Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. Classification accuracies of these degrade considerably in cross-year mapping. Cross-year is more useful as it allows the prediction following years’ maps using previously labeled data. We propose vector dynamic time warping (VDTW), an innovative multi-year ...

The technique of dynamic programming for the time registration of a reference and a test pattern has found widespread use in the area of isolated word recognition. Recently, a number of variations on the basic time warping algorithm have been proposed by Sakoe and Chiba, and Rabiner, Rosenberg, and Levinson. These algorithms all assume that the test input is the time pattern of a feature vector...

This paper aims to develop a writing robot by recognizing the speech signal from the user. The robot arm constructed mainly for the disabled people who can’t perform writing on their own. Here, dynamic time warping (DTW) algorithm is used to recognize the speech signal from the user. The action performed by the robot arm in the environment is done by reducing the redundancy which frequently fac...

Many applications generate and consume temporal data and retrieval of time series is a key processing step in many application domains. Dynamic time warping (DTW) distance between time series of size N and M is computed relying on a dynamic programming approach which creates and fills an N × M grid to search for an optimal warp path. Since this can be costly, various heuristics have been propos...

The need for the temporal alignment of gait cycle data is well known; however, there is little consensus concerning which alignment method to use. In this paper, we discuss the pros and cons of some methods commonly applied to temporally align gait cycle data (normalization to percent gait cycle, dynamic time warping, derivative dynamic time warping, and piecewise alignment methods). In additio...

In this paper, we propose a method of improving Convolutional Neural Networks (CNN) by determining the optimal alignment of weights and inputs using dynamic programming. Conventional CNNs convolve learnable shared weights, or filters, across the input data. The filters use a linear matching of weights to inputs using an inner product between the filter and a window of the input. However, it is ...

Seismic data are recorded and commonly interpreted in vertical two-way time; well logs, measured in depth, must be tied to seismic using a time-depth curve. However, well ties contain a large amount of uncertainty due to errors in the generation of synthetic seismograms and manual matching of synthetic seismograms to seismic traces. Using dynamic time warping, a fast algorithm that optimally al...