Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncover the Power of Multipath : Detecting NLOS Drones Using Low-Cost WiFi Devices

In recent years, consumer UAV technology has seen considerable advances. Consumer UAVs have become an ideal vector for privacy invasions due to their affordability, size, maneuverability, and their ability to stream live high-quality video. There is considerable proliferation of drones in both civil and military domains. Hence it is critical to detect invading unmanned aerial vehicles (UAVs) or drones in a timely manner for both security and safeguarding privacy. Currently available solutions like active radar, video or acoustic sensors are very expensive (especially for individuals) and have considerable constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. The state of the arts in this direction mainly focus on detecting drones using line-of-sight (LOS) RF signals which are less noisy as compared to their non-LOS (NLOS) counterparts. To the best of our knowledge, there is no existing cost-effective solution for the general public to enable non-LOS(NLOS) detection for drone privacy invasion, which is the most common condition and it still remains an open challenge. This thesis research provides a low-cost UAV detection system for privacy invasion caused by customer drone. Our model supports NLOS detection with low-cost hardware under $50, and hence it is affordable for the general public to deploy in their house, apartments, and office. Our work utilizes inherent drone motions (i.e., body shifting and vibrations) as unique signatures for drone detection. Firstly, we validated the relationship between drone motions and RF signal under the NLOS condition using extensive experiments. This is motivated by the fact that under NLOS conditions slight changes to the position or motion of a drone could lead to dramatic change in multi-path components in received RF signals. The NLOS condition “amplifies” the RF signatures introduced by drone motions. We designed a deep learning model to capture the complex features from NLOS RF signals. In particular, we designed and trained a long short-term memory (LSTM) neural network [15, 27], a generative model which can effectively extract features of inputs for NLOS drone detection. Moreover, without knowing the presence of drones, our system starts with classifying any detected RF signals into LOS signals and NLOS signals before the NLOS drone learner is used. Classification of LOS and NLOS sig-
Safety-PLC ' s striking role for Partial Valve Stroke Testing

Partial Valve Stroke Testing or PVST, is an emerging concept to automatically increase the performance of Safety Instrumented Systems. PVST is a concept where safety-related valves like ESD valves and shut-off valves are automatically tested concerning failure modes that are related to valve sticking and slowing down operation. Current trends in the industry show an upcoming number of dedicated technical PVST solutions by various automation and instrumentation vendors. The added value of PVST within the process industries is a significant reduction of the frequency of required manual periodic valve proof tests, its related manual test cost and reduced spurious trips due to manual errors. Partial testing is performed by additional automated test instrumentation, which can easily be initiated and controlled by the safety-instrumented systems’ logic solver such as the safetyrelated PLC. This paper will discuss practical examples of Partial Valve Stroke Testing in which it appears that SIL 1 rated valves can be upgraded to SIL 2, and off-line proof test intervals which can be extended from 2 to 5 years.
A Fast Multipole Accelerated Singular Boundary Methodfor Potential Problems

The singular boundary method (SBM) is a recently-developed meshless boundary collocation method. This method overcomes the well-known fictitious boundary issue associated with the method of fundamental solutions (MFS) while remaining the merits of the later of being truly meshless, integral-free, and easy-to-program. Similar to the MFS, this method, however, produces dense and unsymmetrical coefficient matrix, which although much smaller in size compared with domain discretization methods, requires O(N2) operations in the iterative solution of the resulting algebraic system of equations. To remedy this bottleneck problem for its application to large-scale problems, this paper makes the first attempt to develop a fast multipole SBM (FM-SBM) formulation for two-dimensional (2D) potential problems. The proposed strategy can solve large-scale problems with several millions boundary discretization nodes on a desktop computer.
Safety-Oriented Planning and Control Using Danger Field

This paper presents a framework for planning and control for the safe behavior of the robotic manipulators. The framework exploits the relations between robot, human and the environment to measure safety via quantity called danger field. According to the degree of danger, the robot reacts promptly, improving the human safety. Above the danger field based control, there is a global path planner that does not only provide the collision-free paths but strives for safer ones.
hemical composition and seasonal variation of the volatile oils from rembleya phlogiformis leaves

