Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Energy-Efficient Node Management in Wireless Sensor Networks

In sensor network-related researches, communication has been the most important target for reducing energy consumption. The frequencies in communication and sensing in a sensor node affect its energy efficiency. Therefore, the sensing cycle and the transmission cycle in a sensor node need to be managed to increase energy efficiency in communication. In this paper we present mechanisms to manage sensor nodes within a sensor network energy efficiently and describe their implementation.
Voltage dependence of calcium transport in the thick ascending limb of Henle's loop.

Thick ascending limbs of Henle's loop were dissected from rabbit kidneys and perfused in vitro. Unidirectional transepithelial calcium fluxes from lumen-to-bath and bath-to-lumen were measured with 45Ca using different solutions that caused the transepithelial voltage to vary over a wide range. With lumen-positive voltages there was net calcium absorption from lumen to bath which varied directly with the voltage. With voltage near zero there was no measurable net flux. When the voltage was made negative, the direction of net calcium transport reversed (i.e., secretion from bath to lumen). The presence or absence of bicarbonate in the lumen did not affect the calcium fluxes. Calcium permeability, calculated from the dependence of net flux on voltage, was 7.7 x 10(-6) cm/s, which is approximately 25% of the sodium permeability previously determined in this segment. Analysis of the calcium flux ratios revealed interdependence of the bidirectional fluxes consistent with single-file diffusion but no evidence for active calcium transport. We conclude that there is an important component of passive net calcium transport driven by the voltage in this segment.
Enhancing electrical conductivity of rubber composites by constructing interconnected network of self-assembled graphene with latex mixing

Vulcanized graphene/natural rubber composites with a conductive segregated network exhibiting good electrical conductivity, water vapor permeability and high mechanical strength are prepared by self-assembly in latex and static hot pressing. The composite exhibits a percolation threshold of ∼0.62 vol% and a conductivity of 0.03 S m−1 at a content of 1.78 vol%, which is ∼5 orders of magnitude higher than that of the composites made by conventional methods at the same loading fraction.
The Research of Measuring Approach and Energy Efficiency for HadoopPeriodic Jobs

Current consumption of cloud computing has attracted more and more attention of scholars. Hadoop as a cloud platform, the research about its energy consumption has also received considerable attentions from scholars. This paper presents a method to measure the energy consumption of jobs that run on Hadoop, and this method is used to measure the effectiveness of the implementation of periodic tasks on Hadoop platform. Combining with the current mainstream about energy estimate formula to conduct further analysis, this paper has reached a conclusion about how to reduce energy consumption of Hadoop by adjusting the split size or using appropriate size of workers (servers). Finally, experiments show the effectiveness of these methods of energy-saving and verify the feasibility of the method for measurement of periodic tasks at the same time.
Eclectic Rule-Extraction from Support Vector Machines

Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule- extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule- extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity.
A study for non-rigid 2x2D-3D registration of coronary artery images using bifurcation points matching with bi-plane X-ray fluoroscopy

The registration of preoperative 3D to intraoperative 2D images plays a crucial role in percutaneous coronary intervention. This technique reduces uncertainty regarding vessel topology and depth information in 2D intraoperative images. The aim of 2D/3D image registration is to determine the most appropriate image alignment. However, 2D/3D registration of heart images is challenging, due to differences in dimensionality and non-rigid motion, among others. In this paper, we propose a bifurcation-based biplane non-rigid 2D/3D registration method for coronary arteries. Experiments were performed in a virtual environment, and the results of the registration show an average distance with a ground truth of 0.57 mm.
Primal-Dual Strategy for Constrained Optimal Control Problems

An algorithm for efficient solution of control constrained optimal control problems is proposed and analyzed. It is based on an active set strategy involving primal as well as dual variables. For discretized problems sufficient conditions for convergence in finitely many iterations are given. Numerical examples are given and the role of the strict complementarity condition is discussed.
Delay-dependent H∞ filtering for nonlinear systems via T-S fuzzy model approach

