Ultra wideband (UWB) technology is suitable for high data rate short range wireless communication, localization, and imaging techniques. However, UWB systems require high sampling rate and precise synchronization. In order to reduce the sampling rate, have precise synchronization, and for low power requirement, UWB systems are implemented using compressive or sub-Nyquist rate measured samples by exploiting the sparsity of the UWB signal. Compressive sensing (CS)-based UWB systems are being designed in two ways: 1) signal demodulation or detection is performed in the CS domain without full signal recovery at the front-end. Thus, demodulation or detection works on compressive measurements. However, system performance deteriorates in the CS domain as compared with full Nyquist rate sampling and 2) after, Nyquist rate signal is recovered using efficient algorithms at the front-end, the signal demodulation or detection is performed using the conventional receiver. Thus, one requires an efficient CS/sampling of signal measurement at the front-end for better system performance for both the cases stated earlier. In this paper, we propose a deterministic (partial) UWB waveform-matched measurement matrix. The proposed measurement matrix has a circulant structure and is sparse in nature. The proposed matrix is easy to implement in hardware and is operationally time efficient as needed in a practical system. The bit error rate performance of the corresponding UWB system and the operational time complexity with the proposed measurement matrix are better as compared with the existing measurement matrices in the CS domain for both the above receiver designs. The efficacy of the proposed measurement matrix is verified through extensive simulations in both the additive white Gaussian noise and multipath communication environments. In addition, we have also compared other desirable properties of the proposed measurement matrix with the existing measurement matrices.

A New Sparse Signal-Matched Measurement Matrix for Compressive Sensing in UWB Communication

A lossy compression algorithm for still color images is presented. Based on DCT and using adaptive block scanning, the proposed method utilizes a simple technique to encode efficiently the DCT coefficients. The required image quality is guaranteed by using the bisection method to threshold the DCT coefficients of the YCbCr image gotten from the input RGB image. Four scan orders (zigzag, horizontal, vertical and hilbert) are used as adaptive block scanning to read the DCT coefficients from the retained DCT block coefficients. Following a scan order, an index vector is formed by the length of the zero-run sequence that preceded a nonzero DCT coefficient. The lowest value of the four index vectors maximums determines the best scan. Finally, the nonzero DCT coefficients and the index vector for each block are encoded to form the compressed image. The obtained results faced to those reported in recent methods show clearly that the proposed technique achieves high performances.

DCT-based color image compression algorithm using adaptive block scanning

Video watermarking on the dual tree-complex wavelet (DT CWT) domain is shown to be effective to offer high robustness. Existing DT CWT video watermarking schemes tend to use all the coefficients on the high-pass sub-bands of the DT CWT domain for watermark embedding and detection, which lack of investigating the correlations among different sub-bands and fail to explore the stable coefficients for robust watermarking. In this paper, we propose a novel DT CWT video watermarking scheme by exploring the stable coefficients on joint sub-bands. We first extract a set of candidate coefficients by applying block singular value decomposition (SVD) on the DT CWT domain. Then, we simulate the watermark embedding by modifying the candidate coefficients on each sub-band, from which we identity two pairs of strongly correlated sub-bands termed as the joint sub-bands. The watermark is eventually embedded by modifying the candidate coefficients of the joint sub-bands on a level which is adaptively chosen according to the video resolution. During the watermark detection, we identify and extract a set of stable coefficients from the candidate coefficients of the joint sub-bands to verify the ownership of the video. Extensive experiments demonstrate the advantage of our propose scheme over the latest DT CWT based schemes, which also performs better than the existing non-DT CWT transformed domain video watermarking schemes. 

Exploring Stable Coefficients on Joint Sub-Bands for Robust Video Watermarking in DT CWT Domain

Video synopsis is an effective technique for the efficient analysis of long videos in a short time. To generate a compact video, multiple tracks of moving objects, which we call as tubes are displayed simultaneously by rearranging them along the time axis. Contemporaneous video synopsis approaches focus on collision avoidance, or preservation of chronological order among tubes. However, generation of an adaptive personalized user-oriented synopsis video congruent to users’ preferences has yet not been thoroughly experimented. This paper propounds a framework for personalized visualization of synopsis video, integrating pertinent object attributes such as color, type, size, speed, travel path and direction towards generation of synopsis video for precise inference of user needs. The framework motivates users to interactively define queries for creation of the targeted synopsis. User queries are classified into visual-queries, temporal-queries, spatial-queries, and spatio-temporal queries concomitant with the visual and spatio-temporal attributes. Tubes relevant to a user-query are selected, and grouped according to original behavioral interactions followed by their rearrangement, to generate synopsis video with fewer false collisions. To evaluate the proffered technique, two evaluation metrics are proposed and extensive experiments of publicly available surveillance videos are conducted. The experimental results demonstrate the propriety and usability of the newer approach.

