Minimum Window Size Research Articles

Quad-port multiservice integrated optically transparent automotive antenna for vehicular classification applications

The demand for vehicular antennas increases in tandem with the need for multiple features in automobiles. The development of optically transparent antenna (OTA) has made it possible to deploy antennas on delicate surfaces such as glass. Earlier studies on OTA demonstrated its viability using materials such as transparent conducting oxides (TCO) and conductive polymers. A tri-band OTA is proposed in this paper for vehicular applications. The antenna operates at 1.8 GHz, 2.4 GHz and 3.39–12 GHz bands, covering automotive/wireless applications such as GSM, Bluetooth, Wi-Fi, vehicular communication and electronic toll collection. The proposed OTA is developed on soda lime glass, and the material TCO is used for the radiator and the ground plane. The antenna prototype is tested on windshield and in an anechoic chamber, the gain and efficiency are found to be greater than 1 dBi and 80%, respectively. Furthermore, a machine learning technique for vehicle classification is proposed, which could help in electronic toll collection, automatic vehicle identifier, and parking management applications. The presented algorithm achieves 80% classification accuracy with a minimum window size.

DFA as a window into postural dynamics supporting task performance: does choice of step size matter?

Introduction: Detrended Fluctuation Analysis (DFA) has been used to investigate self-similarity in center of pressure (CoP) time series. For fractional gaussian noise (fGn) signals, the analysis returns a scaling exponent, DFA-α, whose value characterizes the temporal correlations as persistent, random, or anti-persistent. In the study of postural control, DFA has revealed two time scaling regions, one at the short-term and one at the long-term scaling regions in the diffusion plots, suggesting different types of postural dynamics. Much attention has been given to the selection of minimum and maximum scales, but the choice of spacing (step size) between the window sizes at which the fluctuation function is evaluated may also affect the estimates of scaling exponents. The aim of this study is twofold. First, to determine whether DFA can reveal postural adjustments supporting performance of an upper limb task under variable demands. Second, to compare evenly-spaced DFA with two different step sizes, 0.5 and 1.0 in log2 units, applied to CoP time series. Methods: We analyzed time series of anterior-posterior (AP) and medial-lateral (ML) CoP displacement from healthy participants performing a sequential upper limb task under variable demand. Results: DFA diffusion plots revealed two scaling regions in the AP and ML CoP time series. The short-term scaling region generally showed hyper-diffusive dynamics and long-term scaling revealed mildly persistent dynamics in the ML direction and random-like dynamics in the AP direction. There was a systematic tendency for higher estimates of DFA-α and lower estimates for crossover points for the 0.5-unit step size vs. 1.0-unit size. Discussion: Results provide evidence that DFA-α captures task-related differences between postural adjustments in the AP and ML directions. Results also showed that DFA-α estimates and crossover points are sensitive to step size. A step size of 0.5 led to less variable DFA-α for the long-term scaling region, higher estimation for the short-term scaling region, lower estimate for crossover points, and revealed anomalous estimates at the very short range that had implications for choice of minimum window size. We, therefore, recommend the use of 0.5 step size in evenly spaced DFAs for CoP time series similar to ours.

Improving word embedding quality with innovative automated approaches to hyperparameters

AbstractDeep learning practices have a great impact in many areas. Big data and significant hardware developments are the main reasons behind deep learning success. Recent advances in deep learning have led to significant improvements in text analysis and classification. Progress in the quality of word representation is an important factor among these improvements. In this study, we aimed to develop word2vec word representation, also called embedding, by automatically optimizing hyperparameters. Minimum word count, vector size, window size, negative sample, and iteration number were used to improve word embedding. We introduce two approaches for setting hyperparameters that are faster than grid search and random search. Word embeddings were created using documents of approximately 300 million words. We measured the quality of word embedding using a deep learning classification model on documents of 10 different classes. It was observed that the optimization of the values of hyperparameters alone increased classification success by 9%. In addition, we demonstrate the benefits of our approaches by comparing the semantic and syntactic relations between word embedding using default and optimized hyperparameters.

Improved Accuracy of Cardiac Tissue-Level Simulations by Considering Membrane Resistance as a Cellular-Level Optimization Objective.