Trembleya phlogiformis DC. , Melastomataceae, is a shrub whose leaves are used as a dye for dyeing wool and cotton. The present article aimed to carry out the morphological description of the species, to study the chemical composition of volatile oils from the leaves and flowers and the seasonal variability from the leaves during a year. Macroscopic characterization was carried out with the naked eye and with a stereoscopic microscope. Volatile oils were isolated by hydrodistillation in Clevenger apparatus and analyzed by gas chromatography/mass spectrometry. The major components of the volatile oil of T. phlogiformis flowers were: n-heneicosane (33.5%), phytol (12.3%), n-tricosane (8.4%) and linoleic acid (6.1%). It was verified the existence of a large chemical variability of the volatile oils from the leaves of T. phlogiformis over the months, with the majority compound (oleic alcohol, ranging from 5.7 to 26.8%) present in all samples. A combination of Cluster Analysis and Principal Component Analysis showed the existence of three main clusters, probably related to the seasons. The results suggested that the volatile leic alcohol oils of T. phlogiformis leaves possess high chemical variability, probably related to variation associated with rainfall and the variation in the behavior of specimens throughout the year. This research provides insights for future studies on the volatile oils obtained from the T. phlogiformis leaves and flowers, mainly related to biological markers of applications monitored in the leaves and flowers of this species. © 2017 Sociedade Brasileira de Farmacognosia. Published by Elsevier Editora Ltda. This is an open he CC access article under t
P o S ( M R U ) 0 3 5 Axes of discovery : The time variable Universe

As Heraclitus might have said, “You don’t observe the same un iverse twice. ” In modern times we recognize the time domain as an important dimension in the ov erall phase space of variables that characterizes the observable universe. Examples of time-d ep ndent sources abound across the electromagnetic spectrum and in non-photonic regimes (neu tri os, gravitational waves, cosmic rays). This paper discusses both known and speculative aspe cts of the radio transient sky, with an emphasis on discoveries that can be made with new, appropr iately designed instrumentation and telescopes. Known source classes include planets, flare stars, brown dwarfs, pulsars, magnetars, masers, supernovae and gamma-ray burst afterglows. H ypothetical classes include additional planetary emissions, ETI emissions (including radar signa ls to aid mitigation of orbital debris impacts), evaporating black holes, hyper-giant pulses from n eutron stars rotating near breakup, and prompt gamma-ray bursts. Nature undoubtedly has a longer li st! A generalized survey figure of merit is presented that takes into account the rate and dur ation of transient celestial events. The key for expanding discovery space is a wide field of view (F oV) combined with adequate sensitivity and high-resolution sampling in the combined t ime-frequency domain. Implementation of time-frequency processing is an integral part of syn optic survey modes for existing and forthcoming radio telescopes that can allow commensal obse rvations of steady sources (e.g. HI from galaxies) along with transient sources. Time-frequen cy processing is needed in all cases for calibration and excision of radio-frequency interference . The Square Kilometer Array (SKA), in particular, can be a powerful radio synoptic survey telesco pe (RSST) that has tremendous potential as a discovery instrument and for cross-wavelength and joi t photonic/non-photonic studies.
Audio based Music Classification based on Genre and Emotion using Gaussian Process

In the field of Music Information Retrieval (MIR), music genre classification and music emotion recognition are the two main tasks to investigate for further development. In this project work, these two tasks are focused. For this purpose, Gaussian Processes Model is used. Gaussian Processes (GPs) are Bayesian nonparametric models that are becoming more popular for their superior capabilities to capture highly nonlinear data relationships in various tasks, such as dimensionality reduction, time series analysis, novelty detection, as well as classical regression and classification tasks. Gaussian Processes are used to investigate the feasibility and applicability of Gaussian Process model for music genre classification and emotion estimation. Along with this, we are reducing the time required for feature extraction for classification tasks. Principle component analysis (PCA) technique is used to reduce this time. In this, it selects and considers only higher order features for classification.
Size-tuneable Nanometric MRI Contrast Agents for the Imaging of Molecular Weight Dependent Transport Processes

The purpose of the current study was to evaluate size-tuneable polymeric glycol-chitosan (GC)-DTPAGadolinium (Gd) conjugates as MRI contrast agents that can be used as a platform for imaging of molecular weight (MW) dependent transport processes. GC-DTPA-Gd conjugates of precisely controlled MWs were synthesised and evaluated in mice against Gd-DTPA using time series of high-resolution MRI images of trunk, head, and xenograft flank tumours. GC-DTPA modification ratio was one DTPA per 3.9-5.13 of GC monomers. GC-DTAP-Gd provided overall superior contrast compared to Gd-DTPA with the duration of the enhancement depending on MW (≥1hr for 40kD). The kidneys showed early enhancement also, particularly in the renal pelvis, suggesting renal elimination. Imaging of the head with GC-DTPA-Gd allowed detailed anatomical identification of specific blood vessels in particular with the high MW CA agent. Sequential, high-resolution, isotropic imaging of established A431 xenograft flank tumours with DTPA-Gd and GC-DTPA-Gd demonstrated that the initial distribution of the contrast agents was well correlated with blood vessels and supply. In contrast, subsequent tissue transport was primarily by diffusion and limited by CA molecular weight. The data also highlight the role of heterogeneity in CA distribution which was more prominent for the high MW agent. Precise control of glycol chitosan (GC) polymer chemistry facilitates synthesis of a family of Gd-based MRI contrast agents of tuneable MW but otherwise identical physicochemical properties. Such agents allow isotropic high-resolution threedimensional imaging of MW dependent transport processes relevant to the clinical and pre-clinical prediction of drug transport processes.
Title Selection of patients of hepatocellular carcinoma beyond theMilan criteria for liver transplantation

