Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive transmissions under a primary ARQ process via backward interference cancellation

In this work, we address the problem of the coexistence of Primary and Secondary Users (PU and SU, respectively) in a wireless network, where the PU employs a retransmission based error control technique (ARQ). This mechanism offers the SU a non trivial opportunity: by decoding the Primary Message (PM), the Secondary Receiver (SR) can perform interference cancellation during the whole primary ARQ window, thus enhancing its own outage performance. In particular, we investigate a Backward Interference Cancellation (BIC) mechanism: the SR buffers the secondary transmissions that underwent outage due to primary interference, and attempts to recover them once the knowledge about the PM becomes available due to decoding operation in a future instant. We present analytical results for the scenario where the primary ARQ process is limited to one retransmission, and show by numerical results the throughput benefit of BIC, over other techniques investigated in the literature.
Low Rate Sampling of Pulse Streams with Application to Ultrasound Imaging

Signals comprised of a stream of short pulses appear in many applications including bio-imaging, radar, and ultrawideband communication. Recently, a new framework, referred to as finite rate of innovation, has paved the way to low rate sampling of such pulses by exploiting the fact that only a small number of parameters per unit time are needed to fully describe these signals. Unfortunately, for high rates of innovation, existing approaches are numerically unstable. In this paper we propose a general sampling approach which leads to stable recovery even in the presence of many pulses. We begin by deriving a condition on the sampling kernel which allows perfect reconstruction of periodic streams of pulses from a minimal number of samples. This extends previous work which assumes that the sampling kernel is an ideal low-pass filter. A compactly supported class of filters, satisfying the mathematical condition, is then introduced, leading to a sampling framework based on compactly supported kernels. We then extend our periodic solution to finite and infinite streams, and show that our method is numerically stable even for a large number of pulses per unit time. High noise robustness is demonstrated as well when the time delays are sufficiently separated. Finally, we apply our results to ultrasound imaging data, and show that our techniques result in substantial rate reduction with respect to traditional ultrasound sampling schemes.
A Study on Generation of 3 D Model and Mesh Image of Excavation Work using UAV

Recently, advanced and semi-automated equipment is used for earthwork project. For automated and intelligent earthwork management, surveying method needs to be improved and updated to acquire and store on-site information. Unmanned Aerial Vehicles (UAVs) was considered as an important technology and using UAVs in various areas are prominent area in recent times. Unmanned Aerial vehicles (UAVs), aerial movable image sensing platform, have become the area that has been becoming notable in all industries. Miniature UAVs can measure the imagery taken form aerial and detect environment data. This study aims to determine the optimal algorithm based on meshed surface from arbitrary imagery acquired by UAVs and generate 3D model for analyzing and getting earthwork on site. Study results are aimed to generate ground control data based on imagery information in earthwork project.
Force control of a Scara robot using neural networks

This paper describes experimental results achieved by applying artificial neural networks (NNs) in trying to perform force/position control of a real Scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on feedback error learning architecture. Two NNs are used: one for the position loop control and another to the force loop control based on the structure known as the hybrid force/position control. The main purpose was to let the NNs compensate the dynamical effects that arises when a manipulator is in contact with an environment. Successful results have been achieved for the position loop control but practical problems were observed which are related to the force loop control using NNs even with different sets of input data for this NN. Practical problems are discussed.
Ontology Alignment for Classification of Low Level Sensor Data

In this work we show how alignment techniques can be used to align an ontology to a decision tree representing the features used in classification of sensor signals. The sensor data represents time-series data from an electronic nose when measuring bacteria in blood samples. The objective is to provide from the classification of these signals an estimate of the type of bacteria present in the sample. As these classification are inherently uncertain, knowledge about standard laboratory tests are used together with the classification result in order to determine a subset of tests to conduct that should result in a fast identification of the bacteria. The information about the laboratory tests are contained in an ontology. The result from the alignment is new classifier where recommendations are given to a user (expert) based on the interpretation of the sensor data that is done
Constituents of the Roots and Leaves of Ekebergia capensis and Their Potential Antiplasmodial and Cytotoxic Activities

