Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech Enhancement Using Successive State Estimation under Industrial Noise Environment

Speech communication under an industrial noise environment can seriously affect an operator's hearing and collaboration. For this environment, we use a successive state estimation method to perform speech enhancement to eliminate the effect of noise on speech. The successive state estimation uses Kalman filter algorithm. Compared with traditional Kalman speech-enhancement methods, we use a simpler difference form for a state equation to reduce the number of equation parameters. In experiments, we adopt a noise signal in a public database and add it to an actual speech signal as a noisy speech signal to be processed. Compared with Fast ICA (Independent Component Analysis) algorithm, this speech enhancement algorithm has better real-time performance.
Drug‐delivering nerve conduit improves regeneration in a critical‐sized gap

Autologous nerve grafts are the current “gold standard” for repairing large nerve gaps. However, they cause morbidity at the donor nerve site and only a limited amount of nerve can be harvested. Nerve conduits are a promising alternative to autografts and can act as guidance cues for the regenerating axons, without the need to harvest donor nerve. Separately, it has been shown that localized delivery of GDNF can enhance axon growth and motor recovery. FK506, an FDA approved small molecule, has also been shown to enhance peripheral nerve regeneration. This paper describes the design of a novel hole‐based drug delivery apparatus integrated with a polytetrafluoroethylene (PTFE) nerve conduit for controlled local delivery of a protein such as GDNF or a small molecule such as FK506. The PTFE devices were tested in a diffusion chamber, and the bioactivity of the released media was evaluated by measuring neurite growth of dorsal root ganglions (DRGs) exposed to the released drugs. The drug delivering nerve guide was able to release bioactive concentrations of FK506 or GDNF. Following these tests, optimized drug releasing nerve conduits were implanted across 10 mm sciatic nerve gaps in a BL6 yellow fluorescent protein (YFP) mouse model, where they demonstrated significant improvement in muscle mass, compound muscle action potential, and axon myelination in vivo as compared with nerve conduits without the drug. The drug delivery nerve guide could release drug for extended periods of time and enhance axon growth in vitro and in vivo.
A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

A novel approach is proposed for the path tracking of a Wheeled Mobile Robot (WMR) in the presence of an unknown lateral slip. Much of the existing work has assumed pure rolling conditions between the wheel and ground. Under the pure rolling conditions, the wheels of a WMR are supposed to roll without slipping. Complex wheel-ground interactions, acceleration and steering system noise are the factors which cause WMR wheel slip. A basic research problem in this context is localization and slip estimation of WMR from a stream of noisy sensors data when the robot is moving on a slippery surface, or moving at a high speed. DecaWave based ranging system and Particle Filter (PF) are good candidates to estimate the location of WMR indoors and outdoors. Unfortunately, wheel-slip of WMR limits the ultimate performance that can be achieved by real-world implementation of the PF, because location estimation systems typically partially rely on the robot heading. A small error in the WMR heading leads to a large error in location estimation of the PF because of its cumulative nature. In order to enhance the tracking and localization performance of the PF in the environments where the main reason for an error in the PF location estimation is angular noise, two methods were used for heading estimation of the WMR (1): Reinforcement Learning (RL) and (2): Location-based Heading Estimation (LHE). Trilateration is applied to DecaWave based ranging system for calculating the probable location of WMR, this noisy location along with PF current mean is used to estimate the WMR heading by using the above two methods. Beside the WMR location calculation, DecaWave based ranging system is also used to update the PF weights. The localization and tracking performance of the PF is significantly improved through incorporating heading error in localization by applying RL and LHE. Desired trajectory information is then used to develop an algorithm for extracting the lateral slip along X- and Y-axis from the PF estimated position of the WMR, the lateral slip along X- and Y-axis is then used to take some corrective measures. Lateral slip information is also used to find the direction along which WMR has to move to get back along the desired trajectory. Simulation results show that our proposed LHE and RL heading estimation methods significantly improve the PF localization and tracking performance on a slippery surface in both indoor and outdoor environments. The simulation results also show that the accurate locations of WMR and desired path information are used to estimate and compensate the lateral slip.
Application of Time-Domain Full Waveform Inversion to Cross-Hole Radar Data Measured at Xiuyan Jade Mine, China