This paper focuses on the problem of H∞ filter design for nonlinear systems with time-varying delays. A new approach of filter design is proposed. The present results may improve the existing ones due to a method to estimate the upper bound of the derivative of Lyapunov functional without ignoring some useful terms. A sufficient condition for the existence of such a filter is established by using the linear matrix inequality (LMI) approach. Finally, an example is given to demonstrate the effectiveness and the benefits of the proposed method.
Incremental Model Selection for Detection and Tracking of Planar Surfaces

Man-made environments are abundant with planar surfaces which have attractive properties and are a prerequisite for a variety of vision tasks. This paper presents an incremental model selection method to detect piecewise planar surfaces, where planes once detected are tracked and serve as priors in subsequent images. The novelty of this approach is to formalize model selection for plane detection with Minimal Description Length (MDL) in an incremental manner. In each iteration tracked planes and new planes computed from randomly sampled interest points are evaluated, the hypotheses which best explain the scene are retained, and their supporting points are marked so that in the next iteration random sampling is guided to unexplained points. Hence, the remaining finer scene details can be represented. We show in a quantitative evaluation that this new method competes with state of the art algorithms while it is more flexible to incorporate prior knowledge from tracking.
Integration detection research for high-speed and small target

Aiming at range migration in integration detection for high-speed and small target, the paper adopts parameter estimation and compensation in target detection. When estimating the target parameter, a new method of unambiguous Doppler frequency estimation is proposed, which resolves the problem of Doppler ambiguity, avoids the estimation of fuzzy factor, reduces the calculation quantity and lowers the demand of the input SNR at meantime. It is suitable for multi-target circumstance. Then, using of the estimated frequency of Doppler, the echo signal phase is compensated, without the influence of range migration. So this method can improve the detection performance of high-speed and small target greatly.
Sleep restriction and cognitive load affect performance on a simulated marksmanship task

Sleep restriction degrades cognitive and motor performance, which can adversely impact job performance and increase the risk of accidents. Military personnel are prone to operating under sleep restriction, and previous work suggests that military marksmanship may be negatively affected under such conditions. Results of these studies, however, are mixed and have often incorporated additional stressors (e.g. energy restriction) beyond sleep restriction. Moreover, few studies have investigated how the degree of difficulty of a marksmanship task impacts performance following sleep restriction. The purpose of the current experiment was to study the effects of sleep restriction on marksmanship while minimizing the potential influence of other forms of stress. A friend–foe discrimination challenge with greater or lesser degrees of complexity (high versus low load) was used as the primary marksmanship task. Active duty Soldiers were recruited, and allowed 2 h of sleep every 24 h over a 72‐h testing period. Marksmanship tasks, cognitive assessment metrics and the NASA‐Task Load Index were administered daily. Results indicated that reaction times to shoot foe targets and signal friendly targets slowed over time. In addition, the ability to correctly discriminate between friend and foe targets significantly decreased in the high‐cognitive‐load condition over time despite shot accuracy remaining stable. The NASA‐Task Load Index revealed that, although marksmanship performance degraded, participants believed their performance did not change over time. These results further characterize the consequences of sleep restriction on marksmanship performance and the perception of performance, and reinforce the importance of adequate sleep among service members when feasible.
On the photovoltaic effect in local field potential recordings

Abstract. Optogenetics allows light activation of genetically defined cell populations and the study of their link to specific brain functions. While it is a powerful method that has revolutionized neuroscience in the last decade, the shortcomings of directly stimulating electrodes and living tissue with light have been poorly characterized. Here, we assessed the photovoltaic effects in local field potential (LFP) recordings of the mouse hippocampus. We found that light leads to several artifacts that resemble genuine LFP features in animals with no opsin expression, such as stereotyped peaks at the power spectrum, phase shifts across different recording channels, coupling between low and high oscillation frequencies, and sharp signal deflections that are detected as spikes. Further, we tested how light stimulation affected hippocampal LFP recordings in mice expressing channelrhodopsin 2 in parvalbumin neurons (PV/ChR2 mice). Genuine oscillatory activity at the frequency of light stimulation could not be separated from light-induced artifacts. In addition, light stimulation in PV/ChR2 mice led to an overall decrease in LFP power. Thus, genuine LFP changes caused by the stimulation of specific cell populations may be intermingled with spurious changes caused by photovoltaic effects. Our data suggest that care should be taken in the interpretation of electrophysiology experiments involving light stimulation.
The application of software and safety engineering techniques to security protocol development