The Milan criteria have been proven to be reliable and easily applicable in selection of patients with small unresectable hepatocellular carcinomas for liver transplantation. It has been repeatedly shown that patients who met these criteria had a 5-year survival of over 70% after transplantation. Such a result is remarkably good for an otherwise incurable malignancy. The main disadvantage of this set of criteria is that it is rather restrictive. Following it religiously denies transplantation to many patients who have tumor stage slightly more advanced. There have been many attempts to extend the criteria to include tumors with larger sizes (as in the UCSF criteria) or with a larger number (as in the Kyoto criteria). Alpha-fetoprotein and PIVKA-II, two biological markers in more aggressive tumors, have also been employed in the selection of patients, and biopsies have been used by the University of Toronto to determine tumor aggressiveness before deciding on transplantation. Patients with tumors beyond the Milan criteria yet not of a high grade have been accepted for transplantation and their survival is comparable to that of transplant recipients who were within the Milan criteria. Preoperative dual-tracer (C-acetate and FDG) positron emission tomography has been used to determine tumor grade, and transarterial chemoembolization has been used to downstage tumors, rendering them meeting the Milan criteria. Patients with downstaged tumors have excellent survival after transplantation. Partial response to chemical treatment is a reflection of less aggressive tumor behavior. Careful selection of patients beyond the Milan criteria with the aid of serum tumor marker assay, positron emission tomography or tumor biopsy allows transplanting more patients without compromising survival. The use of liver grafts either from the deceased or from living donors could thus be justified.
Transcriptomics of Sugarcane Osmoprotectants Under Drought

Sugarcane (Saccharum spp.) is an alogamous plant from the Poaceae family and the Andropogoneae tribe (Daniels & Roach, 1987). This crop covers more than 23 million hectares worldwide, representing about 0.5 % of the total global area used for agriculture, with a production of 1.6 billion metric tons of crushable stems (FAOSTAT, 2009). Brazil is the world’s largest producer, contributing with two-thirds of total sugar production about 31 million tons per year of which 19.5 million tons are exported (UNICA, 2009). Sugarcane, its derivatives and by products have received great attention, due to their multiple uses, with emphasis on the ethanol production, representing an important renewable biofuel source. It has been estimated that sugarcane ethanol fuel may replace up to 10.0 % of the world’s refined petroleum products consumption in the next 15 to 20 years (Goldemberg, 2007). Despite its importance and similarity to other important agronomic crops, the sugarcane production has been adversely influenced by many environmental factors such as harsh climate and soil conditions. Abiotic stresses are among the main causes of losses in the productivity of the major crops worldwide (Bray et al., 2000), a scenario where drought figures as the most significant stress, causing negative impacts on crop adaptation and productivity. Besides, this condition can exacerbate the effect of other stresses (biotic or abiotic) to which the plants may be submitted. Although breeding activities have provided significant progress for the understanding of the physiological and molecular responses of plants to water deficit, there is still a large gap between yields in optimal and stressful conditions (Cattivelli et al., 2008). Essays regarding plant responses to drought stress have been published applying technologies of functional genomics (Wang et al., 2011). These evaluations provided important insights into molecular and biochemical mechanisms in the study of drought tolerance in various crops and model species. Plants are able to ‘‘perceive’’ the external stimuli by multiple sensors, recognizing adverse situations and invoking signal transduction cascades and consequently secondary messengers, activating stress responsive genes (Grennan, 2006), resulting in both molecular and physiological responses. Among the mechanisms developed by plants to face the adverse conditions generated under drought, the accumulation of osmoprotectants compounds are often recognized as a mitigation mechanism of the negative consequences of water deficit (Choluj et al., 2008).
The galactic abundance gradient from Cepheids - IV. New results for the outer disc