Xiuyan Jade, produced in Xiuyan County, Liaoning Province, China is one of the four famous jade in China. King Jade, which is deemed the largest jade body of the world, was broken out from a hill. The local government planned to build a tourism site based on the jade culture there. The purpose of the investigation was to evaluate the stability of subsurface foundation, and the possible positions of mined-out zones to prevent the further rolling of the jade body. Cross-hole radar tomography is the key technique in the investigation. Conventional travel time and attenuation tomography based on ray tracing theory cannot provide high-resolution images because only a fraction of the measured information is used in the inversion. Full-waveform inversion (FWI) can provide high-resolution permittivity and conductivity images because it utilizes all the information provided by the radar signals. We deduce the gradient expression of the time-domain FWI with respect to the permittivity and conductivity using a method that is different from that of the previous work and realize the FWI algorithm that can simultaneously update the permittivity and conductivity by using the conjugate gradient method. Inverted results from synthetic data show that time-domain FWI can significantly improve the resolution compared with the ray-based tomogram methods. FWI can distinguish targets that are as small as one-half to one-third wavelength and the inverted physical values are closer to the real ones than those provided by the ray tracing method. We use the FWI algorithm to the field data measured at Xiuyan jade mine. Both the inverted permittivity and conductivity can comparably delineate four mined-out zones, which exhibit low-permittivity and low-conductivity characteristics. Furthermore, the locations of the interpreted mined-out zones are in good agreement with the existing mining channels recorded by geological data.
Effect of loaded orthodontic miniscrew implant on compressive stresses in adjacent periodontal ligament.

OBJECTIVES: To describe the relationship between the proximity of miniscrew implants (MSIs) to the periodontal ligament (PDL) and stress in the PDL under different load magnitudes and different bone properties. MATERIALS AND METHODS: Sixteen subject-specific finite element models of the region of the maxillary first molar and second premolar were developed using computed tomography images of four patients. For each patient, an MSI surface model derived from micro-computed tomography was placed at four different distances from the premolar PDL. Finite element analysis was conducted with mesial load on the MSI, increasing from 1 N to 4 N. Peak absolute compression stress (CS) was calculated at each 1 N step. Stepwise multiple regression modeling was conducted to explain compressive stress by proximity, load magnitude, and bone properties. RESULTS: The multiple regression model explained 83.47% of the variation of CS and included all three factors: proximity, load magnitude, and bone properties. The model expected significant interaction between the bone properties and load magnitude, implying that strong bone properties could be associated with significant increases in CS at small increases in load. CONCLUSIONS: To ensure the safety of adjacent roots, MSIs should be placed at least 1 mm from the roots. Assessment of alveolar bone properties is recommended when the use of MSI is intended, as some patients may present with strong bone properties and thereby a high risk of MSI-induced root resorption.
Analysis of Bag-of-n-grams Representation's Properties Based on Textual Reconstruction

Despite its simplicity, bag-of-n-grams sen- tence representation has been found to excel in some NLP tasks. However, it has not re- ceived much attention in recent years and fur- ther analysis on its properties is necessary. We propose a framework to investigate the amount and type of information captured in a general- purposed bag-of-n-grams sentence represen- tation. We first use sentence reconstruction as a tool to obtain bag-of-n-grams representa- tion that contains general information of the sentence. We then run prediction tasks (sen- tence length, word content, phrase content and word order) using the obtained representation to look into the specific type of information captured in the representation. Our analysis demonstrates that bag-of-n-grams representa- tion does contain sentence structure level in- formation. However, incorporating n-grams with higher order n empirically helps little with encoding more information in general, except for phrase content information.
Cooperative Target Tracking and Signal Propagation Learning Using Mobile Sensors

Target tracking refers to positioning mobile objects over time. The targets may be hospital patients, park visitors, mall shoppers, warehouse assets, etc. We consider a novel cooperative system to track targets, where a target carries low-cost RF tag which not only beacons its ID, but also receives and rebroadcasts beacons of tags within a certain hop away. Mobile sensors, equipped with localization and communication modules, are used to capture and forward the beacons to a server to track the targets. Such multi-hop approach greatly extends the sensing range of the mobile sensors, or equivalently, the beaconing range of the tags, leading to cost-effective deployment. We propose Mosent, a highly accurate multi-hop system using mobile sensors for target tracking. To account for complex signal propagation in different indoor and outdoor environment, we represent the received signal strength (RSS) matrix overcoming the assumption on propagation model. Given sensor locations, beacons detected by the sensors and RSS matrix, Mosent jointly considers temporal and spatial information to track targets using a modified particle filter. Mosent has an optional, independent and offline module to learn spatial signal propagation in terms of RSS matrix using cooperative mobile sensors equipped with beaconing transceivers. We have implemented Mosent and conducted extensive experiments. Our results show that Mosent achieves 4.37m and 9.46m tracking error in the campus and the shopping mall, respectively, which outperforms other state-of-the-art approaches with significantly lower tracking error (often by more than 30%).
A Solanum neorickii introgression population providing a powerful complement to the extensively characterized Solanum pennellii population

