Sliding Window Research Articles

Computing Minimal Unique Substrings for a Sliding Window

A substring u of a string T is called a minimal unique substring (MUS) of T if u occurs exactly once in T and any proper substring of u occurs at least twice in T. In this paper, we study the problem of computing MUSs for a sliding window over a given string T. We first show how the set of MUSs can change when the window slides over T. We then present an O(nlog sigma ')-time and O(d)-space algorithm to compute MUSs for a sliding window of size d over the input string T of length n, where sigma 'le d is the maximum number of distinct characters in every window.

Efficient online one-way traffic scheduling for restricted waterways

In Yangtze River traffic management, to manage ship sequencing and scheduling effectively and efficiently in restricted waterways has long been a challenging issue. This paper proposes a Sliding Window based Online ship Sequencing and Scheduling algorithm (OSS-SW) to tackle this problem. The OSS algorithm is capable of generating a more efficient ship sequence by introducing the ‘position shift’ concept which takes advantages of ships’ sailing time differences in the restricted waterway. The sliding window mechanism on the other hand is introduced to handle the traffic uncertainties and to reduce the computational complexity. The impact of the restricted waterway congestion on the performance of OSS-SW is also investigated. Further, the parameter settings of the proposed OSS-SW are investigated in detail. Both simulation studies and the real data applications confirm that the proposed OSS-SW algorithm outperforms the existing widely adopted Traffic Signal Revealing System (TSRS) and the Expert System based algorithm (FAHP-ES) in solving the ship sequencing and scheduling problem.

Zebra Crossing Detection and Time Scheduling Accuracy, Enhancement Optimization Using Artificial Intelligence

Zebra crossing detection is a fundamental function of the electronic travel aid. It can locate the zebra crossing and estimate its direction to help the visually impaired to cross the road safely. In contrast to the conventional methods, a regression approach is adopted to detect zebra crossing based on convolutional neural networks. Specifically, a fixed‐size window slides across the image captured at the intersection. The image patches are sequentially fed to the logistic regression model to identify the zebra crossing. Then the image patch of zebra crossing is fed to the regression model to predict the direction. The parameters of models are optimized by the ANN back propagation algorithm before predictions. Compared with existing methods, the proposed method can improve the precision‐recall performance of the zebra crossing identification and reduce the root mean square error of predicted directions.

EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and cubic SVM

Communication of the human brain with the surroundings became reality by using Brain- Computer Interface (BCI) based mechanism. Electroencephalography (EEG) being the non-invasive method has become popular for interaction with the brain. Traditionally, the devices were used for clinical applications to detect various brain diseases but with the advancement in technologies, companies like Emotiv, NeuoSky are coming up with low cost, easily portable EEG based consumer graded devices that can be used in various application domains like gaming, education etc as these devices are comfortable to wear also. This paper reviews the fields where the EEG has shown its impact and the way it has proved useful for individuals with severe motor disorder, rehabilitation and has become a means of communication to the real world. This paper investigates the use of Cubic SVM algorithm In the EEG classification. EEG feature extraction is Implemented by maximum overlap discrete wavelet transform (MODWT) to reduce the dimensionality of data. The Sliding Window Technique is used to calculate the mean within each window samples. The feature vectors are loaded into the support vector machine (SVM) and optimize tree.

EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

A Sliding-Window Principal Component thermography reconstruction approach for enhancement and identification of electronic components internal structure

