Muhammad Shahzad

Multivariate Time Series Classification (MTSC) is crucial in extensive practical applications, such as environmental monitoring, medical EEG analysis, and action recognition. Real-world time series datasets typically exhibit complex dynamics. To capture this complexity, RNN-based, CNN-based, Transformer-based, and hybrid models have been proposed. Unfortunately, current deep learning-based methods often neglect the simultaneous construction of local features and global dependencies at different time scales, lacking sufficient feature extraction capabilities to achieve satisfactory classification accuracy. To address these challenges, we propose a novel Multiscale Periodic Time Series Network (MPTSNet), which integrates multiscale local patterns and global correlations to fully exploit the inherent information in time series. Recognizing the multi-periodicity and complex variable correlations in time series, we use the Fourier transform to extract primary periods, enabling us to decompose data into multiscale periodic segments. Leveraging the inherent strengths of CNN and attention mechanism, we introduce the PeriodicBlock, which adaptively captures local patterns and global dependencies while offering enhanced interpretability through attention integration across different periodic scales. The experiments on UEA benchmark datasets demonstrate that the proposed MPTSNet outperforms 21 existing advanced baselines in the MTSC tasks.

Multivariate Time Series Classification (MTSC) is crucial in extensive practical applications, such as environmental monitoring, medical EEG analysis, and action recognition. Real-world time series datasets typically exhibit complex dynamics. To capture this complexity, RNN-based, CNN-based, Transformer-based, and hybrid models have been proposed. Unfortunately, current deep learning-based methods often neglect the simultaneous construction of local features and global dependencies at different time scales, lacking sufficient feature extraction capabilities to achieve satisfactory classification accuracy. To address these challenges, we propose a novel Multiscale Periodic Time Series Network (MPTSNet), which integrates multiscale local patterns and global correlations to fully exploit the inherent information in time series. Recognizing the multi-periodicity and complex variable correlations in time series, we use the Fourier transform to extract primary periods, enabling us to decompose data into multiscale periodic segments. Leveraging the inherent strengths of CNN and attention mechanism, we introduce the PeriodicBlock, which adaptively captures local patterns and global dependencies while offering enhanced interpretability through attention integration across different periodic scales. The experiments on UEA benchmark datasets demonstrate that the proposed MPTSNet outperforms 21 existing advanced baselines in the MTSC tasks.

Unmanned Aerial Vehicles (UAVs) have revolutionized various industries by providing efficient and automated flight capabilities. However, reliance on GPS and traditional navigation systems poses challenges in scenarios where signal interference or failures occur. In this research, we present a novel computer vision-based method to enhance UAV navigation, enabling accurate height and location estimation. Our approach utilizes a sophisticated network that leverages a pair of images to estimate UAV height. The Pyramid Stereo Matching Network is employed to extract robust image features and generate a disparity map. Subsequently, a custom network processes and convolves this data, employing diverse computer vision techniques to achieve precise height estimation. To evaluate the effectiveness of our proposed method, we collected a comprehensive dataset by conducting flights with a Phantom 4 Pro drone over NUST Main campus, H-12 Islamabad. The dataset encompasses images captured at ten different heights, spanning from 100 to 280 meters, with flights evenly spaced 20 meters apart. In rigorous evaluations, our approach demonstrates promising results compared to existing methods. By liberating UAVs from reliance on GPS, this vision-based 3D localization technique holds immense potential to ensure successful flights even in challenging environments.

Change detection using satellite/aerial images is used to quantify the impacts of many natural and man-made disasters. At the occurrence of such events, both prechange optical and synthetic aperture radar (SAR) images can be obtained by going back in time. However, the availability of the postchange optical image is often hindered by the presence of artifacts like clouds. To circumnavigate this, we propose a novel change detection data setting that uses both optical and SAR images prechange, yet only SAR imagery postchange. For this challenging scenario, we propose a Siamese network that processes the prechange and postchange SAR inputs using a shared set of weights, while the prechange optical input is processed using a network that do not share the weights with the SAR inputs. The encoded weights from the three networks are fused and finally decoded using a common decoder to obtain the change map. Our model effectively fuses multisensor information and can obtain satisfactory result despite the absence of the postchange optical image. Experimental results on a multisensor urban dataset demonstrate the effectiveness of the proposed approach.