. In this regard, a number of wireless technologies and systems—each with its peculiar characteristics and pros and cons—have been proposed for use in various emergency response situations. This paper presents, to the best of our knowledge, the first comprehensive survey of the research on this practically important topic. We motivate the use of wireless technology for emergency response and present a comparative analysis of the available wireless technologies. After outlining the scope and requirements of emergency response systems, we provide an overview of the architectures and features of wireless-based emergency response systems and then compare different wireless technologies for different emergency settings. We provide not only details about individual emergency response systems and technologies but also expend efforts to show the forest for the trees to emergency response practitioners—in particular, we provide general high-level guidelines that can help in deciding the right technology for a particular situation, and discuss potential pitfalls from the large-scale emergency-network deployment experience of one of the authors. The emergency response operation is split into five functionalities: we present general guidelines that would help public safety agencies in choosing suitable wireless technologies for each one of them. Finally, we also highlight opportunities and identify the potential areas for further investigation.
Time-Frequency Analysis for Music Genre Classification by using Wavelet Package Decompositions

In this paper, time-frequency analysis method was studied in the genre classification of music songs. Due to the benefits of multi-resolution analysis, the proposed methods first adopted the wavelet package decomposition (WPD) to obtain candidate features from the spectrograms of music songs. Then the singular value decomposition (SVD) was used to extract the desired features because of its dimension reduction ability. In the experiments, well-known database named ISMIR2004 was applied. To examine the performance of the proposed methods, the experimental results of the proposed methods were compared with the results of the MFCC based methods.
Toward Improved Child–Robot Interaction by Understanding Eye Movements

Globally, 1 in 160 children has an autism spectrum disorder (ASD). Problems with joint attention (JA) are core features of ASDs. Here, we investigate how typically developing (TD) children and children with ASD initiate JA with a gaze contingent avatar. Thirty-one participants with ASD and 33 TD matched controls directed an avatar’s gaze to a series of referent images. Observing pupil diameter and gaze location data, we explore how distinguishing the two groups as well as their different eye-movement behaviors could be used to improve child–robot interaction. With a sequence to sequence neural network we distinguish if a child is TD or has an ASD, then using ${K}$ -means clustering, we group pupil diameters and gaze locations independently to determine the child’s attention level as well as to refine the classification process. Using these metrics, we could trigger appropriate responses from the robot to increase the level of attention from the child toward the robot. Results show significant differences between the eye behaviors of individuals with ASDs and those without. Further to this, we achieve a 79.76% classification accuracy when using pupil diameter data to distinguish the two groups.
Dynamic Origin-Destination Estimation without Historical Origin-Destination Matrices for Microscopic Simulation Platform in Urban Network

As the input of dynamic traffic assignment (DTA) model, dynamic origin-destination (OD) matrix is crucial for intelligent transportation applications. This paper presents a solution scheme for Dynamic OD estimation without historical OD matrices by using traffic flow data of loop detectors. Through a bi-1evel optimization structure, OD matrices of different time intervals are estimated sequentially. A ridge regression method is applied to obtain the initial OD matrix in this paper. A constrained nonlinear programming method is proposed to calibrate the assignment matrix for accurately mapping the demand to the traffic flows. With assignment matrix, a modified simultaneous perturbation stochastic approximation (SPSA) algorithm, called Restart-SPSA, is proposed to estimate OD matrix in each time interval. The capability of the proposed solution scheme is validated in an urban network of Singapore using the microscopic traffic simulator VISSIM. The simulation results show that calibration of assignment matrix and Restart-SPSA can improve the performance significantly compared with the method using uncalibrated assignment matrix and original SPSA.
Insights Into SND1 Oncogene Promoter Regulation