Electronic supply chain vulnerability has been threatened by counterfeits for decades and as a result, the authentication and quality of Electronic Components (ECs) are highly valued by stakeholders for safety-critical industrial systems. Thermography Nondestructive Testing (NDT), offering a rapid inspection while covering a large area within a short time frame, is a promising technique to inspect the integrity of ECs. However, recognising the internal structural layout directly through a packaged EC from thermography images is still a challenge due to its sealed and cramped structure. In this research, we propose a Pulsed Thermography (PT) based data analysis method for enhancement and identification of the layout of die and lead frame in ECs. The proposed solution combines the Sliding Window (SW) concept, Thermographic Signal Reconstruction (TSR) and Principal Component Thermographic (PCT) technique, named SWPCT, to enhance the contrast of die, die substrate and lead frame. Compared to the conventional full-scale PT image analysis, the proposed method presents its superior sensitivity in small thermal features by reducing the spatial thermal information redundancy benefiting from the sliding window PCT analysis. It overcomes the structural thermograms obstruction from the thick packaging, reveals the hidden die and lead frame layout and suppresses the noise simultaneously. By selecting a proper window size, this paper reveals that SWPCT can reconstruct the position and dimension of die and die substrate in ECs, and spatially distinguish the lead frame layout. Two groups of typical OpAmps chip samples with internal structure diversity (X-ray observed) were tested using PT to demonstrate the effectiveness of the proposed method. Simulation data were also used to verify this approach. This research could unlock the challenge of thermography-based inspection for key internal structure in the encapsulated integrated components.

High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.

Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcription factor binding sites (TFBSs) with high resolution to promote biology and realize precision medicine. Recently, deep learning-based models have shown exemplary performance in the prediction of TFBSs at the base-pair level. However, the previous models fail to integrate nucleotide position information and semantic information without noisy responses. Thus, there is still room for improvement. Moreover, both the inner mechanism and prediction results of these models are challenging to interpret. To this end, the Deep Attentive Encoder-Decoder Neural Network (D-AEDNet) is developed to identify the location of TFs-DNA binding sites in DNA sequences. In particular, our model adopts Skip Architecture to leverage the nucleotide position information in the encoder and removes noisy responses in the information fusion process by Attention Gate. Simultaneously, the Transcription Factor Motif Discovery based on Sliding Window (TF-MoDSW), an approach to discover TFs-DNA binding motifs by utilizing the output of neural networks, is proposed to understand the biological meaning of the predicted result. On ChIP-exo datasets, experimental results show that D-AEDNet has better performance than competing methods. Besides, we authenticate that Attention Gate can improve the interpretability of our model by ways of visualization analysis. Furthermore, we confirm that ability of D-AEDNet to learn TFs-DNA binding motifs outperform the state-of-the-art methods and availability of TF-MoDSW to discover biological sequence motifs in TFs-DNA interaction by conducting experiment on ChIP-seq datasets.

SWiVIA – Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging

Controlling blending processes of solid material using advanced real-time sensing technologies tools is crucial to guarantee the quality attributes of manufactured products from diverse industries. The use of process analytical technology (PAT) tools based on chemical imaging systems are useful to assess heterogeneity information during mixing processes. Recently, a powerful procedure for heterogeneity assessment based on the combination of off-line acquired chemical images and variographic analysis has been proposed to provide specific heterogeneity indices related to global and distributional heterogeneity. This work proposes a novel PAT tool combining in situ chemical imaging and variogram-derived quantitative heterogeneity indices for the real-time monitoring of blending processes. The proposed method, so called sliding window variographic image analysis (SWiVIA), derives heterogeneity indices in real-time associated with a sliding image window that moves continuously until the full blending time interval is covered. The SWiVIA method is thoroughly assessed paying attention at the effect of relevant factors for continuous blending monitoring and heterogeneity description, such as the scale of scrutiny needed for heterogeneity definition or the blending period defined to set the sliding image window. SWiVIA is tested on blending runs of pharmaceutical and food products monitored with an in situ near-infrared chemical imaging system. The results obtained help to detect abnormal mixing phenomena and can be the basis to establish blending process control indicators in the future. SWiVIA is adapted to study blending behaviors of the bulk product or compound-specific blending evolutions.

Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series

Water level data obtained from telemetry stations typically contains large number of outliers. Anomaly detection and a data imputation are necessary steps in a data monitoring system. Anomaly data can be detected if its values lie outside of a normal pattern distribution. We developed a median-based statistical outlier detection approach using a sliding window technique. In order to fill anomalies, various interpolation techniques were considered. Our proposed framework exhibited promising results after evaluating with F1-score and root mean square error (RMSE) based on our artificially induced data points. The present system can also be easily applied to various patterns of hydrological time series with diverse choices of internal methods and fine-tuned parameters. Specifically, the Spline interpolation method yielded a superior performance on non-cyclical data while the long short-term memory (LSTM) outperformed other interpolation methods on a distinct tidal data pattern.

A Vision-Inertial Odometer Design Based on ORB and Sliding Window

In order to improve the positioning accuracy and system reliability of the visual odometer in outdoor environment, this paper proposes an unmanned vehicle positioning system using monocular camera and Inertial measurement unit (IMU). The system uses improved ORB algorithm to extract image feature points and establishes a tightly coupled visual-inertial odometer system based on sliding window and nonlinear optimization. Verified by experiment, it can adapt to the environment of changing light, and can complete the positioning task of unmanned vehicles in the outdoor environment.

Algorithm for Solving the Component Assignment Problem in a Multistate Sliding Window System

The multistate sliding window system (SWS) comprises [Formula: see text] multistate components arranged in a line; each group of [Formula: see text] consecutive multistate components is considered as a window. If the total performance rate in a window does not meet the predetermined demand [Formula: see text], then that window is regarded as a failure. The SWS fails if and only if there exists at least one failed window. Several researchers have considered the component assignment problem for the SWS with the aim of finding an appropriate component arrangement that maximizes system reliability. Such an arrangement is called the optimal arrangement. Although several metaheuristic and heuristic algorithms have been proposed, an exact algorithm for solving the component assignment problem of the SWS has not been developed thus far. Therefore, in this study, a branch-and-bound-based algorithm is developed to determine the optimal arrangement of the SWS efficiently. Furthermore, a recursive method is proposed to compute the system reliability. Combining the branch-and-bound-based algorithm with the recursive method enables reduction of the complexity of the reliability computations for determining the optimal arrangement. To investigate the efficiency of the branch-and-bound-based algorithm, numerical experiments were conducted; it was observed that the parameters [Formula: see text] and [Formula: see text] have the maximum effect on computation time, whereas parameter [Formula: see text] has minimal effect. The proposed algorithm is useful for improving the reliability of a practical system that can be expressed as an SWS. In addition, the optimal arrangements can be used to measure the heuristic and metaheuristic performances because they guarantee global optimality.

The Sliding Window and SHAP Theory—An Improved System with a Long Short-Term Memory Network Model for State of Charge Prediction in Electric Vehicle Application

The state of charge (SOC) prediction for an electric vehicle battery pack is critical to ensure the reliability, efficiency, and life of the battery pack. Various techniques and statistical systems have been proposed in the past to improve the prediction accuracy, reduce complexity, and increase adaptability. Machine learning techniques have been vigorously introduced in recent years, to be incorporated into the existing prediction algorithms, or as a stand-alone system, with a large amount of recorded past data to interpret the battery characteristics, and further predict for the present and future. This paper presents an overview of the machine learning techniques followed by a proposed pre-processing technique employed as the input to the long short-term memory network (LSTM) algorithm. The proposed pre-processing technique is based on the time-based sliding window algorithm (SW) and the Shapley additive explanation theory (SHAP). The proposed technique showed improvement in accuracy, adaptability, and reliability of SOC prediction when compared to other conventional machine learning models. All the data employed in this investigation were extracted from the actual driving cycle of five different electric vehicles driven by different drivers throughout a year. The computed prediction error, as compared to the original SOC data extracted from the vehicle, was within the range of less than 2%. The proposed enhanced technique also demonstrated the feasibility and robustness of the prediction results through the persistent computed output from a random selection of the data sets, consisting of different driving profiles and ambient conditions.