Cardiac cellular models are utilized as the building blocks for tissue simulation. One of the imprecisions of conventional cellular modeling, especially when the models are used in tissue-level modeling, stems from the mere consideration of cellular properties (e.g., action potential shape) in parameter tuning of the model. In our previous work, we put forward an accurate framework in which membrane resistance (Rm) reflecting inter-cellular characteristics, i.e., electrotonic effects, was considered alongside cellular features in cellular model fitting. This paper, for the first time, examines the hypothesis that considering Rm as an additional optimization objective improves the accuracy of tissue-level modeling. To study this hypothesis, after cellular-level optimization of a well-known model, source-sink mismatch configurations in a 2-dimensional model are investigated. The results demonstrate that including Rm in the optimization protocol yields a substantial improvement in the relative error of the critical transition border which is defined as the minimum window size between source and sink that wave propagates. Model developers can utilize the proposed concept during parameter tuning to increase the accuracy of models.

Natural lighting in the premises of public institutions situated at the ground floors of buildings

The article deals with the typical problems of natural lighting in the public premises of different types situated at the ground floors of buildings, in which shops, social institutions, banks and post offices, etc. can be located. The problems of natural lighting of such premises are associated with the use of windows as a screen for various types of information - from advertising billboards to the display of goods, etc. In this case, the efficient area of windows is reduced dramatically. This leads to a decrease in the values of the coefficient of daylight and, consequently, to an increase in the cost of electricity aimed at constant additional interior lighting. The authors suggest an optimum method of balancing the informative and lighting performance of window openings in the premises in question. The results of field studies and decisions about the internal illumination are given to confirm the minimum acceptable window size required to maintain both satisfactory interior lighting and minimum power consumption.

Saturation Throughput and Delay Performance Evaluation of the IEEE 802.11g/n for a Wireless Lossy Channel

Non-ideal channel conditions degrade the performance of wireless networks due to the occurrence of frame errors. Most of the well-known works compute the saturation throughput and packet delay for the IEEE 802.11 Distributed Coordination Function (DCF) protocol with the assumption that transmission is carried out via an ideal channel (i.e., no channel bit errors or hidden stations), and/or the errors exist only in data packets. Besides, there are no considerations for transmission errors in the control frames (i.e., Request to Send (RTS), Clear to Send (CTS), and Acknowledgement (ACK)). Considering the transmission errors in the control frames adds complexity to the existing analysis for the wireless networks. In this paper, an analytical model to evaluate the Medium Access Control (MAC) layer saturation throughput and packet delay of the IEEE 802.11g and IEEE 802.11n protocols in the presence of both collisions and transmission errors in a non-ideal wireless channel is provided. The derived analytical expressions reveal that the saturation throughput and packet delay are affected by the network size (n), packet size, minimum backoff window size (Wmin), maximum backoff stage (m), and bit error rate (BER). These results are important for protocol optimization and network planning in wireless networks.

Inbreeding and runs of homozygosity before and after genomic selection in North American Holstein cattle

BackgroundWhile autozygosity as a consequence of selection is well understood, there is limited information on the ability of different methods to measure true inbreeding. In the present study, a gene dropping simulation was performed and inbreeding estimates based on runs of homozygosity (ROH), pedigree, and the genomic relationship matrix were compared to true inbreeding. Inbreeding based on ROH was estimated using SNP1101, PLINK, and BCFtools software with different threshold parameters. The effects of different selection methods on ROH patterns were also compared. Furthermore, inbreeding coefficients were estimated in a sample of genotyped North American Holstein animals born from 1990 to 2016 using 50 k chip data and ROH patterns were assessed before and after genomic selection.ResultsUsing ROH with a minimum window size of 20 to 50 using SNP1101 provided the closest estimates to true inbreeding in simulation study. Pedigree inbreeding tended to underestimate true inbreeding, and results for genomic inbreeding varied depending on assumptions about base allele frequencies. Using an ROH approach also made it possible to assess the effect of population structure and selection on distribution of runs of autozygosity across the genome. In the simulation, the longest individual ROH and the largest average length of ROH were observed when selection was based on best linear unbiased prediction (BLUP), whereas genomic selection showed the largest number of small ROH compared to BLUP estimated breeding values (BLUP-EBV). In North American Holsteins, the average number of ROH segments of 1 Mb or more per individual increased from 57 in 1990 to 82 in 2016. The rate of increase in the last 5 years was almost double that of previous 5 year periods. Genomic selection results in less autozygosity per generation, but more per year given the reduced generation interval.ConclusionsThis study shows that existing software based on the measurement of ROH can accurately identify autozygosity across the genome, provided appropriate threshold parameters are used. Our results show how different selection strategies affect the distribution of ROH, and how the distribution of ROH has changed in the North American dairy cattle population over the last 25 years.