The staphylococcal nuclease and Tudor domain containing 1 gene (SND1), also known as Tudor-SN, TSN or p100, encodes an evolutionarily conserved protein with invariant domain composition. SND1 contains four repeated staphylococcal nuclease domains and a single Tudor domain, which confer it endonuclease activity and extraordinary capacity for interacting with nucleic acids, individual proteins and protein complexes. Originally described as a transcriptional coactivator, SND1 plays fundamental roles in the regulation of gene expression, including RNA splicing, interference, stability, and editing, as well as in the regulation of protein and lipid homeostasis. Recently, SND1 has gained attention as a potential disease biomarker due to its positive correlation with cancer progression and metastatic spread. Such functional diversity of SND1 marks this gene as interesting for further analysis in relation with the multiple levels of regulation of SND1 protein production. In this review, we summarize the SND1 genomic region and promoter architecture, the set of transcription factors that can bind the proximal promoter, and the evidence supporting transactivation of SND1 promoter by a number of signal transduction pathways operating in different cell types and conditions. Unraveling the mechanisms responsible for SND1 promoter regulation is of utmost interest to decipher the SND1 contribution in the realm of both normal and abnormal physiology.
Effect of Organic Solvent on MWCNT-PDMS Nanocomposite Based Capacitive Pressure Sensors

In-spite of the recent advancements related to carbon nanotube (CNT) based sensors, dispersion of CNTs in a polymer matrix is still a challenge. In this work we propose to use organic solvents to pre-disperse CNTs before introducing them into the polymer matrix. The choice of the dispersion medium plays a vital role, not only for the dispersion quality but also for the sensitivity of the sensor itself. Five different solvents have been tested, characterized and compared based on the sensor capacitive response. The results show that tetrahydrofuran (THF) and isopropanol (ISO) have nearly the same sensitivity and hysteresis behavior, but THF shows a better reproducibility of the sensor.
Effect of Sliding Window Time on the Classification of Driver Mental Workload Performance Using Near-Infrared Spectroscopy (NIRS)

Near-infrared spectroscopy (NIRS) is a promising solution for classifying mental workload while driving, which is important for creating a support system and preventing traffic accidents. Most of the existing research classified mental workload levels via machine learning or deep learning algorithms, where the sliding window time plays an important role in obtaining the features. This study investigated a novel classification of mental workload level using NIRS data by the difference of sliding window time in both channel-independent and subject-independent scenarios. Five sliding times from 1 to 5 s were tested for classification of the mental workload level in 60 experiments in the naturalistic condition. The results indicated that the sliding times were not significantly related to the accuracy of classification using NIRS data when the sample size was just enough. Furthermore, this study succeeded in indicating the driver mental workload using NIRS information in the naturalistic situation with autocross and car-following conditions with an accuracy of over 80%.
On Integrating Non-Intrusive Current Measurement into GaN Power Modules

Increasing the efficiency and the reliability of converters are the main goals in power electronics. This research contributes to these goals by investigating how to integrate nonintrusive current measurement into Gallium Nitride (GaN) power modules. Therefore, Finite Element Analysis (FEA) simulations are conducted to determine the location of the sensor as well as the power electronic layout and the AC capacitor placement. These simulations and the experience gathered while working with a synchronous GaN buck converter and an anisotropic magnetoresistive (AMR) sensor board lead finally to the design of the GaN power module, which is presented in this article. The novel outcome of this project is the current sensing unit included in the power module. Based on the simulation results, it was decided to measure the current mismatch between the two parallel GaN switches for reliability assurance and circuit protection. Moreover, the sensor choice is discussed and experimental measurements with the board are presented.
High Density Ground Maps using Low Boundary Height Estimation for Autonomous Vehicles

In this work we propose a new method to create high density ground maps for autonomous vehicles based on low boundary elevation estimation. Ground maps are created using ray casting of laser beams in 3D LiDAR data accumulated over time and computing the lower elevation data between 3D LiDAR scans. This lower boundary works as a low envelope of the accumulated point cloud. Our lower boundary ground maps approach is not affected by moving objects on the road, and produces high density maps even with coarse vertical resolution 3D LiDARs, works on long curved roads with different elevations, and it is computationally efficient. As proof of concept, we present an application for real-time obstacle and ground segmentation of 3D LiDAR data using our ground maps.
PUF Based Pay-Per-Device Scheme for IP Protection of CNN Model