We present a complementary resource for trait fine-mapping in tomato to those based on the intra-specific cross between cultivated tomato and the wild tomato species Solanum pennellii, which have been extensively used for quantitative genetics in tomato over the last 20 years. The current population of backcross inbred lines (BILs) is composed of 107 lines derived after three backcrosses of progeny of the wild species Solanum neorickii (LA2133) and cultivated tomato (cultivar TA209) and is freely available to the scientific community. These S. neorickii BILs were genotyped using the 10K SolCAP single nucleotide polymorphism chip, and 3111 polymorphic markers were used to map recombination break points relative to the physical map of Solanum lycopersicum. The BILs harbor on average 4.3 introgressions per line, with a mean introgression length of 34.7 Mbp, allowing partitioning of the genome into 340 bins and thereby facilitating rapid trait mapping. We demonstrate the power of using this resource in comparison with archival data from the S. pennellii resources by carrying out metabolic quantitative trait locus analysis following gas chromatography-mass spectrometry on fruits harvested from the S. neorickii BILs. The metabolic candidate genes phenylalanine ammonia-lyase and cystathionine gamma-lyase were then tested and validated in F2 populations and via agroinfiltration-based overexpression in order to exemplify the fidelity of this method in identifying the genes that drive tomato metabolic phenotypes.
Searching on Encrypted E-Data Using Random Searchable Encryption (RanSCrypt) Scheme

Cloud computing is intensifying the necessity for searchable encryption (SE) for data protection in cloud storage. SE encrypts data to preserve its confidentiality while offering a secure search facility on the encrypted data. Typical index-based SEs in data sharing scenarios can effectively search secure keyword indexes. However, due to the smaller size of the keyword space, SEs using a public key are susceptible to a Keyword Guessing Attack (KGA) and other statistical information leakage. In this paper, for secure search in a data sharing scenario, we propose Random Searchable enCryption (RanSCrypt) that adds randomness to a transformed keyword to increase its space and aspires to make it irreversible. At the core of the mechanism, two keywords are garbled with randomness, still enabling another party to determine if the two garbled keywords (RanSCrypt’s terms REST and Trapdoor) are the same or not without knowing the actual keywords. As SE in a public key setting suffers from vulnerability to KGA, RanSCrypt transfers into a symmetric key setting with minimum overhead and without losing the features of a data sharing scenario. RanSCrypt also adulterates the search result to add perplexity and provides full control of access only to the data receiver. The receiver can cull out the erroneous results from the search result locally. Finally, we introduce a new type of attack on SE, namely, the Keyword Luring Attack (KLA), and show that RanSCrypt is safe from KLA attack due to adulteration of the result. Our security analysis proves RanSCrypt is invulnerable against KGA and leaks no information.
Source Coding Options to Improve HEVC Video Streaming in Vehicular Networks

Video delivery in Vehicular Ad-hoc NETworks has a great number of applications. However, multimedia streaming over this kind of networks is a very challenging issue because (a) it is one of the most resource-demanding applications; (b) it requires high bandwidth communication channels; (c) it shows moderate to high node mobility patterns and (d) it is common to find high communication interference levels that derive in moderate to high loss rates. In this work, we present a simulation framework based on OMNeT++ network simulator, Veins framework, and the SUMO mobility traffic simulator that aims to study, evaluate, and also design new techniques to improve video delivery over Vehicular Ad-hoc NETworks. Using the proposed simulation framework we will study different coding options, available at the HEVC video encoder, that will help to improve the perceived video quality in this kind of networks. The experimental results show that packet losses significantly reduce video quality when low interference levels are found in an urban scenario. By using different INTRA refresh options combined with appropriate tile coding, we will improve the resilience of HEVC video delivery services in VANET urban scenarios.
Diet, Microbiota and Gut-Lung Connection

