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The traditional constant time window-based waveform classification method is a robust tool for seismic facies analysis. However, when the interval thickness is seismically variable, the fixed time window is not able to contain the complete geologic information of interest. Therefore, the constant time window-based waveform classification method is inapplicable to conduct seismic facies analysis. To expand the application scope of seismic waveform classification in the strata with varying thickness, we have proposed a novel scheme for unsupervised seismic facies analysis of variable window length. The input of the top and bottom horizons can guarantee the comprehensive geologic information of the target interval. Throughout the whole workflow, we use the dynamic time warping (DTW) distance to measure the similarities between seismic waveforms of different lengths. First, we improve the traditional spectral clustering algorithm by replacing the Euclidean distance with the DTW distance. Therefore, it can be applicable in the interval of variable thickness. Second, to solve the problem of large computation when applying the improved spectral clustering approach, we adopt the method of seismic data thinning based on the technology of the superpixel. We combine these two algorithms and perform the integrated workflow of improved spectral clustering. The experiments on synthetic data show that the proposed workflow outperforms the traditional fixed time window-based clustering algorithm in recognizing the boundaries of different lithologies and lithologic associations with varying thickness. The practical application shows great promise for reservoir characterization of interval with varying thickness. The plane map of waveform classification provides convincing reference to delineate reservoir distribution of the data set.

Light, especially daylight, plays a critical role in human health as the main timer for circadian rhythms. Indoor environments usually lack the correct exposure to daylight and are highly dependent on electric lighting, disrupting the circadian rhythm and compromising the health of occupants. The methodology proposed assesses the combination of natural and electric lighting on circadian rhythms for operational environments. The case study chosen examines a 24/7 laboratory area representing an open-plan shift-work area. Several electric lighting scenarios under different sky conditions have been assessed, considering a variable window size and resulting in a spectrum which establishes the indoor circadian regulation performance according to the amount of light perceived. A set of configurations is presented to determine optimal electric lighting configuration based on natural light conditions in order to ensure a suitable circadian stimulus and the electric lighting flux threshold for different scenarios, benefiting occupants’ health while also ensuring energy conservation.

In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.

Simultaneous sources acquisition, also referred to as “blended acquisition”, involves recording two or more shots simultaneously. It allows for denser spatial sampling and can greatly speed up the field data acquisition. Thus, it has potential advantage to improve seismic data quality and reduce acquisition cost. In order to achieve the goal of blended acquisition, a deblending procedure is necessary. It attenuates the interference and thus improves the resolution of the pre-stack time migration image. In this paper, we propose an efficient deblending method, which applies frequency-varying median and mean filters to cross-spread azimuth-offset gathers (XSPR-AO). The method can be used with variable window sizes according to the characteristics of the interference. The effectiveness of the method is validated by a field data example.

Accurate tree density and location are important information for optimizing the management and production of forest. Combination of remote sensing techniques and local maximum (LM) filtering algorithm provides a feasible approach to individual tree crown detection, but still faces high error under complicate canopy structure. In this study, a revised LM (RLM) algorithm is presented and evaluated for identifying individual trees from four high spatial-resolution images. Instead of a moving window technique, the RLM algorithm finds crown center seeds by searching local maximal in the transects along row and column directions of the image. Each of final crown centers is then searched using a variable window centered at the crown center seed. Strategies for splitting and merging crowns are implemented in the RLM algorithm to reduce false detection. Result showed that accuracy of the RLM algorithm was more sensitive to its minimum crown length parameter (CLmin). The RLM algorithm driven by the CLmin estimates achieved high overall accuracies between 85% and 91% and low commission (9–14%) and omission errors (8–15%) for the four images. Splitting and merging strategies implemented in the RLM algorithm effectively reduced commission and omission errors. These results indicate that the RLM algorithm is a feasible method with well-defined parameters for automatically detecting individual trees with satisfactory detection accuracy.