With great success of Convolutional Neural Network (CNN) in many applications, it is not surprising that the CNN models will become commercial IPs. This paper proposes a Physical Unclonable Function (PUF) based pay-per-device scheme for protecting IPs of CNN models. PUFs are embedded into the FPGA based CNN accelerator. The original CNN model trained by the IP vendor is obfuscated based on the PUFs before being distributed to the end users. The PUF challenges come from obfuscated CNN model parameters, and the PUF responses determine outputs of convolutional layers. In this way, the obfuscated CNN model is limited to be correctly executed in one specific FPGA. Experiments on AlexNet show that performance and hardware overhead of the CNN accelerator are negligible. For authorized end users, the prediction accuracy of the obfuscated CNN model is the same as that of the original one, while for adversaries, prediction accuracies of guessed ones are nearly 0.
Real-Time Taxi Demand Prediction using data from the web

In transportation, nature, economy, environment, and many other settings, there are multiple simultaneous phenomena happening that are of interest to model and predict. Over the last few years, the traffic data that we have at our disposal have significantly increased, and we have truly entered the era of big data for transportation. Most existing traffic flow prediction methods mainly focus on capturing recurrent mobility trends that relate to habitual/routine behaviour, and on exploiting short-term correlations with recent observation patterns. However, valuable information that is often available in the form of unstructured data is neglected when attempting to improve forecasting results. In this paper, we explore time-series data and textual information combinations using machine learning techniques in the context of creating a prediction model that is able to capture in real-time future stressful situations of the studied transportation system. Using publicly available taxi data from New York, we empirically show that the proposed models are able to significantly reduce the error in the forecasts. The final mean absolute error (MAE) of our predictions is decreased by 19.5% for a three months testing period and by 57% if we focus only on event periods.
A RaaS Model Based on Emotion Analysis and Double Labeling Applied to Mobile Terminal

With the rapid development of mobile Internet applications, it is more and more difficult for mobile users to choose the right commodity in the face of a large amount of commodity information. Recommendation system is one of the effective means to solve the problem of information overload. However, the low-hardware configuration and limited computing capacity are common problems in most mobile devices, which make mobile computing applications face more and more serious challenges with increasingly strong demand for computing resources. Cloud computing, as a model of providing resources on demand, can provide technical support for solving the above problems. Recommendation as a service (RaaS), which packaged the recommendation system as a service, has attracted extensive attention from the business community and academia. In this paper, the RaaS model based on emotion analysis and double labeling is proposed. In this model, we make full use of commodity description information and user comment text to explore users’ emotional preference on the commodity attributes that is less considered in the existing recommendation models or algorithms. First, we extract the keywords by analyzing the commodity description and user behaviors to establish commodity intrinsic attribute labels and user preference labels. Second, we mine the user’s comments through emotion analysis to establish the commodity feedback labels and user concern labels. Finally, the recommendation results for users are generated by calculating the similarity of above four types of labels. Experimental results show that the proposed model and its algorithms can improve the effect of recommendation.
DroidMorph: Are We Ready to Stop the Attack of Android Malware Clones?

The number of Android malware variants (clones) are on the rise and, to stop this attack of clones we need to develop new methods and techniques for analysing and detecting them. As a first step, we need to study how these malware clones are generated. This will help us better anticipate and recognize these clones. In this paper we present a new tool named DroidMorph, that provides morphing of Android applications (APKs) at different level of abstractions, and can be used to create Android application (malware/benign) clones. As a case study we perform testing and evaluating resilience of current commercial anti-malware products against attack of the Android malware clones generated by DroidMorph. We found that 8 out of 17 leading commercial anti-malware programs were not able to detect any of the morphed APKs. We hope that DroidMorph will be used in future research, to improve Android malware clones analysis and detection, and help stop them.
An Enhanced Variable Phase Accumulator with Minimal Hardware Complexity Dedicated to ADPLL Applications