A self-adaptive back-off optimization scheme based on beacons probability prediction for vehicle Ad-Hoc networks

In order to improve the broadcast reception rates of beacon messages in vehicle ad-hoc networks, a conclusion that the relationship between collision probability and minimum contention window size and the relationship between expiration probability and minimum window size was reached by building a Markov model. According to this conclusion, a back-off algorithm based on adjusting the size of minimum contention window called CEB is proposed, and this algorithm is on the basis of the differential size between the number of expiration beacons and preset threshold. Simulations were done to compare the performance of CEB with that of RBEB and BEB, and the results show that the performance of the new proposed algorithm is better than that of RBEB and BEB.

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

With the growing popularity of information and communications technologies and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the advanced message queuing protocol. We provide a comprehensive analysis on the effect of adaptive and nonadaptive window size in segmentation of time series using SensorSAX and symbolic aggregate approximation (SAX) approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with three parameters, namely window size parameter of the SAX algorithm, sensitivity level, and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilized segmentation approach, due to the fact that each geographically different sensory environment has got a different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

Frequent Item Set Mining Using INC_MINE in Massive Online Analysis Frame Work

Frequent Pattern Mining is one of the major data mining techniques, which is exhaustively studied in the past decade. The technological advancements have resulted in huge data generation, having increased rate of data distribution. The generated data is called as a ‘data stream’. Data streams can be mined only by using sophisticated techniques. The paper aims at carrying out frequent pattern mining on data streams. Stream mining has great challenges due to high memory usage and computational costs. Massive online analysis frame work is a software environment used to perform frequent pattern mining using INC_MINE algorithm. The algorithm uses the method of closed frequent mining. The data sets used in the analysis are Electricity data set and Airline data set. The authors also generated their own data set, OUR-GENERATOR for the purpose of analysis and the results are found interesting. In the experiments five samples of instance sizes (10000, 15000, 25000, 35000, 50000) are used with varying minimum support and window sizes for determining frequent closed itemsets and semi frequent closed itemsets respectively. The present work establishes that association rule mining could be performed even in the case of data stream mining by INC_MINE algorithm by generating closed frequent itemsets which is first of its kind in the literature.

Robust Automated Step Extraction From Time-Series Contact Force Data Using the PDShoe.

This paper presents a method of stride identification, extraction, and analysis of data sets of time-series contact force data for ambulating subjects both with and without Parkinson's disease (PD). This method has been made robust with the use of seeded K-Means clustering, fast Fourier transformation (FFT) spectral analysis, and minimum window size rejection. These methods combine to produce well selected strides of active walking data. We are able to calculate quality of walking measures of stride duration, stance duration (as percent of gait cycle - %GC), swing duration (%GC), time to maximum heel force (%GC), time to maximum toe force (%GC), time spent in heel contact (%GC), and time spent in toe contact (%GC).

Enhanced TCP Congestion Control with Higher Utilization in Under-Buffered Links

TCP Reno is not fully utilized in under-buffered links. We propose a new TCP congestion control algorithm that can utilize the link almost up to 100% except the first congestion avoidance cycle. Our scheme estimates the minimum congestion window size for full link utilization in every congestion avoidance cycle and sends extra packets without touching TCP Reno congestion control. It has the same RTT fairness and the same saw-tooth wave as TCP Reno does. Our scheme does not affect competing TCP Reno flows since it uses only unused link capacity. We provide a simple mathematical modeling as well as ns-2 simulation results which show that the link utilization is improved by up to 19.88% for k=1/8 against TCP Reno when the buffer is k times the optimal buffer size. We claim that our scheme is useful for transmitting large amount of data in under-buffered links.