Obfuscated Tor Traffic Identification Based on Sliding Window

Tor is an anonymous communication network used to hide the identities of both parties in communication. Apart from those who want to browse the web anonymously using Tor for a benign purpose, criminals can use Tor for criminal activities. It is recognized that Tor is easily intercepted by the censorship mechanism, so it uses a series of obfuscation mechanisms to avoid censorship, such as Meek, Format-Transforming Encryption (FTE), and Obfs4. In order to detect Tor traffic, we collect three kinds of obfuscated Tor traffic and then use a sliding window to extract 12 features from the stream according to the five-tuple, including the packet length, packet arrival time interval, and the proportion of the number of bytes sent and received. And finally, we use XGBoost, Random Forest, and other machine learning algorithms to identify obfuscated Tor traffic and its types. Our work provides a feasible method for countering obfuscated Tor network, which can identify the three kinds of obfuscated Tor traffic and achieve about 99% precision rate and recall rate.

An investigation into the nonstationary characteristics of separation-bubble formation on a smooth circular cylinder in the critical transition range

Abstract To investigate the characteristics of the separation-bubble formation of flow over a smooth circular cylinder in the critical transition range, a sliding-window (SW) method and a peak-valley (PV) method were proposed to identify the intermittent jumps, named the characteristic events, in the real-time pressure signal obtained on both sides of the cylinder model. By evaluating the counts of the qualified events of the SW and PV methods, the PV method was found less sensitive to the small-scale disturbances in the pressure signal, therefore was adopted for later analysis. With the PV method, the characteristic events were identified from the pressure signal and categorized into two types: Type-1 is referred to the events of pressure descending and Type-2 is referred to the events of pressure ascending. Subsequently, the count per minute of the characteristic events was determined for describing the intermittency of the separation-bubble formation, and the time scale of each of the characteristic event was regarded as the time length of the separation-bubble formation. The count per minute of the characteristic events appeared to be the highest in the transitional regime. While the time scales of the characteristic events were varying with Reynolds number, the weighting-averaged normalized time scales in the transitional regime of the three cases studied were found comparable to the normalized time scale of the lift jump noted in the literature. Physically, the characteristic events found in the pressure signals in this study can be attributed to the three-dimensional aspect of separation-bubble formation.

Dynamic changes of large-scale resting-state functional networks in major depressive disorder

Sliding window method is widely used to study the functional connectivity dynamics in brain networks. A key issue of this method is how to choose the window length and number of clusters across different window length. Here, we introduced a universal method to determine the optimal window length and number of clusters and applied it to study the dynamic functional network connectivity (FNC) in major depressive disorder (MDD). Specifically, we first extracted the resting-state networks (RSNs) in 27 medication-free MDD patients and 54 healthy controls using group independent component analysis (ICA), and constructed the dynamic FNC patterns for each subject in the window range of 10–80 repetition times (TRs) using sliding window method. Then, litekmeans algorithm was utilized to cluster the FNC patterns corresponding to each window length into 2–20 clusters. The optimal number of clusters was determined by voting method and the optimal window length was determined by identifying the most representative window length. Finally, 8 recurring FNC patterns regarded as FNC states were captured for further analyzing the dynamic attributes. Our results revealed that MDD patients showed increased mean dwell time and fraction of time spent in state #5, and the mean dwell time is correlated with depression symptom load. Additionally, compared with healthy controls, MDD patients had significantly reduced FNC within FPN in state #7. Our study reported a new approach to determine the optimal window length and number of clusters, which may facilitate the future study of the functional dynamics. These findings about MDD using dynamic FNC analyses provide new evidence to better understand the neuropathology of MDD.

