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Control of continuous microwave heating system (CMHS) is truly a complex problem with time variance, uncertainty and nonlinearity, which becomes prohibitive to use a conventional model-based approach. To overcome this, a novel data-based optimal temperature tracking control is designed for CMHS in this paper. In order to obtain the complex dynamics of CMHS, a neural network model is first constructed driven by process data. After transforming the original temperature tracking problem into an error regulation problem, adaptive dynamic programming is introduced to deal with the regulation problem as well as to decrease operation cost. The design and operation of this controller depend mainly on the online data, and minor prior knowledge is required. Simulation results show that the proposed method can effectively control the CMHS in terms of temperature tracking and energy utilization.

Active fingerprinting is an effective penetration testing technique to know about vulnerability of hosts against security threats and network as a whole. Sometimes firewalls may block fingerprinting packets, hence making the probes infeasible. Measured Round Trip Time (RTTm) is a benign number that can be obtained from communication based on legitimate non malicious packets. In this paper, RTTm has been used along with other timers namely Smoothened Round-trip Time (SRTT), Round-trip Time Variance (RTTVar), Retransmission Time Out (RTO) and Scantime for pattern recognition and association analysis with the aid of cross-correlations. Experimental relationship among these timers are derived to back-up existing theoretical knowledge. A novel method to estimate IP-ID Sequence classes and network-traffic intensity based on these timers has been proposed. Results show that the model can be used to accurately derive (about 100% accuracy) active fingerprinting components IP-ID sequences and link traffic estimation. Analytical results obtained by this study can help in designing high-performance realistic networks and dynamic congestion control techniques.

We developed a general method to generate populations of artificial spike trains (ASTs) that match the statistics of recorded neurons. The method is based on computing a Gaussian local rate function of the recorded spike trains, which results in rate templates from which ASTs are drawn as gamma distributed processes with a refractory period. Multiple instances of spike trains can be sampled from the same rate templates. Importantly, we can manipulate rate-covariances between spike trains by performing simple algorithmic transformations on the rate templates, such as filtering or amplifying specific frequency bands, and adding behavior related rate modulations. The method was examined for accuracy and limitations using surrogate data such as sine wave rate templates, and was then verified for recorded spike trains from cerebellum and cerebral cortex. We found that ASTs generated with this method can closely follow the firing rate and local as well as global spike time variance and power spectrum. The method is primarily intended to generate well-controlled spike train populations as inputs for dynamic clamp studies or biophysically realistic multicompartmental models. Such inputs are essential to study detailed properties of synaptic integration with well-controlled input patterns that mimic the in vivo situation while allowing manipulation of input rate covariances at different time scales.

© 2018 IEEE. This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.

Background Recent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing – and hence the interpretation of measurement results – is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question. Method Our methodology is based on three pillars. The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts. The second pillar addresses the estimation of connectivity and directionality using three methods: nonlinear correlation coefficient (h2), general synchronization (H), and Granger causality (GC). The third pillar is related to signal classification and discrimination between pregnancy and labor using receiver operating curves (ROC) and connectivity and direction maps. For this purpose, we analyze the impact of four factors on data processing efficiency: i) method of connectivity detection, ii) effect of piecewise stationary segmentation preprocessing, iii) retained frequency content and iv) electrode configuration used for EHG recording (bipolar vs. unipolar). Results Our results show that piecewise signal segmentation and filtering considerably improves classification performance and statistical significance for some connectivity methods, in particular the h2. To this end we propose a new approach (detailed below) for h2 called Filtered-Windowed (FW) h2 that better highlights the differences between pregnancy and labor in the connectivity matrix and directionality maps. Conclusions This is the first comparative study of the effects of multiple processing factors on connectivity measurement efficiency. Our results indicate that appropriate preprocessing can improve the differentiation of pregnancy and labor-induced contraction signals and may lead to innovative applications in the prevention of preterm labor.