This paper presents a high-speed topology for phase counter in an All-digital phase-locked loop (ADPLL) architectures. The structure, called Variable Phase Accumulator (VP AC) is a digital block running at the highest frequency in the ADPLL. The high operating speed feature of the architecture is obtained by exploiting the count output in the reference frequency domain while the circuit needs to handle the radio frequency (RF) signal from the oscillator output. The enhanced topology decreases the timing critical path and minimizes the hardware logic in the highest frequency domain to a shift register encoding only four states. Indeed, a simple logic gate is used to slow the count process. The proposed circuit is demonstrated in 90-nm CMOS process. , which allows having a power save of about 20 times the power of a conventional counter without penalty in silicon area or frequency running.
On Vietoris-Rips complexes of planar curves

A Vietoris--Rips complex is a way to thicken a (possibly discrete) metric space into a larger space containing more topological information. We prove that if $C$ is a convex closed differentiable curve in the plane such that the convex hull of $C$ contains the evolute of $C$, then the homotopy type of a Vietoris--Rips complex of a subset of $n$ points from $C$ can be computed in time $O(n\log n)$. Furthermore, we show how to compute the $k$-dimensional persistent homology of these $n$ points in running time $O(n^2(k+\log n))$, which is nearly quadratic in the number of vertices $n$. This improves upon the traditional persistent homology algorithm, which is cubic in the number of simplices of dimension at most $k+1$, and hence of running time $O(n^{3(k+2)})$ in the number of vertices $n$. We ask if there are other geometric settings in which computing persistent homology is (say) quadratic or cubic in the number of vertices, instead of in the number of simplices.
Vehicle Detection and Localization using 3D LIDAR Point Cloud and Image Semantic Segmentation

This paper presents a real-time approach to detect and localize surrounding vehicles in urban driving scenes. We propose a multimodal fusion framework that processes both 3D LIDAR point cloud and RGB image to obtain robust vehicle position and size in a Bird's Eye View (BEV). Semantic segmentation from RGB images is obtained using our efficient Convolutional Neural Network (CNN) architecture called ERFNet. Our proposal takes advantage of accurate depth information provided by LIDAR and detailed semantic information processed from a camera. The method has been tested using the KITTI object detection benchmark. Experiments show that our approach outperforms or is on par with other state-of-the-art proposals but our CNN was trained in another dataset, showing a good generalization capability to any domain, a key point for autonomous driving.
Improving CNN-RNN Hybrid Networks for Handwriting Recognition

The success of deep learning based models have centered around recent architectures and the availability of large scale annotated data. In this work, we explore these two factors systematically for improving handwritten recognition for scanned off-line document images. We propose a modified CNN-RNN hybrid architecture with a major focus on effective training using: (i) efficient initialization of network using synthetic data for pretraining, (ii) image normalization for slant correction and (iii) domain specific data transformation and distortion for learning important invariances. We perform a detailed ablation study to analyze the contribution of individual modules and present state of art results for the task of unconstrained line and word recognition on popular datasets such as IAM, RIMES and GW.
Observer Based Tracking Control for the Isothermal CSTR

In this paper, an observer based state feedback control using Parallel Distributed Compensation (PDC) structure has been used to ensure the stability and trajectory tracking for the isothermal CSTR. Continuous Stirred Tank Reactors (CSTRs) are the most important element in the chemical industry. They are characterized by their complexity and nonlinearity. In this study, the Takagi-Sugeno (T -S) approach is proposed to simplify the nonlinear model of CSTR. However, in real-world control problems, the states may not be measurable, so, an observer is designed in order to estimate the immeasurable states and a tracking control is constructed based on the estimated states. An integral action is used to track a reference trajectory. The simulation results show a high precision tracking control.
Principles for Minimizing Cognitive Assistance Distraction in the Cockpit

Over the last three years, the MITRE Corporation has been developing a cognitive assistant concept for pilots called Digital Copilot. Digital Copilot reduces pilot workload and increases safety by offloading pilot tasks, increasing task efficiency, and inferring pilot intent to provide the right information at the right time. In this paper, we will introduce the Digital Copilot concept and review three design principals for the cockpit cognitive assistance gleaned from the design process, flight testing, and simulator studies of Digital Copilot with General Aviation pilots.