Grid’5000 Based Large Scale OCR Using the DTW Algorithm: Case of the Arabic Cursive Writing

A time series analysis method for spacecraft telemetry data is presented in this paper. For spacecraft testing and on-orbit flight, this method can monitor the changes of telemetry data automatically and identify the failure modes of spacecraft. Using dynamic time warping (DTW) algorithm, combining historical data samples as well as fault cases with this method analyzes the similarity of telemetry data transformed into time series. By comparing the results of analysis with the results of DTW distance calculation, the relative deviation of data is measured and the abnormal data in fault mode is identified. The results show that the telemetry data analysis method based on DTW algorithm can effectively detect data anomalies and realize fault identification, which has a certain application prospect.

Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, researches are going on in the area of entomology using chemical, biological and mechanical approaches. The data relevant to the flying insects often changes over time, and classification of such data is a central issue. And such time series mining tasks along with classification is critical nowadays. Most time series data mining algorithms use similarity search and hence time taken for similarity search is the bottleneck and it does not produce accurate results and also produces very poor performance. In this paper, a novel classification method that is based on the dynamic time warping (DTW) algorithm is proposed. The dynamic time warping algorithm is deterministic and lacks in modeling stochastic signals. The dynamic time warping (DTW) algorithm is improved by implementing a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms is less than that of the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction. With running spectrum filtering (RSF) and dynamic range adjustment (DRA). NMF seek the median distance of every reference of time series data and the recognition accuracy is much improved. In this research work, optical sensors are used to record the sound of insect flight, with invariance to interference from ambient sounds. The implementation of our tool includes two parts, an optical sensor to record the "sound" of insect flight, and a software that leverages on the sensor information, to automatically detect and identify flying insects.

The Dynamic Time Warping (DTW) algorithm is widely used in finding the global alignment of time series. Many time series data mining and analytical problems can be solved by the DTW algorithm. However, using the DTW algorithm to find similar subsequences is computationally expensive or unable to perform accurate analysis. Hence, in the literature, the parallelisation technique is used to speed up the DTW algorithm. However, due to the nature of DTW algorithm, parallelizing this algorithm remains an open challenge. In this paper, we first propose a novel method that finds the similar local subsequence. Our algorithm first searches for the possible start positions of subsequence, and then finds the best-matching alignment from these positions. Moreover, we parallelize the proposed algorithm on GPUs using CUDA and further propose an optimization technique to improve the performance of our parallelization implementation on GPU. We conducted the extensive experiments to evaluate the proposed method. Experimental results demonstrate that the proposed algorithm is able to discover time series subsequences efficiently and that the proposed GPU-based parallelization technique can further speedup the processing.

Partial matching and search space reduction for QbE-STD

Croplands are commonly mapped using time series of remotely sensed images. The dynamic time warping (DTW) algorithm is an effective method for realizing this. However, DTW algorithm faces the challenge of capturing complete and accurate representative cropland time series on a national scale, especially in Asian countries where climatic and topographic conditions, cropland types, and crop growth patterns vary significantly. This study proposes an automatic cropland extraction method based on the DTW algorithm and density-based spatial clustering of applications with noise (DBSCAN), hereinafter referred to as ACE-DTW, to map croplands in Pakistan in 2015. First, 422 frames of multispectral Landsat-8 satellite images were selected from the Google Earth Engine to construct monthly normalized difference vegetation index (NDVI) time series. Next, a total of 2409 training samples of six land cover types were generated randomly and explained visually using high-resolution remotely sensed images. Then, a multi-layer DBSCAN was used to classify NDVI time series of training samples into different categories automatically based on their pairwise DTW distances, and the mean NDVI time series of each category was used as the standard time series to represent the characteristics of that category. These standard time series attempted to represent cropland information and maximally distinguished croplands from other possible interference land cover types. Finally, image pixels were classified as cropland or non-cropland based on their DTW distances to the standard time series of the six land cover types. The overall cropland extraction accuracy of ACE-DTW was 89.7%, which exceeded those of other supervised classifiers (classification and regression trees: 78.2%; support vector machines: 78.8%) and existing global cropland datasets (Finer Resolution Observation and Monitoring of Global Land Cover: 87.1%; Global Food Security Support Analysis Data: 83.1%). Further, ACE-DTW could produce relatively complete time series of variable cropland types, and thereby provide a significant advantage in mountain regions with small, fragmented croplands and plain regions with large, high-density patches of croplands.