A new triterpenoid, 3-oxo-12β-hydroxy-oleanan-28,13β-olide (1), and six known triterpenoids 2–7 were isolated from the root bark of Ekebergia capensis, an African medicinal plant. A limonoid 8 and two glycoflavonoids 9–10 were found in its leaves. The metabolites were identified by NMR and MS analyses, and their cytotoxicity was evaluated against the mammalian African monkey kidney (vero), mouse breast cancer (4T1), human larynx carcinoma (HEp2) and human breast cancer (MDA-MB-231) cell lines. Out of the isolates, oleanonic acid (2) showed the highest cytotoxicity, i.e., IC50’s of 1.4 and 13.3 µM against the HEp2 and 4T1 cells, respectively. Motivated by the higher cytotoxicity of the crude bark extract as compared to the isolates, the interactions of oleanonic acid (2) with five triterpenoids 3–7 were evaluated on vero cells. In an antiplasmodial assay, seven of the metabolites were observed to possess moderate activity against the D6 and W2 strains of P. falciparum (IC50 27.1–97.1 µM), however with a low selectivity index (IC50(vero)/IC50(P. falciparum-D6) < 10). The observed moderate antiplasmodial activity may be due to general cytotoxicity of the isolated triterpenoids.
The Evidence for Easy-Read for People With Intellectual Disabilities: A Systematic Literature Review

Producing accessible information for people with intellectual disabilities has been seen as a priority for the past 20 years. Easy-read resources are now widely available and several guidelines have been produced to support their development. However, little is known about the effectiveness of easy-read resources and the specific components that make it effective. A systematic review of the literature in electronic databases (Medline, Embase, BNI, CINAHL, HMIC, PsycINFO, ERIC, PubMed, and Cochrane Library) conducted between November 2013 and January 2014 yielded 11 publications that attempted to evaluate the impact of easy-read resources. The large variation in methodology among studies prevented a direct comparison of results; however, there were mixed findings concerning the impact of adding illustrations to written text on comprehension. A reader's level of familiarity with symbols emerged as an important factor, particularly with more abstract symbol systems that require some learning. Photographs and illustrations were generally found to be helpful, although it was acknowledged that these can be confusing and clear explanations are needed to ensure the correct message is conveyed. The format and level of difficulty of the text played an important role in the overall accessibility of information and particular linguistic features were associated with increased understanding. The methodological limitations of these studies were also considered and used to inform recommendations for future research. More attention needs to be focused on evaluating and distributing easy-read information, as well as producing it.
Discovering the Hidden Structure of a Social Network: A Semi Supervised Approach

Contagions such as information, rumors, infectious diseases, actions, and influence diffuse as cascades in large networks. Each contagion appears in some node and spreads through the nodes over the underlying network. In most cases, the network structure is hidden and we can only observe the times at which nodes are infected by contagions. So, inferring network structures and analyzing information diffusion processes are required in various domains. The vast majority of existing methods are parametric with the assumption that the diffusion patterns of contagions follow a particular distribution. In this paper, we propose a nonparametric method to infer the network topology, regardless of the contagion propagation model. We consider a static network and a set of given observed cascades, then, we try to infer not only the edges of the network; but also their strengths. First, we model the diffusion network as a Markov decision process (MDP), where the transition probabilities are assumed as pairwise transmission rates between the nodes. Later, a reinforcement learning paradigm is employed to solve this MDP. As compared to other methods, we do not make any assumption about the information diffusion pattern in our method; making it more general purpose. Experimental results on both synthetic and real datasets show that our method outperforms several state-of-the-art methods for different types of network structures and propagation models.
All-optical switching with bacteriorhodopsin protein coated microcavities and its application to low power computing circuits

We show all-optical switching of an input infrared laser beam at 1310 nm by controlling the photoinduced retinal isomerization to tune the resonances in a silica microsphere coated with three bacteriorhodopsin (BR) protein monolayers. The all-optical tunable resonant coupler re-routes the infrared beam between two tapered fibers in 50 μs using a low power (<200 μW) green (532 nm) and blue (405 nm) pump beams. The basic switching configuration has been used to design all-optical computing circuits, namely, half and full adder/subtractor, de-multiplexer, multiplexer, and an arithmetic unit. The design requires 2n−1 switches to realize n bit computation. The designs combine the exceptional sensitivities of BR and high-Q microcavities and the versatile tree architecture for realizing low power circuits and networks (approximately mW power budget). The combined advantages of high Q-factor, tunability, compactness, and low power control signals, with the flexibility of cascading switches to form circuits, and r.
Prototype and Exemplar-Based Information in Natural Language Categories

Two experiments are reported in which four dependent variables; typicality ratings, response times, category-naming frequencies, and exemplar-generation frequencies of natural language concepts, were predicted by two sorts of prototype predictors and by an exemplar predictor related to Heit and Barsalou’s (1996) instantiation principle. In the first experiment, the exemplar predictor was compared to a prototype predictor calculated as in Hampton (1979). The four dependent variables were either predicted better by the exemplar measure than by the prototype predictor or the predictive value was about equal. In the second experiment, a new prototype predictor was calculated based on Rosch and Mervis’ (1975) classic family resemblance measure. The results showed that the exemplar predictor accounted better for the dependent variables than Hampton’s and Rosch and Mervis’ prototype measures. The differences between the prototype measures were not significant. © 2000 Academic Press
NEW APPROACH IN SYMMETRIC BLOCK CIPHER SECURITY USING A NEW CUBICAL TECHNIQUE