Security protocols are series of steps designed to achieve a particular aim, whilst also providing security functions. However, despite extensive research, protocols are still being designed which are susceptible to common flaws and attacks. Much of the research in this area has focused on protocol verification and, to a lesser extent, protocol design. Many comparisons have been made between software and safety and security engineering. This thesis builds on these comparisons in order to identify an approach to protocol development which reduces the likelihood of the occurrence of common flaws and attacks. This thesis proposes a process model for protocol development and justifies why such a model should be used to structure protocol development. One area that has often been overlooked in protocol research is that of the requirements of the security protocol. In this thesis, this area is investigated and, as a result, two techniques for the analysis and elicitation of security protocol requirements have been developed. The first technique is the Vulnerability Identification and Analysis (VIA) method. This is based on the HAZOP technique, which is successfully used in safety engineering, as well as a wide variety of other industries. The VIA method provides a structured approach to the deviation analysis of security protocol requirements, as well as the elicitation of further requirements. The second technique, Requirements Analysis and Elicitation (RAE) trees, extends the tree based analysis techniques which have been developed for the security field and draws on ideas from the safety analysis technique Fault Tree Analysis. The RAE trees are used to determine the causes of, and identify requirements to address, protocol vulnerabilities.
Cerebellum, temporal predictability and the updating of a mental model

We live in a dynamic and changing environment, which necessitates that we adapt to and efficiently respond to changes of stimulus form (‘what’) and stimulus occurrence (‘when’). Consequently, behaviour is optimal when we can anticipate both the ‘what’ and ‘when’ dimensions of a stimulus. For example, to perceive a temporally expected stimulus, a listener needs to establish a fairly precise internal representation of its external temporal structure, a function ascribed to classical sensorimotor areas such as the cerebellum. Here we investigated how patients with cerebellar lesions and healthy matched controls exploit temporal regularity during auditory deviance processing. We expected modulations of the N2b and P3b components of the event-related potential in response to deviant tones, and also a stronger P3b response when deviant tones are embedded in temporally regular compared to irregular tone sequences. We further tested to what degree structural damage to the cerebellar temporal processing system affects the N2b and P3b responses associated with voluntary attention to change detection and the predictive adaptation of a mental model of the environment, respectively. Results revealed that healthy controls and cerebellar patients display an increased N2b response to deviant tones independent of temporal context. However, while healthy controls showed the expected enhanced P3b response to deviant tones in temporally regular sequences, the P3b response in cerebellar patients was significantly smaller in these sequences. The current data provide evidence that structural damage to the cerebellum affects the predictive adaptation to the temporal structure of events and the updating of a mental model of the environment under voluntary attention.
Measurement and control system based on CC2530 for greenhouse

The Zigbee technology is one of the most important technologies in the control domain and has been increasingly applied in agricultural productions. For the sake of overcameing the shortcoming of the wired system such as complex wiring and low anti-interference capacity, a wireless greenhouse monitoring and control system based on CC2530 was designed, which is composed of upper monitor control PC, wireless gateway CC2530, sensor node CC2530 and sensor module. The accurate control is achieved by means of fuzzy Algorithm. The wireless sensor node is programmed by C language in IAR Embedded Workbench for MCS-51 Evaluation environment. The upper PC control system software is developed based on Visual C++ 6.0 platform. It is shown by experiments that data transmission in the system is accurate and reliable and the error of the temperature and humidity is below 4%, meeting the functional requirements for the greenhouse measurement and control system.
Object identification with tactile sensors using bag-of-features

In this paper, we present a novel approach for identifying objects using touch sensors installed in the finger tips of a manipulation robot. Our approach operates on low-resolution intensity images that are obtained when the robot grasps an object. We apply a bag-of-words approach for object identification. By means of unsupervised clustering on training data, our approach learns a vocabulary from tactile observations which is used to generate a histogram codebook. The histogram codebook models distributions over the vocabulary and is the core identification mechanism. As the objects are larger than the sensor, the robot typically needs multiple grasp actions at different positions to uniquely identify an object. To reduce the number of required grasp actions, we apply a decision-theoretic framework that minimizes the entropy of the probabilistic belief about the type of the object. In our experiments carried out with various industrial and household objects, we demonstrate that our approach is able to discriminate between a large set of objects. We furthermore show that using our approach, a robot is able to distinguish visually similar objects that have different elasticity properties by using only the information from the touch sensor.
Semi-supervised and active training of conditional random fields for activity recognition

Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs), a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB) algorithm for training CRFs – a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via minimum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. In a set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data.
Mycobacterium tuberculosis Beijing Genotype Is Associated with HIV Infection in Mozambique

The Beijing genotype is a lineage of Mycobacterium tuberculosis that is distributed worldwide and responsible for large epidemics, associated with multidrug-resistance. However, its distribution in Africa is less understood due to the lack of data. Our aim was to investigate the prevalence and possible transmission of Beijing strains in Mozambique by a multivariate analysis of genotypic, geographic and demographic data. A total of 543 M. tuberculosis isolates from Mozambique were spoligotyped. Of these, 33 were of the Beijing lineage. The genetic relationship between the Beijing isolates were studied by identification of genomic deletions within some Regions of Difference (RD), Restriction Fragment Length Polymorphism (RFLP) and Mycobacterial Interspersed Repetivie Unit – variable number tandem repeat (MIRU-VNTR). Beijing strains from South Africa, representing different sublineages were included as reference strains. The association between Beijing genotype, Human Immunodeficiency Virus (HIV) serology and baseline demographic data was investigated. HIV positive serostatus was significantly (p=0.023) more common in patients with Beijing strains than in patients with non-Beijing strains in a multivariable analysis adjusted for age, sex and province (14 (10.9%) of the 129 HIV positive patients had Beijing strains while 6/141 (4.3%) of HIV negative patients had Beijing strains). The majority of Beijing strains were found in the Southern region of Mozambique, particularly in Maputo City (17%). Only one Beijing strain was drug resistant (multi-drug resistant). By combined use of RD and spoligotyping, three genetic sublineages could be tentatively identified where a distinct group of four isolates had deletion of RD150, a signature of the “sublineage 7” recently emerging in South Africa. The same group was very similar to South African “sublineage 7” by RFLP and MIRU-VNTR, suggesting that this sublineage could have been recently introduced in Mozambique from South Africa, in association with HIV infection.
A single frequency full-duplex radio relay station for Frequency Domain Equalization systems

Full-duplex radio relay station using a single frequency for receiving and transmitting signals requires coupling wave (self-interference) canceller in order to avoid oscillation. In this paper, we propose a coupling wave cancellation scheme for the relay station in the FDE (Frequency Domain Equalization) system. The proposed relay station utilizes a blind coupling canceller composed by an actual antenna element and virtual coupling wave paths as each element of receiving adaptive antenna array, and controls the weights of the adaptive array so that the SNR (Signal-to-Noise Ratio) at the FDE output, which is virtually-placed after the adaptive array, is maximized. With the proposed approach, we can achieve the coupling wave cancellation and multiple desired signal reception, which increases the received desired signal power, without noise enhancement at the FDE of the destination terminal. The BER (Bit Error Rate) performance of the proposed method is demonstrated via computer simulations.
Functional Connectivity Measures in Memory Networks Using Independent Component Analysis

Memory function often appears to be compromised in several neurological pathologies and has been the subject of several studies within the field of neurosciences. The objective of this work was to explore methods for the analysis of functional connectivity of functional magnetic resonance imaging (fMRI) data, assessed by independent component analysis (ICA). Analysis was carried out using data from a previous study (Figueiredo et al. 2008) of memory function in normal subjects and patients with medial temporal lobe epilepsy and right hippocampal sclerosis. In order to do so, three different group ICA approaches were tested: tensorial group probabilistic ICA (PICA), group PICA with temporal concatenation approach and group ICA with temporal concatenation. Component sorting methods were developed and tested to assist in the selection of the components of interest. Overall, the tested ICA approaches yielded similar results with comparable networks, although, in general, the tensorial approach proved better in the detection of group differences, between normal controls and patients or between younger and older subjects.