In this article scan statistics for detecting a local change in variance for two dimensional normal data are discussed. When the precise size of the rectangular window where a local change in variance has occurred is unknown, multiple and variable window scan statistics are proposed. A simulation study is presented to evaluate the performance of the scan statistics investigated in this article via comparison of power. A method for estimating the rectangular region where a change in variance has occurred and the size of the change in variance is also discussed.

Stereo vision finds a wide range of applications in areas of computer and machines, such as providing three dimensional perceptions, imitating humans. The crucial correspondence problem faced by most visual systems can be resolved by Stereo matching algorithms to a great extent. Broadly, the matching algorithms are categorized as area based and feature based. Area Based technique involves correlation based matching which is relatively fast and provides a dense disparity map. In this paper, some significant techniques involved in area based matching are discussed. This paper describes the computational optimization strategy, which is based on a very effective pixel selection scheme. Normalized Cross Correlation (NCC) has the advantages of accuracy, strong robustness, but speed is its weakness, so online application is restricted. This method uses novel pixel selection schemes where disparity map produced using NCC is modified in such a way that provides accurate and computationally efficient results. Finally, we provide experimental results obtained by the proposed methods on variable window sizes; we compare these data with those obtained using a well-known, fast, area-based algorithm SAD. The results show that the proposed method is computationally faster than the conventional NCC scheme.

The aquatic ecosystems are dynamic environments often affected directly or indirectly by a myriad of anthropogenic contaminants that need to be properly identified. In this study, liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QqTOF-MS) suspected-screening was applied to mussels and riverine sediment both, non-spiked and spiked with a mixture of 32 pharmaceuticals. Three data acquisition methods –sequential window acquisition of all theoretical fragment-ion spectra (SWATH), in fix (FSWATH) and variable (VSWATH) window modes and Information Dependent Acquisition (IDA)– were compared to determine the most suitable acquisition technique. The results obtained in the spiked samples showed that the two SWATH modes enable to obtain the MS/MS spectrum of a higher number of compounds (up to 27 with FSWATH and 25 with VSWATH) than IDA (up to 19) in sediment and mussel. The different data acquisition modes were also tested in non-spiked samples to verify the results obtained in the spiked ones. Importantly, all the methods are able to detect the MS/MS spectrum of several contaminants in the samples when analysed against a database of >600 compounds. Up to 7 contaminants were tentatively detected with IDA, 15 with FSWATH and 17 with VSWATH. Most pollutants were pesticides and pharmaceuticals, being of particular interest the presence of ibuprofen and acetaminophen in mussels., This work has been supported by the Spanish Ministry of Science, Innovation and Universities and the ERDF (European Regional Development Fund) through the project Eco2RISK-DDS (CGL2015-64454-C2-1) and by the Generalitat Valenciana through the project ANTROPOCEN@ (PROMETEO/2018/155).

Variation in network characteristics introduces jitter to the propagating voice packets. Jitter hampers voice quality and makes the VoIP call uncomfortable to the user. Often buffers are used to store the received packets for a short time before playing them at equal spaced intervals to minimize jitter. Choosing optimum buffering time is essential for reducing the added end-to-end delay and number of discarded packets. In this chapter, some established adaptive jitter buffer playout algorithms have been studied and a new algorithm has been proposed. Further studies have been conducted for finding the optimum sliding window size for the proposed algorithm. The designed algorithm kept jitter within tolerable limit along with significant reduction of delay and loss compared to other algorithms.

The process of embryo implantation is carried out during the receptive stage of the endometrium in the midluteal phase of the menstrual cycle, known as window of implantation (WOI). It has been assumed that the WOI is not a constant variable in all women and the determination of its displacement is of crucial importance, especially for patients with recurrent implantation failure (RIF). Furthermore, in rare cases it could have different duration and position in the menstrual cycle even in the same woman but during different periods. Here, we report a 37-year-old woman with RIF, who was previously classified as idiopathic but has now been diagnosed as having a variable WOI. This interpretation was done after the performance of immunohistochemical and histomorphological analyses of endometrial biopsies taken in the midluteal phase during three sequential menstrual cycles. In order to solve the problem with pinpointing a variable WOI, a specific type of embryo transfer, called mixed double embryo transfer (MDET), was done where one Day 3 and one Day 5 good quality embryos were transferred simultaneously 7 days after ovulation. A viable single pregnancy was confirmed by ultrasound scan and a healthy girl was born. This case showed a unique approach in overcoming the problem in RIF patients with variable WOI.