Recommender systems facilitate the users by providing the ample information of the items or the products they are interested. Users would not be aware of item details without the help of recommender systems due to the size of information available on the web. Collaborative filtering and content-based filtering are the two traditional filtering techniques of recommender systems. Both the filtering techniques have their advantages and certainly the disadvantages too. This can be solved by combining both the filtering techniques and improves the accuracy of recommendations. This leads to system as a hybrid recommender system. This paper presents a novel hybrid approach by combining a dynamic item-based collaborative filtering with the content-based filtering. Time variance and machine learning algorithms are applied on the filtering techniques to overcome the problems in recommendations. The approach is demonstrated using the MovieLens data sets to ensure the effectiveness of the proposed hybrid system.

The chapter presents a study on objective evaluation of a tanpura from the acoustic signals produced from it for the purpose of grading. The first part consists of perceptual grading of 23 tanpuras from signals produced through actual playing of tanpuras by expert tanpura players. These signals are perceptually evaluated by four different groups consisting of expert musicians, tanpura players, senior and junior scholars of music. The perception study consists of grading each tanpura into a four-point scale by individuals in each group. In the second part of the experiment spectral structure of each tanpura signal is studied and some parameters are selected for evaluating the musical quality. An objective evaluation is made using multiple regressions with these features and the subjective evaluation. The results show that an objective evaluation of tanpura from recorded signals conforming to the subjective evaluation is possible with 95% level of confidence. This chapter also intends to present an analysis of some selected spectral parameters for evaluating musical quality. The amount of energy present in the frequency band of 60 Hz–1 kHz and in the band 1–3 kHz are two features in the long-term average spectra over the whole cycle. To capture the gross time variance of spectral quality the whole length of the signal for one cycle is divided into 5 equal segments. The difference in amplitude (expressed in dB) between two largest peaks in the average power spectra for all these segments is also examined. For this purpose 4 tanpuras, 2 good and 2 bad selected through an elaborate perception experiment, are properly tuned and played by the professionals. The signals are digitized and the spectral analysis is done using standard software packages for spectral analysis. A comparison of the values of spectral parameters of individual string with those corresponding to the combined spectra of actual playing is also presented.

Self-organizing systems rely on positive feedback (amplification of perturbations). In particular, in swarm systems, positive feedback builds up in a transient phase until maximal positive feedback is reached and the system converges temporarily on a state close to consensus. We investigate two examples of swarm systems showing time-variant positive feedback: alignment in locust swarms and adaptive aggregation of swarms. We identify an influencing bias in the spatial distribution of agents compared to a well-mixed distribution and two features, percentage of aligned swarm members and neighborhood size, that allow us to model the time variance of feedbacks. We report an urn model that is capable of qualitatively representing all these relevant features. The increase in neighborhood sizes over time enables the swarm to lock in a highly aligned state but also allows for infrequent switching between lock-in states. We report similar occurrences of time-variant feedback in a second collective system to indicate the potential for generality of this phenomenon. Our study is concluded by applications of methods from renormalization group theory that allow us to focus on the neighborhood dynamics as scale transformations. Correlation lengths and critical exponents are determined empirically.

The ability to detect online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.

Detecting and isolating faults in cyber-physical systems (CPSs), e.g., critical infrastructures, smart buildings/cities, and the internet-of-things, are tasks that do generally scale badly with the CPS size. This work introduces a model-free fault detection and diagnosis system (FDDS) designed, having in mind scalability issues, so as to be able to detect and isolate faults in CPSs characterised by a large number of sensors. Following the model-free approach, the proposed FDDS learns the nominal fault-free conditions of the large-scale CPS autonomously by exploiting the temporal and spatial relationships existing among sensor data. The novelties in this paper reside in 1) a clustering method proposed to partition the large-scale CPS into groups of highly correlated sensors in order to grant scalability of the proposed FDDS, and 2) the design of model- and fault-free mechanisms to detect and isolate multiple sensor faults, and disambiguate between sensor faults and time variance of the physical phenomenon the cyber layer of CPS inspects.