The gut microbial community (Gut microbiota) is known to impact metabolic functions as well as immune responses in our body. Diet plays an important role in determining the composition of the gut microbiota. Gut microbes help in assimilating dietary nutrients which are indigestible by humans. The metabolites produced by them not only modulate gastro-intestinal immunity, but also impact distal organs like lung and brain. Micro-aspiration of gut bacteria or movement of sensitized immune cells through lymph or bloodstream can also influence immune response of other organs. Dysbiosis in gut microbiota has been implicated in several lung diseases, including allergy, asthma and cystic fibrosis. The bi-directional cross-talk between gut and lung (termed as Gut-Lung axis) is best exemplified by intestinal disturbances observed in lung diseases. Some of the existing probiotics show beneficial effects on lung health. A deeper understanding of the gut microbiome which comprises of all the genetic material within the gut microbiota and its role in respiratory disorders is likely to help in designing appropriate probiotic cocktails for therapeutic applications.
Using Online Economic News to Predict Trends in Brazilian Stock Market Sectors

In this paper, we investigate the impact of online economic news on BM&FBOVESPA, the leading stock exchange in Brazil. We collected economic news published in high circulation online Brazilian newspapers between 2000 and 2015 and extracted, from each news, the reader comments, the amount of shares in social media sites and its associated sentiment. From this, we built several features that were used as input for machine learning models with the aim of predicting the trends on different sectors of BM&FBOVESPA concerning their average price, amount of trades, amount of contracts traded and financial volume. Our assumption is that the more predictable a sector is, based on these features, the more sensitive to news it is. Besides showing that sectors are indeed affected differently by news, we also show that the machine learning models employed performed better than random and other simpler baselines for all sectors, thus providing evidence that news data carry indeed an important signal for understanding BM&FBOVESPA dynamics.
Estimating selection on the act of inbreeding in a population with strong inbreeding depression

Inbreeding depression is widely regarded as a driving force in the evolution of dispersal, mate choice and sperm selection. However, due to likely costs of inbreeding avoidance, which are poorly understood, it is unclear to what extent selection to avoid inbreeding is expected in nature. Moreover, there are currently very few empirical estimates of the strength of selection against the act of inbreeding (mating with a relative), as opposed to the fitness costs of being inbred. Here, we use data from the individual‐based study of red deer on the Scottish island of Rum, a strongly polygynous system which harbours a large inbreeding load, to estimate selection against the act of inbreeding for each sex. We use pedigree and genomic estimates of relatedness between individuals and measure fitness using both lifetime breeding success (number of calves born) and lifetime reproductive success (number of calves surviving to independence), with the latter incorporating inbreeding depression in calf survival. We find for both sexes that the repeatability of the act of inbreeding was low (< 0.1), suggesting little among‐individual variation for this trait on which selection can act. Using the genomic measures, there was significant selection against the act of inbreeding in males, but not in females, and there was considerable uncertainty in the estimate in both sexes. We discuss possible explanations for these patterns and their implications for understanding the evolution of inbreeding avoidance in natural populations.
Multi-Source Stego Detection with Low-Dimensional Textural Feature and Clustering Ensembles

This work tackles a recent challenge in digital image processing: how to identify the steganographic images from a steganographer, who is unknown among multiple innocent actors. The method does not need a large number of samples to train classification model, and thus it is significantly different from the traditional steganalysis. The proposed scheme consists of textural features and clustering ensembles. Local ternary patterns (LTP) are employed to design low-dimensional textural features which are considered to be more sensitive to steganographic changes in texture regions of image. Furthermore, we use the extracted low-dimensional textural features to train a number of hierarchical clustering results, which are integrated as an ensemble based on the majority voting strategy. Finally, the ensemble is used to make optimal decision for suspected image. Extensive experiments show that the proposed scheme is effective and efficient and outperforms the state-of-the-art steganalysis methods with an average gain from 4 % to 6 % .
Magnetic Navigation of a Rotating Colloidal Swarm Using Ultrasound Images

Microrobots are considered as promising tools for biomedical applications. However, the imaging of them becomes challenges in order to be further applied on in vivo environments. Here we report the magnetic navigation of a paramagnetic nanoparticle-based swarm using ultrasound images. The swarm can be generated using simple rotating magnetic fields, resulting in a region containing particles with a high area density. Ultrasound images of the swarm shows a periodic changing of imaging contrast. The reason for such dynamic contrast has been analyzed and experimental results are presented. Moreover, this swarm exhibits enhanced ultrasound imaging in comparison to that formed by individual nanoparticles with a low area density, and the relationship between imaging contrast and area density is testified. Furthermore, the microrobotic swarm can be navigated near a solid surface at different velocities, and the imaging contrast show negligible changes. This method allows us to localize and navigate a microrobotic swarm with enhanced ultrasound imaging indicating a promising approach for imaging of microrobots.