A Method for Processing Top-k Continuous Query on Uncertain Data Stream in Sliding Window Model

The data stream is a series of data generated at sequential time from different sources. Processing such data is very important in many contemporary applications such as sensor networks, RFID technology, mobile computing and many more. The huge amount data generated and frequent changes in a short time makes the conventional processing methods insufficient. The Sliding Window Model (SWM) was introduced by Datar et. al to handle this problem. Avoiding multiple scans of the whole data sets, optimizing memory usage, and processing only the most recent tuple are the main challenges. The number of possible world instances grows exponentially in uncertain data and it is highly difficult to comprehend what it takes to meet Top-k query processing in the shortest amount of time. Following the generation of rules and the probability theory of this model, a framework was anticipated to sustain top-k processing algorithm over the SWM approach until the candidates expired. Based on the literature review study, none of the existing work have been made to tackle the issue arises from the top-k query processing of the possible world instance of the uncertain data streams within the SWM. The major issue resulted from these scenarios need to be addressed especially in the computation redundancy area that contributed to the increases of computational cost within the SWM. Therefore, the main objective of this research work is to propose the top-k query processing methods over uncertain data streams in SWM utilizing the score and the Possible World (PW) setting. In this study, a novel expiration and object indexing method is introduced to address the computational redundancy issues. We believed the proposed method can reduce computational costs and by managing insertion and exit policy on the right tuple candidates within a specified window frame. This research work will contribute to the area of computational query processing.

A method for compressing AIS trajectory data based on the adaptive-threshold Douglas-Peucker algorithm

The deficiencies of massive data like storage difficulty, computation inefficiency, and information redundancy call for ship trajectory compression. While most studies on ship trajectory compression have one or more of the following drawbacks: the drawback of low compression efficiency; the problem resulted from error ship static information when the distance threshold is set based on ship length or width; poor compression quality for some trajectories, which is caused by experience-based optimal threshold. To solve these problems, we propose the ADP (Adaptive-threshold Douglas-Peucker) algorithm based on DP (Douglas-Peucker) algorithm. By determining the key points of each trajectory through the threshold change rate, ADP no longer relies on ship static information and makes it easier to determine the threshold, which is what traditional algorithms cannot achieve. Additionally, we use the advantage of matrix operation and the method of reducing points to improve the algorithm's computation efficiency. To verify the feasibility and superiority of the proposed algorithm, we compared our algorithm with DP algorithm, Partition-DP algorithm and Sliding Window algorithm from four aspects, namely, compression rate, Synchronous Euclidean distance, Length Loss Rate and running time. The experimental results prove that our algorithm has advantages over the other three algorithms, especially in threshold setting.

Modal analysis of slow varying non-stationary vibration by model updating with Schur complement

This paper proposes a new algorithm for the online monitoring of slow varying modal parameters in vibrating structures subjected to unknown excitations. In this method, an Auto-Regressive (AR) model will be applied in a Short-Time Sliding Window (STSW) on measured signals. The model parameters will be determined and updated through the order and time obtained from the previous computational window. The recursive multivariable least-squares method is utilized in conjunction with the Schur complement method to find solutions. Numerical simulations of the gradual changing system acted by the random excitations and sinusoidal force are presented. Finally, the modal parameters for the multichannel data measured on an experimental steel plate emerging from the water are identified and compared with those obtained with the analytical method and Short-Time Fourier Transform (STFT), providing a powerful means of validating the proposed method.

Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

Evaluation of a Sliding Window mechanism as DataAugmentation over Emotion Detection on Speech

Emotion analysis is an important field of study, with many applications for security, financial, and politician. Despite beinga subjective branch of study, emotion analysis can be simulated by Machine Learning algorithms that are trained for this purpose, throughcataloged audio datasets, they can recognize patterns in these media that could be related to corresponding emotion. Neural NetworkAlgorithms are able to work on the recognition of these emotions, with a focus only on audio, known as Speech Emotion Recognition(SER). Neural Network Algorithms generally obtain unequal averages of referring results such as recognition of emotions when applied todifferent audio datasets. This research evaluates a Data Augmentation method called Slide Window, which generates more data samplesin order to increase the averages of classification rates. The method has been applied to three public datasets: EMO-DB, SAVEE, and RAVEDESS. The experiments have shown effectiveness in the increasing of the recognition rates of about to 11.95% on the EMO-DBbase, 22.76% on SAVEE, and 18.82% on RAVEDESS when compared to other approaches in the literature.