Cryptography is a security technique that must be applied in both communication sides to protect the data during its transmission through the network from all kinds of attack. On the sender side, the original data will be changed into different symbols or shapes by using a known key; this is called encryption. On the other communication side, the decryption process will be done and the data will be returned to its former shape by using the agreed key. The importance of cryptography is to fulfil the communication security requirements. Real time applications (RTA) are vulnerable for the moment because of their big size. However, some of the current algorithms are not really appropriate for use with these kinds of information. In this paper, a novel symmetric block cipher cryptography algorithm has been illustrated and discussed. The system uses an 8x8x8 cube, and each cell contains a pair of binary inputs. The cube can provide a huge number of combinations that can produce a very strong algorithm and a long key size. Due to the lightweight and fast technique used in this idea, it is expected to be extremely rapid compared to the majority of current algorithms, such as DES and AES.
Speaker Identification Using Combined MFCC and Phase Information

Speaker recognition is the identification of the person who is speaking by the characteristics of their voices. To improve the performance of speaker recognition systems, an effective and robust method is proposed to extract speech features, capable of operating in noisy environment. For capturing the characteristics of the signal, the Mel-Frequency Cepstral Coefficients (MFCC) are calculated. Gaussian Mixture Models (GMMs) are used for the recognition stage as they give better recognition for the speakers' features. In conventional speaker recognition methods based on MFCC, phase information has been ignored. The proposed method integrated the phase information with MFCC on the speaker recognition method. Comparison of the proposed approach with the MFCCs conventional feature extraction method shows that the proposed method improves the recognition rate.
A simple setpoint controller for dynamic manipulation of biological cells using optical tweezers

Optical tweezers are one of the most common and useful tools in non-contact cell manipulation. While several control methods have been developed for cell manipulation using optical tweezers, the control input is commonly treated as the position of the laser beam and open-loop controllers are designed to move the laser source. Investigating the interaction between the robotic manipulator of laser source and biological cells can help us gain understanding into the dynamic manipulation problem using optical tweezers. However, the interaction between the cell dynamics and the manipulator dynamics leads to a fourth-order overall dynamics, and the use of high-order derivatives of the state variables is usually required in the overall control input. In this paper, a simple setpoint control method is proposed for optical manipulation of biological cells. The proposed method is able to manipulate the trapped cell to a desired position without using the high-order derivatives of the state variables such as acceleration and jerk. The stability of closed-loop system is analyzed by using LaSalle's invariance principle, with the consideration of the dynamics of both the cell and the robotic manipulator. The proposed control method is simple and easy to implement. Both simulation and experimental results are presented to illustrate the performance of the proposed control method.
Soot Reference Materials for instrument calibration and intercomparisons: a workshop summary with recommendations

Abstract. Soot, which is produced from biomass burning and the incomplete combustion of fossil and biomass fuels, has been linked to regional and global climate change and to negative health problems. Scientists measure the properties of soot using a variety of methods in order to quantify source emissions and understand its atmospheric chemistry, reactivity under emission conditions, interaction with solar radiation, influence on clouds, and health impacts. A major obstacle currently limiting progress is the absence of established standards or reference materials for calibrating the many instruments used to measure the various properties of soot. The current state of availability and practicability of soot standard reference materials (SRMs) was reviewed by a group of 50 international experts during a workshop in June of 2011. The workshop was convened to summarize the current knowledge on soot measurement techniques, identify the measurement uncertainties and limitations related to the lack of soot SRMs, and identify attributes of SRMs that, if developed, would reduce measurement uncertainties. The workshop established that suitable SRMs are available for calibrating some, but not all, measurement methods. The community of users of the single-particle soot-photometer (SP2), an instrument using laser-induced incandescence, identified a suitable SRM, fullerene soot, but users of instruments that measure light absorption by soot collected on filters did not. Similarly, those who use thermal optical analysis (TOA) to analyze the organic and elemental carbon components of soot were not satisfied with current SRMs. The workshop, and subsequent, interactive discussions, produced a number of recommendations for the development of new SRMs, and their implementation, that would be suitable for the different soot measurement methods.
Etanercept Attenuates Traumatic Brain Injury in Rats by Reducing Brain TNF-α Contents and by Stimulating Newly Formed Neurogenesis