We present a modified w avelength-resolved S 2 m ethod t hat a llows r ecovery of highly-dispersive modes. We employed this method using a variable window spectrogram to characterize a hollow core photonic crystal fiber where several surface-modes are recovered.

In order to solve the drawback of SNM algorithm, which leads to inefficiency in detecting precision on approximately duplicate records when multiple sources need to be integrated, the improved SNM algorithm based on the variety of edit distance and the variable window is proposed in this paper. According to the record pattern of edit distance and the size of edit distance, the approximately duplicate records of data are deleted to reduce the number of comparisons. Then we put forward the mechanism of variable window to solve the problem. Empirical results show that this algorithm can effectively solve the problem that the window is too big or too small to leak. The experiment shows that the improved SNM algorithm can solve many problems in the integration of multi-source, and it has obvious advantages in ensuring precision and boosting efficiency.

Impulsive noise usually has adverse effect on image processing. It has been proved that adaptive median filter is effective to impulsive noise removal. However, high percentage impulsive noise is still a challenge to adaptive median filter. Thus, a sequential median filter is proposed in this paper. Adaptive median filter and median filter are combined in sequence to handle high percentage impulsive noise. Moreover, variable window sizes are employed in the sequential median filter. Experimental results indicate that the sequential median filter is effective for high percentage impulsive noise removal.

A simple and efficient new method for QRS detection in Electrocardiogram (ECG) is proposed in this paper. Initially data is preprocessed using two stage median filter for removing baseline drift. The second stage enhances the peaks of ECG wave components by using sixth power of signal. The next stage identifies the QRS complex by taking a variable window size. The detection sensitivity (Se) and positive predictivity (+P) of CSE (Common Standards for Quantitative Electrocardiography) measurement database, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) Arrhythmia database, European ST-T database and QT database are Se 99.51 & +P 99.69%, Se 99.21 & +P 99.34%, Se 99.53 & +P 99.72% and Se 99.87 & +P 99.95% respectively. These four standard databases used to perform QRS detection considered 368 cases, tested 1,006,168 beats and achieved overall average sensitivity 99.52% and positive predictivity 99.69%. The MIT/BIH Noise Stress Test Database also tested by proposed method.

Air pollution is a major problem in modern cities and developing countries. Fine particulate matter (PM2.5) is a growing public health concern and become the most serious air pollution. In this study, we formulate the PM2.5 inference problem in conventional environmental sensors as a sequence-to-sequence problem. We adopt the encoder-decoder LSTM (Long short term memory) framework to solve the PM2.5 inference problem. A novel width-variable window attention mechanism is proposed for the encoder-decoder LSTM system. The proposed method learn the position and width of the attention window simultaneously. The proposed method is evaluated on large scale data and the experimental results show that it achieves better performance on two datasets with different concentration of PM2.5.