Interactive visualization-based surveillance video synopsis

Communication for hearing-impaired communities is an exceedingly challenging task, which is why dynamic sign language was developed. Hand gestures and body movements are used to represent vocabulary in dynamic sign language. However, dynamic sign language faces some challenges, such as recognizing complicated hand gestures and low recognition accuracy, in addition to each vocabulary’s reliance on a series of frames. This paper used MediaPipe in conjunction with RNN models to address dynamic sign language recognition issues. MediaPipe was used to determine the location, shape, and orientation by extracting keypoints of the hands, body, and face. RNN models such as GRU, LSTM, and Bi-directional LSTM address the issue of frame dependency in sign movement. Due to the lack of video-based datasets for sign language, the DSL10-Dataset was created. DSL10-Dataset contains ten vocabularies that were repeated 75 times by five signers providing the guiding steps for creating such one. Two experiments are carried out on our dataset (DSL10-Dataset) using RNN models to compare the accuracy of dynamic sign language recognition with and without the use of face keypoints. Experiments revealed that our model had an accuracy of more than 99%.

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MediaPipe’s Landmarks with RNN for Dynamic Sign Language Recognition

Compressive sensing principle claims that a compressible signal can be recovered from a small number of random linear measurements. However, the design of efficient measurement basis in compressive imaging remains as a challenging problem. In this paper, a new set of hybrid wavelet measurement matrices is proposed to improve the quality of the compressive imaging, increase the compression ratio and reduce the processing time. The performance of these hybrid wavelet matrices for image modeling and reconstruction is evaluated and compared with other traditional measurement matrices such as the random measurement matrices, Walsh and DCT matrices. The compressive imaging approach chosen in this study is the block compressive sensing with smoothed projected Landweber reconstruction technique. The simulation results indicate that the imaging performance of the proposed hybrid wavelet measurement matrices is approximately 2–3 dB better than that obtained using Gaussian matrix especially at higher compression ratios.

Hybrid wavelet measurement matrices for improving compressive imaging

These days surveillance cameras are spreading very fast for security issues; however reviewing, retrieving and analyzing all these surveillance videos consume a lot of time. Video synopsis technology solved this problem by extracting all the active objects tubes that occurred at different times and relocating these objects simultaneously in a video for fast reviewing. Rearranging objects tubes in the video is considered the main challenge in creating video synopsis. Conventional methods proposed different approaches to optimize the energy function for relocating the object, but it suffers from high computational complexity and time-consuming. Moreover, some of these methods could not save the chronological order of moving objects. In this paper, the particle swarm algorithm is proposed for the first time to solve the energy minimization function and rearrange the objects to generate a condensed synopsis video with less collision and in chronological order. The experiments are applied to a benchmark dataset (VIRAT), and the preliminary simulation results demonstrate that the proposed method outperforms the genetic algorithm.

Object-based video synopsis approach using particle swarm optimization

Sparsity adaptive matching pursuit (SAMP) is a greedy reconstruction algorithm for compressive sensing signals. SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms. However, SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios. In the proposed research, the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique. Furthermore, the idea of block compressive sensing (BCS) is combined with the SAMP technique to improve the performance of the SAMP technique. Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques. Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels (dB) relative to the conventional SAMP technique. In addition, the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques. Moreover, the computation time of the proposed BCS-SAMP is less than that of the iterative techniques, especially at lower measurement fractions.

Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix

Efficient common objects localization based on deep hybrid Siamese network

In this paper, a new robust and secure video watermarking scheme based on discrete Wavelet Transform (DWT), singular value decomposition (SVD), and Laplacian pyramid is proposed for copyright protection applications. The main contributions of this work are: 1) Increasing watermark robustness towards common signal processing attacks by separating the watermark image into a high-frequency image and a low-frequency image using laplacian pyramid. Then, the singular values of each of the low-frequency watermark image and the high-frequency watermark image are hidden in two different DWT subbands of the original video frame. Separating the watermark and hiding its two parts in two different subbands of the DWT of the video frame makes it robust towards noise, filtering, and compression attacks. 2) Improving the quality of the extracted watermarks via correcting the received watermarked videos using SURF points when those videos are geometrically attacked, mainly by rotation, scaling, and shear attacks. 3) Increasing security and avoiding false positive problems (FPP) associated with SVD by using perceptual image hashing in the proposed scheme. Through experimental results and comparisons to other techniques, the proposed scheme has shown very high imperceptibility for the tested watermarked videos in terms of peak signal to noise ratio (PSNR). Also, the proposed scheme has demonstrated high watermark robustness towards various types of attacks, and an improvement in the quality of the extracted watermarks from 4 to 20 dB compared to other techniques.

Rasha Shoitan

Papers

Hybrid wavelet measurement matrices for improving compressive imaging

Object-based video synopsis approach using particle swarm optimization

Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix

Efficient common objects localization based on deep hybrid Siamese network

A robust video watermarking scheme based on Laplacian pyramid, SVD, and DWT with improved robustness towards geometric attacks via SURF