It remains unclear whether etanercept penetrates directly into the contused brain and improves the outcomes of TBI by attenuating brain contents of TNF-α and/or stimulating newly formed neurogenesis. Rats that sustained TBI are immediately treated with etanercept. Acute neurological and motor injury is assessed in all rats the day prior to and 7 days after surgery. The numbers of the colocalizations of 5-bromodeoxyuridine and doublecortin specific markers in the contused brain injury that occurred during TBI were counted by immunofluorescence staining. Enzyme immunoassay for quantitative determination of TNF-α or etanercept in brain tissues is also performed. Seven days after systemic administration of etanercept, levels of etanercept can be detected in the contused brain tissues. In addition, neurological and motor deficits, cerebral contusion, and increased brain TNF-α contents caused by TBI can be attenuated by etanercept therapy. Furthermore, the increased numbers of the colocalizations of 5-bromodeoxyuridine and doublecortin specific markers in the contused brain tissues caused by TBI can be potentiated by etanercept therapy. These findings indicate that systemically administered etanercept may penetrate directly into the contused brain tissues and may improve outcomes of TBI by reducing brain contents of TNF-α and by stimulating newly formed neurogenesis.
Passive track fusion via hybrid coordinate filtering with recursive least squares estimator

An estimation fusion algorithm based on a group of hybrid coordinate (HC) filters is presented to the position and velocity estimation using angle-only measurements extracted from multiple maneuverable aircrafts with onboard passive sensor. The algorithm is a hierarchical architecture which consists of several local processors and a global processor. In each local processor, an extended Kalman (EK) filter utilizes the reference Cartesian coordinate (RCC) system for state and state covariance extrapolation and utilizes the modified spherical coordinate (MSC) system for state and state covariance updating. In the global processor, a recursive least squares (RLS) estimator sequentially computes a global estimate in the local inertial Cartesian coordinate (LICC) system. The estimator is developed by utilizing RCC updated state covariance to compute each filter weight for combining the outputs of local processors. The typical case of target motion analysis is investigated through computer simulation. In simulation study the proposed algorithm is compared to each HC EK filter. The EK filter encounters slow convergence problem under both circular arc and waving flight scenarios. By using a RLS estimator, the convergence is greatly accelerated. The algorithm markedly improves the tracking accuracy as well.
Character Recognition Based on PCANet

The character recognition is an important issue, which has been pursued in recent year. In the paper, we used PCANet(principal component analysis network) to learn the character features. We verified the influence of these parameters on the performance of PCANet by modifying the key parameters of the experiment. Then we made a handwritten dataset to do the experiment and to verify whether the PCANet can also be used to identify. And the results not only was okey but also the recognition rate can reach 95.56%. At last, we compared the experimental results with the experimental results on Caffe.
Particle Swarm Optimization for Sage Maximization Step in Channel Parameter Estimation

This paper presents an application of particle swarm optimization (PSO) in space alternating generalized expectation maximization (SAGE) algorithm. SAGE algorithm is a powerful tool for estimating channel parameters like delay, angles (azimuth and elevation) of arrival and departure, Doppler frequency and polarization. To demonstrate the improvement in processing time by utilizing PSO in SAGE algorithm, the channel parameters are estimated from a synthetic data and the computational expense of SAGE algorithm with PSO is discussed. (4 pages)
The Behavioral Immune System and Attitudes About Vaccines

The present research utilized evolutionary theory to examine the relation between the behavioral immune system (i.e., disgust sensitivity) and attitudes about vaccines. The findings from the studies suggest that higher levels of dispositional disgust sensitivity is predictive of more negative attitudes toward vaccines. These findings are consistent with several recent publications and thus have broad implications for public health research associated with vaccines. In Study 1, participants reporting higher dispositional disgust sensitivity (specifically, contamination disgust) tended to report more negative attitudes about vaccines. Study 2 replicated this result in a nonstudent sample using additional measures of disgust sensitivity more closely associated with aversion to perceived sources of contamination. Study 2 also revealed that beliefs about the likelihood of contracting illness in the future were unrelated to vaccine attitudes. Implications for the observed relation between intuitive aversion to contamination and vaccine attitudes are discussed.
EEDBACK A DAPTIVE C ONTROL OF A C LASS OF N ONLINEAR S YSTEMS U SING F UZZY A PPROXIMATORS

This paper describes the design of an adaptive direct control scheme for a class of nonlinear systems. The architecture is based on a fuzzy inference system (FIS) of Takagi Sugeno (TS) type to approximate a feedback linearization control law. The parameters of the consequent part of the fuzzy system are adapted and changed according to a law derived using Lyapunov stability theory. The asymptotic Lyapunov stability will be established with the tracking errors converging to a neighborhood of the origin. Finally, the adaptive direct fuzzy controller is applied in simulation to control three nonlinear systems.