Bölgesel yöntem tabanlı stereo eşleşme probleminde, sabit pencereli metotların değişken pencereli metotlarla karşılaştırıldığı zaman, gürültülerden ve sahne içeriklerinden dolayı genellikle daha kötü sonuçlar verdikleri gözlemlenmektedir. Bu gözlemlerden faydalanarak bu tez çalışmasında, stereo eşleşme probleminde maliyetlerin toplamı adımı için yeni bir değişken pencere yöntemi önerilmiştir. Stereo görüntüler ve gerçek fark haritaları üzerindeki gözlemlere dayanarak derinlik değişimlerinin görüntülerin kenar noktalarında olduğu belirtilmiştir. Bu sebepten dolayı önerilen yöntem, kenar bilgisine dayalı pencere boyutunu belirlemek ve görüntünün değişken olmayan alanlarını eşleştirmek üzere tasarlanmıştır. Önerilen kenar tabanlı değişken pencere metodu, çeşitli pencere boyutlarındaki mutlak farkların toplamı (MFT) yöntemiyle, Middlebury stereo veri kümesi üzerinde test edilmiştir. Elde edilen sonuçları değerlendirmek için kötü piksel yüzdesi yöntemi kullanılmıştır. Kötü piksel yüzdesi yöntemi kullanılarak benzetim sonuçları değerlendirildiği zaman, önerilen kenar tabanlı değişken pencere metodu yönteminin çeşitli pencere boyutlarındaki mutlak farkların toplamı yönteminden daha iyi sonuçlar verdiği gösterilmiştir., In the case of the local method based stereo matching problem, it is observed that the fixed window methods are generally less accuracy compared to the variable window methods. The result come from image noise and the scene contents. By taking advantage of these observations, in this thesis, a new variable window method is proposed for the cost (support) aggregation step in the stereo matching problem. Based on observations from stereo images and ground truth disparity map, it is stated that the depth changes are at the edges of the images. For this reason, the proposed method is designed to determine the window size based on edge information and match non-variable fields of view. The proposed edge-based variable window method has been compared on the Middlebury stereo dataset with the sum of absolute differences (SAD) method in different window sizes. Poor pixel percentage method was used to evaluate the results obtained. When simulated results are evaluated using the bad pixel percentage method, it has been shown that the proposed edge based variable window method gives better results than the sum of absolute differences in various window sizes.

The Fourier-Bessel (FB) series expansion with a variable window length is introduced to determine the number of multi-component linear frequency modulated (LFM) signals, and to separate the signal components. This method is mainly tackled into two stages, with the first being a coarse component number detection, which is determined as the number of pinnacles obtained by the fractional Fourier transform (FrFT). Secondly, a fine number determination of close-frequency components is obtained by adopting a large window length in FB series expansion, and the individual components are separated and reconstructed by several corresponding FB coefficients. Simulation results indicate that the proposed method outperforms the Radon transform with the time-frequency (TF) analysis methods in component number determination correctness, individual component separation accuracy, and robustness in low SNR.

This paper presents the journal relative impact (JRI), an indicator for scientific evaluation of journals. The JRI considers in its calculation the different culture of citations presented by the Web of Science subject categories. The JRI is calculated considering a variable citation window. This citation window is defined taking into account the time required by each subject category for the maturation of citations. The type of document considered in each subject category depends on its outputs in relation to the citations. The scientific performance of each journal in relation to each subject category that it belongs to is considered allowing the comparison of the scientific performance of journals from different fields. The results obtained show that the JRI can be used for the assessment of the scientific performance of a given journal and that the SJR and SNIP should be used to complement the information provided by the JRI. The JRI presents good features as stability over time and predictability.

Faced with the prevalence of multi-spatial resolution satellite data sets, selecting data with an appropriate resolution has become a new problem. This paper analyses the significance of scale selection of remote sensing images and discusses a geo-statistics based method for selecting the optimal spatial resolution of a remote sensing image. Breaking through the limitation of traditional average local variance (ALV), the modified ALV method based on variable window size and variable resolution is proposed to quantitatively select the optimal spatial resolution of a remote sensing image. In order to verify the validity of this method and interpret the relationship among spatial resolution, local variance and classification accuracy, this paper gives further image classification experiments with different spatial resolution. The experimental results show that the trend of classification accuracy along spatial resolution is basically accordant with that of modified ALV, which means that the image classification accuracy of the optimal resolution image is basically higher than those of others. Therefore, the modified ALV method for quantitively selecting the optimal spatial resolution of remote sensing image has theoretical and instructional meaning to a certain extent.