As a continuation of our previous work on the abundance gradient in the outer part of the galactic disc, this paper presents results on the metallicicty distribution over galactocentric distances up to 15 kpc. The outer disc is clearly separated from the middle part by the existence of a step in the metallicity distribution at about 10 kpc. Taking the region of galactocentric distances from 10 kpc to 15 kpc, one can derive an iron gradient $-0.03 \pm 0.01$ dex kpc -1 (25 stars). The existence of a discontinuity can be caused by the effective suppression of mixing processes near the corotation circle where the radial component of the gas velocity should be very small.
OF A MICROSCALE MAGNETOSTRICTIVE ASYMMETRIC THIN FILM BIMORPH ( μ MAB ) MICROROBOT

This paper presents the design, analysis, and performance results for a mobile microrobot that was designed for competing in the 2010 NIST Mobile Microrobot Challenge. Inspired by a crab-like microrobot driven by pulsating cardiomyocyte cells, an asymmetrically dimensioned magnetostrictive thin film bimorph microrobot has been designed. Utilizing the magnetostrictive principle, different bending and blocking forces occur under the robot’s feet due to the in-plane strain generated in the bimorphs by the application of external magnetic fields in the workspace of the microrobot. The differences in the resulting frictional forces drive the movement of the robot body. To calculate and simulate whether the feet of the robot can generate enough force for locomotion, the design was abstracted and translated into a piezoelectric cantilever FEM model. The results are consistent with the magnetostrictive theoretical equations. Microrobot fabrication and test-bed development based on this analysis is shown along with experimental results validating this approach. Finally, a discussion of the performance results and recommendations for future improvements are provided.
Effect of labour market information (LMI): comparison between independent and assisted use of LMI

According to a large meta-analysis (Brown and Ryan Krane, 2000), labour market information (LMI) is a critical ingredient in achieving career goals of individuals. This study aims to assess the effect of LMI use, with and without the help of a counsellor, taking into account the possible presence of differential effects according to client consultation needs (career choice or job search). Clients (n = 203) in job centres in New Brunswick and Saskatchewan were assigned randomly to two delivery modes: the independent self-help (use of LMI without help) or the assisted self-help (use of LMI with help of a counsellor). The results suggest that the LMI effect over time, although it is significant in both groups, is more important when participants are assisted by a counsellor. At the clinical level, this effect is moderate (Cohen, 1988). This result does not differ significantly according to the client consultation needs.
RNA Interference: A Recent Approach for the Remedy of Various Diseases

RNA Interference: A Recent Approach for the Remedy of Various Diseases RNA interference (RNAi) has been a recently discovered phenomenon for the analysis of regulation of gene expression in a variety of cells, by which target messenger RNA (mRNA) is cleaved by small interfering complementary RNA (siRNA). The technique is now established in in-vitrosystems, and much work is focussed on adapting RNAi for in vivoapplication. This technology may also be used for therapeutic purposes, functional genomics and target validation. This review will describe the basic biological processes that drive RNAi, indicate current approaches for the treatment of various diseases, and forecast future areas of development.
Development of Stochastic Daily Weather Generator Conditional on Atmospheric Circulation . Part 1 : Daily Precipitation Model ∗

We consider development of daily precipitation models for 31 sites in Bulgaria. The precipitation processes are modeled as a two-state first-order nonstationary Markov model with mixed transition density of a discrete component at zero and a continuous component describing non-zero amounts. Binary logistic regression is used to fit the occurrence data, and the intensity series is modeled by gamma distribution. Standard software for generalized linear models can be used to perform the computations. Detailed model validation is carried out on various aspects. The proposed model reproduces well the precipitation statistics for the observed and reserved data. PACS codes: 92.60. -e, 02.50. Ga
Topological & network theoretic approaches in hyperspectral remote sensing

Hyperspectral remote sensing is a valuable new technology that has numerous commercial and scientific applications. For example, it has been used to study crop health, mineral and soil composition, and pollution levels. Hyperspectral imaging also has important military and intelligence applications such as the identification of man-made materials, and detection of chemical and biological plumes. The key mathematical challenges of hyperspectral imaging include image classification, anomaly detection, and target detection. Image classification is the process of grouping pixels into spectrally similar clusters. This thesis describes a new topological and network-theoretic approach for classifying pixels in hyperspectral image data. Pixels in hyperspectral image data sets are thought of as constituting a point cloud in a high dimensional topological space, and a network structure is imposed on the data by considering the spectral distance between pairs of pixels. We use the tools of persistent homology to argue that the resulting network effectively models the complex nonlinear structures in the data. We then perform data clustering by applying a network based community detection algorithm called the method of maximum modularity. The method of maximum modularity is an unsupervised, deterministic method for detecting communities in networks where neither the number of communities nor their sizes needs to be specified in advance. Examples of real hyperspectral images that have been classified using the method of maximum modularity are provided in order to demonstrate the feasibility of the approach. Thesis Supervisor: Anthony A. Harkin Title: Associate Professor