Dynamic Time Warping(DTW) algorithm has strong resistance to synchronous error. Aiming at the similarity and asynchrony of meteorological factor, a method of applying DTW to photovoltaic power prediction is proposed. The meteorological factors which have great influence on photovoltaic power generation are selected by Pearson correlation coefficient method. Based on the DTW algorithm, the similarity of meteorological factors between the predicting days and the historical days can be measured. Through weighted calculation of similarity, the similar days are used to obtain training data of Partial Least Squares(PLS) prediction model. The simulation study on a photovoltaic power station verifies the correctness and effective of the proposed prediction method. The results show that the prediction model has great forecasting accuracy and certain feasibility and practicability.

This paper proposes an improved dynamic time warping (DTW) algorithm with a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms are less than the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction with running spectrum filtering (RSF) and dynamic range adjustment (DRA). For analyzing some incorrect results, unlike in conventional DTW, we do not use the minimum distance to recognize. we employ NMF and seek the median distance of the every reference word with the unknown speech waveform. All recognition accuracy of conventional DTW algorithms are improved much more by NMF. The recognition accuracy of Itakura's DTW algorithm is the best. Its recognition accuracy similar to that of the HMM approach in 10 dB and 20 dB white noise.

The gesture sequence recognition method based on the traditional Dynamic Time Warping(DTW) algorithm will lead to too long calculation time when the sample data sequence length is too large, and the accuracy of the recognition result will be obviously low due to the influence of noise. The FastDTW algorithm is mainly used for DTW acceleration, but it cannot suppress noise, and its time complexity is O(N). Edit Distance on Real sequence(EDR) algorithm can only suppress noise, but its efficiency is not improved, and its time complexity is O(N2). Based on the above problems, FastEDR algorithm is proposed for the matching verification of gesture sequences. The results show that the computational efficiency of FastEDR algorithm is significantly improved compared with DTW algorithm, the error acceptance rate FAR is reduced by 2.94%, and the error rejection rate FRR is reduced by 4.5%. The gesture sequence recognition based on FastEDR algorithm can achieve a better recognition state. If it is applied to unlock mobile phones, it is expected to become a new generation of unlocking method.

Today, the elderly population is increasing, and there are many drawbacks for them, especially defects in their knee joints which lead to improper gait. To solve this problem, their knee joint can be replaced with knee arthroplasty. In this letter, level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the dynamic time warping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees are incorporated in a test before and after TKA surgery. Then, the data of gait analysis is collected and the data is compared with a reference using the DTW algorithm. The outcome results illustrate an improvement of 89%-97% by the proposed algorithm after TKA surgery. Therefore, patients can see improvement with high accuracy and very fast that result in more use this technique in TKR surgery.

Fault location method combining transient mutation and dynamic time warping algorithm for grounding system

Unlike the impact of MERS or SARS, the COVID-19 has had a huge impact on both social and economic environments. This study investigated the COVID-19 economic impact using the S&P 500 index by applying the DTW (Dynamic Time Warping) algorithm, an artificial intelligence technique. The DTW algorithm is designed for the identification of events the capital market impact resembles in the past. It is shown that the impact of the capital market on COVID-19 showed a completely different pattern from the patterns seen in MERS and SARS. Also, the results showed patterns similar to those of the 2015 China’s Stock Market Crash, the 2019 U.S.-China Trade Dispute, the 2008 Global Financial Crisis, which are specifically referred to as financial crises. However, it was unexpectedly similar to the shock of the U.S.-North Korea Conflict and the 2001 9·11 Terror. They were treated as political and diplomatic crises. These events are closely related to APEC countries, meaning that APEC countries’ economic and political activities are essential in the global economic crisis. The results imply that the policies implemented by APEC countries to overcome the economic crises are important to resolve the global economic crisis from COVID-19.