An Adaptive Approach for the Progressive Integration of Spatial and Spectral Features When Training Ground-Based Hyperspectral Imaging Classifiers

The use of hyperspectrometers as analytical tools for determining surface material properties in ground-based applications introduces the need of integrating spatial and spectral hyperspectral cube components. A neural-network-based approach is presented in this paper with the aim of automatically adapting to the spatiospectral characteristics of samples in a problem domain so that the most efficient classification can be obtained. Its main application would be in inspection and quality control tasks. The system core is an Artificial Neural Network-based hyperspectral processing unit able to perform the online classification of the material based on the spatiospectral patterns provided by a set of pixels. A training adviser is implemented to automate the determination of the minimum spatial window size, as well as the optimum spectrospatial feature set leading to the desired classification in terms of the available ground truth. Several tests have been carried out on synthetic and real data sets. In particular, the proposed approach is used to discriminate samples of synthetic and real materials, where the pixel resolution implies that a material is characterized by spectral patterns of combinations of pixels.

The pre-AD 585 extension of the U.S. Southwest archaeomagnetic reference curve

A set of 104 independently dated archaeomagnetic directions was used to extend the U.S. Southwest reference curve back to 375 cal BC and to calculate isolated mean VGPs centered on 960 cal BC and 2390 cal BC. Prior to this study, most U.S. Southwest reference curves extended to only ca. AD 585. This study employed Sternberg’s moving window technique with variably sized windows, rather than fixed windows, to smooth the dataset into a continuous curve. The size of each averaging window was determined by the density of data captured by the window, such that each window had a minimum data density of 5.0 and a minimum window size of 50 years. This approach differs from previous studies in the U.S. Southwest, which have applied a uniformly sized averaging window to a dataset regardless of the temporal distribution of the data.

Multi-Fractal Analysis for Feature Extraction from DNA Sequences

This paper presents estimations of multi-scale (multi-fractal) measures for feature extraction from deoxyribonucleic acid (DNA) sequences, and demonstrates the intriguing possibility of identifying biological functionality using information contained within the DNA sequence. We have developed a technique that seeks patterns or correlations in the DNA sequence at a higher level than the local base-pair structure. The technique has three main steps: (i) transforms the DNA sequence symbols into a modified Lévy walk, (ii) transforms the Lévy walk into a signal spectrum, and (iii) breaks the spectrum into sub-spectra and treats each of these as an attractor from which the multi-fractal dimension spectrum is estimated. An optimal minimum window size and volume element size are found for estimation of the multi-fractal measures. Experimental results show that DNA is multi-fractal, and that the multi-fractality changes depending upon the location (coding or non-coding region) in the sequence.

Accuracy analysis of the window overlapping technique for velocity measurements using artificial and real signals

The feasibility of using window overlapping processing technique together with cross-correlation method was investigated for measuring instantaneous particle velocities using a fiber optic system. A pair of artificial signal pulses was generated to yield a known velocity field when they were cross-correlated. A parametric analysis was conducted to determine whether the window overlapping technique enhanced the velocity sampling frequency of the fiber optic probe system. The window overlapping technique was found to be effective in improving the accuracy of the instantaneous velocity calculations for relatively shorter data segments (windows). As the length of the window size increased, the efficiency of the window overlapping method diminished. The window overlapping technique was found to be effective only for window sizes up to four times longer than the minimum window size. The minimum window size contains sufficient number of data points that corresponds to the maximum velocity sampling frequency according to the Nyquist Theory. That is, the window overlapping technique could be beneficial for enhancing the velocity sampling frequency only if the velocity data (or the window size used in the cross-correlation function) were sampled at a frequency of four times lower (or better) than the highest frequency in the flow. Due to the hardware limitations, it is not possible to sample data at frequencies high enough to capture the highest frequency velocity fluctuations in a turbulent flow. Therefore, in this study, it was investigated that the velocity sampling frequency of a fiber optic probe could be enhanced by window overlapping technique using data collected from a highly turbulent particle laden pipe flow. The results indicated that the window overlapping technique slightly improved the accuracy of the instantaneous particle velocity measurements. This is believed to be due to the larger window sizes (or low sampling frequencies) relative to the high frequency velocity fluctuations in the flow. Therefore, the particle velocity fluctuations may not be computed accurately using a fiber optic probe due to the nature of the cross-correlation method which filters the velocity variations during the time period of Δ T . In order to obtain reasonably accurate results for measuring the particle velocity fluctuations in real life applications, the time interval (window size) must be sufficiently small such that it corresponds to the frequencies, at most, four times lower than the highest frequency occurring in the flow. The feasibility of using window overlapping processing technique together with cross-correlation method was investigated for measuring instantaneous particle velocities using a fiber optic system. The efficiency of window overlapping technique for enhancing the velocity sampling frequency was evaluated using artificial signals and data obtained from a fiber optic probe ( Fig. 1 ) in a highly turbulent particle laden pipe flow.