We experience changes in stationarity/time variance in many practical applications. Since changes modify the operational framework the application is working with, its accuracy performance is in turn affected. When changes can occur, we need to detect them as soon as possible, in general by inspecting features extracted from data, and afterwards intervene to mitigate their effects. In this paper, we propose a novel change detection test based on the least squares density difference estimation. Neither assumptions about the distribution of features are needed, nor the change types are made (the method is pdf-free and can handle arbitrary changes.). What here proposed requires limited data to become operational and thresholds needed to assess the change can be set met to predefined false positive rates. We show through comprehensive experiments the effectiveness of the detection method and point out how it outperforms other related methods.

The control issue of underwater robots is very challenging due to the nonlinearity, time variance, unpredictable external disturbances, such as the sea current fluctuation, and the difficulty in accurately modeling the hydrodynamic effect. Conventional linear controllers may fail in satisfying performance requirements, especially when changes in the system and environment occur during the operation since it is almost impossible to manually retune the control parameters in water. Therefore, it is highly desirable to have an underwater robot controller capable of self-adjusting control parameters when the overall performance degrades. This paper presents the theory and experimental work of the adaptive plus disturbance observer (ADOB) controller for underwater robots, which is robust with respect to external disturbance and uncertainties in the system. This control scheme consists of disturbance observer (DOB) as the inner-loop controller and a nonregressor based adaptive controller as the outer-loop controller. The effectiveness of the ADOB was experimentally investigated by implementing three controllers: PID, PID plus DOB, and ADOB on an autonomous underwater robot, ODIN III.

In this paper, an identification method for industrial robots is described that does not require the a priori identification of the friction model. First, the necessity for such a method is motivated by an overview on conventional friction modelling and rigid body identification. It is shown that the time variance of typical friction characteristics lead to systematic identification errors. They are avoided by the presented method, which is based on separating the base parameters into three different groups. Each group is identified by simple measurements and a weighted least-squares method. Measurements are carried out with simple motions in the neighbourhood of a number of especially selected joint configurations. Further advantages of this method are its easy implementation for standard robot controls and the possibility to find modelling errors. The experimental implementation of this method to a typical industrial robot with six rotational joints is carried out and yields remarkable results.

This paper uses (linearized) Kalman filters to estimate first-order geometric parameters (i.e., orientation of contact normals and location of contact points) that occur in force-controlled compliant motions. The time variance of these parameters is also estimated. In addition, transitions between contact situations can be monitored. The contact between the manipulated object and its environment is general, i.e., multiple contacts can occur at the same time, and both the topology and the geometry of each single contact are arbitrary. The two major theoretical contributions are 1) the integration of the general contact model, developed previously by the authors, into a state-space form suitable for recursive processing; and 2) the use of the reciprocity constraint between ideal contact forces and motion freedoms as the “measurement equation” of the Kalman filter. The theory is illustrated by full 3-D experiments. The approach of this paper allows a breakthrough in the state of the art dominated by the classical, orthogonal contact models of Mason that can only cope with a limited (albeit important) subset of all possible contact situations.

In their never-ending search to improve manufacturing performance, companies try to eliminate all sources of waste. In this paper, we describe how the effects of the elimination of waste on performance measures such as lead time and overtime costs can be estimated using logistical simulation. This is illustrated using a real-life case study that was conducted in the repair department of a truck cabin factory. In this department, cabs are controlled and if necessary repaired. The first part of the paper describes how bottlenecks were determined for both the present layout and for a proposed layout change. This turned out to be difficult, as several product flows ‘intertwine’. Modelling was made complex by these interactions, as they caused deadlock phenomena. The second part of the paper represents a sensitivity analysis, which evaluates the effects of different types of waste, such as machine breakdowns, inadequate buffer size and production time variance.