DTW algorithm compares the parameters of an unknown spoken word with the parameters of one or more reference templates. The more reference templates are used for the same word, the higher is the recognition rate. But increasing the number of reference templates for the same word to recognize, leads to an increase in memory resources and computing time. The proposed algorithm is used in the learning phase and combines the advantages of DTW and Vector Quantization (VQ); instead of storing multiple reference templates, it stores only one reference model for each word and that reference is based on classes (like in the vector quantization method), each class is represented by a centroid (or codeword). In the recognition phase, the parameters of the unknown utterance are compared to the centroids of the reference model. This solution increases the speed of calculation in the recognition phase and reduces the quantity of used memory.

The technique of dynamic time warping (DTW) is relied on heavily in isolated word recognition systems. The advantage of using DTW is that reliable time alignment between reference and test patterns is obtained. The disadvantage of using DTW is the heavy computational burden required to find the optimal time alignment path. Several alternative procedures have been proposed for reducing the computation of DTW algorithms. However these alternative methods generally suffer from a loss of optimality or precision in defining points along the alignment path. In this paper we propose another alternative procedure for implementing a DTW algorithm. The procedure is based on the well known class of techniques for a directed search through a grid to find the shortest' path. An adaptive version of a directed search procedure is defined and shown to be capable of obtaining the exact DTW solution with reduced computation of distances but with increased overhead. It is shown that for machines where the time for distance computation is significantly larger than the time for combinatorics and overhead, a potential gain in speed of up to 3 to 1 can be realized with the directed search algorithm. Formal comparison of the directed search algorithm with a standard DTW method, in an isolated word recognition test, showed essentially no loss in recognition accuracy when the parameters of the directed search were selected to realize the 3 to 1 reduction in distance computation.

Recently, the methods based on Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in visual place recognition. CNN is a class of multilayer perceptrons, but unlike common multilayer perceptrons that it is not usually fully connected networks. It can acquire more general image features and make the image processing computationally manageable through filtering the connections by proximity. In this paper, we utilize the deep features generated by CNNs and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n3) to O(n). To test the proposed method, four datasets (Nordland, Gardens Point, St. Lucia, and UoA datasets) are used as benchmarks; using two traverses in each dataset with one for reference and the other for testing. The results show high precision-recall characteristics of our method in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which reflects that the spatiotemporal information contained in the image sequence is significant for the task of visual place recognition. Moreover, the proposed method also shows to outperform the classical sequence-based method SeqSLAM.

In order to solve the problem of encrypted traffic identification, the identification method based on dynamic time warping (DTW) and k-nearest neighbor (KNN) for the encrypted network behaviors was proposed. The method took the encrypted Twitter traffic as an example of research, and a large number of encrypted Twitter network behaviors were deeply analyzed, and then the features representing the encrypted network behaviors were extracted, and the specific encrypted network behavior module database based on DTW and KNN were established, and the DTW between the collection data set and the module database were calculated, and then were normalized, and then the encrypted network behaviors were classified by comparing with the preset empirical threshold, and the distance information were also considered by DTW algorithm, at the same time, the influence of TCP retransmission and duplicate ACK packets can be effectively eliminated by the dynamic time warping algorithm. In order to overcome the noise interference of the similar data traffic except the distance information, the similar filtered data packets were classified as the true behavior or the false behavior by KNN algorithm, and then the encrypted network behaviors were identified automatically and in real time, compared with the only correlation coefficient method or only DTW method, the online correct recognition rate by DTW and KNN has been greatly increased and reached to about 93%, and the missed detection rate is almost same with the traditional methods, the experiments and actual project applications showed that the proposed method was effective.