Speckle effect on polarimetric target scattering decomposition of SAR imagery

Target scattering decompositions have become the standard tools for scattering classification and for target geophysical parameter extraction from polarimetric synthetic aperture radar (SAR) data. Incoherent decompositions are based on the characteristic decomposition of the target coherency matrix and the use of its eigenvalues and eigenvector parameters for a unique characterization of target scattering. The presence of speckle, however, may introduce biases to the incoherent parameters derived from the characteristic decomposition of the coherency matrix sample. In this study, it is shown that both coherency eigenvalues and eigenvector parameters estimates may be significantly biased, with a bias that decreases with an increase in the size of the coherency sample processing window. The statistics of cross-channel coherence we have previously derived for circular complex Gaussian processes can be used to determine the minimum processing window size required for unbiased estimation of the incoherent decomposition parameters. The speckle effect on coherent target decomposition is also investigated, and the implications for the use of coherent versus incoherent decomposition are discussed.

Flexible time-windows for advance reservation in LambdaGrids

Advance-reservation requests are an essential feature of LambdaGrids, where resources may need to be co-allocated at pre-determined times. In this paper, we discuss unconstrained advance reservations, which use flexible time-windows to lower blocking probability and, consequently, increase resource utilization. We claim and show using simulations that the minimum window size, which theoretically brings the blocking probability to 0, in a first-come-first-served advance reservation model without time-slots, equals the waiting time in a queue-based on-demand model. We also show, with simulations, the window sizes, which bring the blocking probability to its minimum, for an advance reservation model with time-slots.

Performance analysis of priority schemes for IEEE 802.11 and IEEE 802.11e wireless LANs

The IEEE 802.11 distributed coordination function (DCF) enables fast installation with minimal management and maintenance costs, and is a very robust protocol for the best effort service in wireless medium. However, the current DCF is unsuitable for real-time applications. This paper studies backoff-based priority schemes for IEEE 802.11 and the emerging IEEE 802.11e standard by differentiating the minimum backoff window size, the backoff window-increasing factor, and the retransmission limit. An analytical model is proposed to derive saturation throughputs, saturation delays, and frame-dropping probabilities of different priority classes for all proposed priority schemes. Simulations are conducted to validate analytical results. The proposed priority schemes can be easily implemented, and the results from this paper are beneficial in designing good priority parameters.

Neuro-computational processing of moving sonar echoes classifies and localizes foliage.

Echoes from in situ tree trunks, similar to those observed by flying bats, are processed. A moving sonar converts echoes into spike sequences and applies neural-computational methods to classify objects and estimate passing range. Two classes of tree trunks act as retro-reflectors that generate strong echoes (SEs), identified by a locally dense spike pattern. Linear drive-by sonar trajectories cause SEs to follow hyperbolic curves specified by passing range. A glint is a collection of consecutive range readings matching expected values on a specific hyperbolic curve. Passing-range detectors compare successive SE data with expected values in a table and tally coincidences. Counters increment when coincidences occur and decrement when they do not. A glint terminates after tallying a sufficient number of coincidences and coincidence failure occurs in the maximum-count detector. Reflector roughness, deviations in sonar trajectory, and echo jitter necessitate a coincidence window to define matches. Short windows identify small glints over piecewise linear sonar trajectories, while long windows accommodate deviations in sonar speed and trajectory, and associate multiple glints observed with shorter windows. The minimum coincidence window size yielding glints classify smooth and rough retro-reflectors.