An effective means for achieving high tracking accuracy is utilization of coarse and fine compound-axis structure in space laser communication. Firstly, this paper makes an optimization design of whole optical-path structure. Secondly, for enhancing energy density of beacon to make CCD work with high frame rate in the process of fine tracking, a technique is put forward, which uses a single beacon to switch between both different divergence angles. Thirdly, this paper designs an image processing unit of FTA (fine tracking assembly) based on FPGA, which adopts a sub-pixel subdivision technology. Finally, test results indicate that the tracking accuracy can be up to 1.2μrad in one time variance when pixel resolution is 3μrad, which meets the pointing accuracy requirement of space laser communications.

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical-based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti's keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user-representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent n-graphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.

We present an efficient pipeline for the segmentation and tracking of the Epicardium (Epi) and Left Ventricle (LV) from a set of 3D cardiac MRI sequences. The surface structure is handled as an array of planar active contours, interconnected between adjacent slices and frames, providing spatial and temporal consistency. In a given cardiac phase, the stacking of slice contours constitute a 3D mesh with a cylindrical topology. In a first stage the heart region is automatically localized by making use of the time variance of the heart region. Extraction of heart border is performed by means of energy minimization. Finally, we construct a 3D tensor array from all time surfaces in cylindrical coordinates and time and space consistency of the segmentation is enforced by fitting an m-variate tensor smoothing spline to the final 3D array.

We present a real-time variance based template matching system for spike sorting. The system employs an ultra-low-power CMOS variance estimator to detect and discriminate between distinct spike classes in real-time. Simulations demonstrate reliable single-unit detection at near 0 dB SNR, and robust classification of action potentials from noisy spike trains; measured results from the variance estimation circuit are in good agreement with simulations. This design thus represents a significant improvement in power efficiency over existing online sorting architectures, which is necessary ill order to achieve fully implantable neural prosthetics.

Many efficient wavelet domain estimation algorithms for noise reduction have been proposed in the literature. All of them are based on variance estimation, which require all wavelet coefficients to be saved in the memory thus causing constrains towards the development of real time system. Tarp filter has been used successfully for online/real-time variance estimation of the multilevel wavelets and has shown exceptional performance in image compression. This paper proposes the utilization of the Tarp filter for the estimation of wavelet coefficient variance without saving them in memory. The paper presents the results for Tarp-linear estimator (TLE) and compares it with simple linear estimator (LE). It is found that TLE, while having an advantage of memory free noise reduction method, compromise on SNR

This paper presents a supervisory system for controller switching as applied to ground-based and space-based deployable manipulator systems. First, a finite family of candidate controllers is established so that the manipulator system performs satisfactorily under the control of one of the controllers, in the possible presence of model uncertainties, unknown parameters, time variance, nonlinearities, and variable operating conditions. A supervisory unit in the system monitors the performance of the manipulator. Based on this, a decision-making logic unit selects an appropriate controller from the family, and activates it while deactivating the currently active controller, so as to ensure proper performance. Capabilities of instantaneous switching and gradual switching using a switching function and a switching compensator are incorporated into the system. The performance of the supervised switching control system is evaluated through computer simulation of an innovative manipulator system consisting of a combination of revolute and prismatic degrees of freedom and joint and link flexibilities.

Watermarks provide a means of embedding information into digital videos that can be used for a variety of purposes, such as establishing ownership, tracing origin of copies, and so forth. We outline an approach that permits a significant increase in the amount of information that can be accommodated in a watermark, namely time-variant watermarks. The approach is formulated assuming the video is represented in an MPEG format. Implementation issues of time-variant watermarks are discussed, as are their advantages over the usual time-invariant watermarks, with emphasis on defeating attacks using filtering, cropping, resizing, and other standard methods used to defeat watermarks, such as changing existing frames, as well as new attacks, such as removing, repeating, or permuting frames.

This paper proposes mixture hidden Markov models (HMM) with a time duration function to solve the recognition of Mandarin Chinese diphthongs and several words that resemble diphthongs. We propose an autoregression model to represent the dynamical relationships of observation symbols with time variance. The model can improve the weaknesses of standard HMM and nonstationary HMM.

Subjective word frequencies were obtained in order to determine the relationship between frequency and lexical decision times across a wide range of frequencies. The subjective ratings proved to be reliable across subject groups, even though they showed somewhat paradoxical range effects. While there were good correlations between the subjective and standard objective frequency counts across the entire frequency range, there was relatively little correlation between them for low-frequency words. Nevertheless, the subjective ratings accounted for more of the decision time variance for these low-frequency words than did the objective counts. Subjective frequency was therefore felt to be more appropriate for determining the frequency-latency relationship in lexical decision, particularly for low-frequency words. Using these ratings to supplement frequency counts, together with data from several lexical decision experiments, shows that the relationship between decision time and frequency is nonlinear, asymptoting for the highest frequencies, but with rapidly increasing reaction times for lower ones. B. Gordon's (1981, Lexical Access and Lexical Decision: Mechanisms of Frequency Sensitivity. Unpublished doctoral dissertation, Johns Hopkins University; 1983, Journal of Verbal Learning and Verbal Behavior, 22, 24–44) resonance model of lexical access can directly account for this relationship, but logogen and sequential search models require further elaboration to do so.

Reading times were collected for sentences in passages in order to examine how cognitive resources are distributed among different components of reading. Multiple regression analyses indicated that most of the reading time variance was predicted by macrostructure processing which integrates information from different sentences, as opposed to microstructure processing which includes the processing of words, syntax, and propositions. Experiment 1 revealed that slower readers require more time than faster readers to perform microstructure processing, but no differences were found for macrostructure components of reading. Experiment 2 revealed that variations in reading goals influence macrostructure processing but not microstructure processing. These findings suggest that functionally separate reading skills may be involved in microstructure versus macrostructure processing.

Performance on a signal detection task was explainable by differences between the two dimensional Fourier transforms of the background and target stimuli. A signal detection experiment by Marshak and Osarczuk (1984) used target and background stimuli designed to systematically differ in spatial frequency and orientation. They found that the hypothesized Fourier differences increased sensitivity and decreased decision time. The present paper reports the Fourier analysis of those stimuli which verify and quantify the stimulus manipulation. Multiple regressions were computed using differences in frequency and orientation to explain performance. The results were that 83 percent of d-prime and 74 percent of the decision time variance could be explained by the Fourier differences. These findings indicate that Fourier descriptions of symbols may be used to predict their effectiveness in work station environments.

Fundamental to increasing our understanding of the role of white matter microstructure in normal/abnormal function in the living human is the development of MR-based metrics that provide increased specificity to distinct attributes of the white matter (e.g., local fibre architecture, axon morphology, and myelin content). In recent years, different approaches have been developed to enhance this specificity, and the Tractometry framework was introduced to combine the resulting multi-parametric data for a comprehensive assessment of white matter properties. The present work exploits that framework to characterise the statistical properties, specifically the variance and covariance, of these advanced microstructural indices across the major white matter pathways, with the aim of giving clear indications on the preferred metric(s) given the specific research question. A cohort of healthy subjects was scanned with a protocol that combined multi-component relaxometry with conventional and advanced diffusion MRI acquisitions to build the first comprehensive MRI atlas of white matter microstructure. The mean and standard deviation of the different metrics were analysed in order to understand how they vary across different brain regions/individuals and the correlation between them. Characterising the fibre architectural complexity (in terms of number of fibre populations in a voxel) provides clear insights into correlation/lack of correlation between the different metrics and explains why DT-MRI is a good model for white matter only some of the time. The study also identifies the metrics that account for the largest inter-subject variability and reports the minimal sample size required to detect differences in means, showing that, on the other hand, conventional DT-MRI indices might still be the safest choice in many contexts.