Vegetation encroachment on the powerline poses significant risks to the reliability and safety of the power infrastructure. Many LiDAR-Based methods have tried to address this problem, yet these methods lack scalability and generality across different LiDAR collection methods with varying resolutions and data sizes. This paper presents LineShield, a generalized LiDAR-based pipeline for automated vegetation encroachment detection on powerlines. The pipeline addresses these challenges on both airborne and mobile LiDAR datasets. Key components: clustering with DBSCAN for accurate powerline segmentation, PCA-based alignment for standardizing orientation, sliding window traversal for efficient processing of large datasets, voxel downsampling for reducing data complexity, and proximity-based severity classification for prioritizing interventions. Experimental results demonstrate the pipeline’s adaptability across three datasets, namely, ECLAIR, DALES, and Toronto-3D, achieving up to 97.4% detection accuracy and maintaining performance in both urban and rural environments. The approach also provides detailed reporting to help maintenance teams prioritize interventions.

Automatic fall detection using radar technology significantly advances assisted living and smarter healthcare solutions. In this paper, a novel unsupervised method for detecting fall incidents in human daily activities is proposed. By analyzing ultra-wideband radar returns, a radar time series is obtained and used to find the time-frequency signatures of different activities. These signatures are subsequently binarized and used as input to a deep stacked auto-encoder network for latent feature extraction. The latent features are then clustered through a clustering layer that leverages an auxiliary distribution function to fine-tune the samples clustered into fall and non-fall groups. The proposed fall clustering method is compared against several clustering approaches in terms of accuracy of clustered samples, normalized mutual information and adjusted rand index metrics. It is shown that the proposed method realizes automatic latent feature learning from the radar data and can distinguish fall from non-fall classes by uncovering patterns in a data set with no pre-existing labels. The results show that the proposed unsupervised fall detection method outperforms the other approaches in terms of providing higher accuracy of the clustered data samples. The advantage of the proposed method is that it does not need a large dataset to achieve distinctiveness between clusters.

Brain computer interface is a developing technology that can provide enhanced quality of life to individuals suffering from various disabilities. In this work, a new binary electroencephalography (EEG) signal decoding algorithm is proposed using a modified common spatial pattern and capsule network. The proposed method is realized by extracting the spectral-temporal common spatial pattern features from the EEG signals while preserving the time resolution of the signal. The resulting features are fed into the capsule network for automatic feature extraction and classification. The capsule network is known to be superior to convolutional neural networks in requiring less training data, which makes it a promising candidate for EEG signals classification. The performance of the proposed method is evaluated and compared to that of the other methods by conducting several experiments. The results demonstrate that the proposed method provides recognition accuracy higher than that provided by other methods.

Data security is a main concern in everyday data transmissions over the Internet. A possible solution to guarantee secure and legitimate transaction is via hiding a piece of tractable information into the multimedia signal, that is, watermarking. In this paper, we propose a new color image watermarking scheme and its corresponding detector in the sparse domain. The watermark detector aims at verifying the ownership and circumventing any unauthorized duplication of the digital data. Most of the existing color image watermarking schemes disregard the inter-channel dependencies. In view of this, we take into account the interchannel dependencies between RGB channels and interscale dependencies of the sparse coefficients of color images by employing the hidden Markov model. An efficient detector is designed by establishing a binary hypothesis test through which the existence of the hidden watermark is examined. Experiments are conducted to evaluate the performance of the proposed watermark detector for color images. The results show that the proposed detector provides detection rates higher than those provided by the other detectors, even in the presence of attacks. It is also shown that the proposed detector exhibits better performance in terms of the robustness of the embedded watermark.

The vector-based hidden Markov model (HMM) is a powerful statistical model for characterizing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross-orientation dependencies of the wavelet coefficients. In this paper we propose a scheme for designing a blind multibit watermark decoder incorporating the vector-based HMM in wavelet domain. The decoder is designed based on the maximum likelihood criterion. A closed-form expression is derived for the bit error rate and validated experimentally with Monte Carlo simulations. The performance of the proposed watermark detector is evaluated using a set of standard test images and shown to outperform the decoders designed based on the Cauchy or generalized Gaussian distributions without or with attacks. It is also shown that the proposed decoder is more robust against various kinds of attacks compared with the state-of-the-art methods.

Multimedia data piracy in the Internet is a growing problem, since it provides easy and fast data transmission. Watermarking is regarded as a solution to restrain unauthorized duplication or distribution data. Image watermarking research mostly focuses on grayscale images with an extension to color images. However, most of these techniques ignore dependencies between color channels. In view of this, in this work, a multichannel color image watermarking technique and its corresponding detector in the wavelet domain is proposed. The inter-channel dependencies between RGB channels and inter-scale dependencies of the wavelet coefficients of color image are taken into account by employing the vector-based hidden Markov model. We conduct experiment on a set of color images to assess the performance of the proposed watermark detector. The results show that the performance of the proposed detector is superior to that of the other detectors in terms of the imperceptibility of the embedded watermark and the detection rate. It is also shown that the proposed detector has better performance in presence or absence of different kinds of attacks in comparison to the other existing methods.

There are a number of image denoising methods in the wavelet domain using statistical models. It is known that the performance of such methods can be significantly improved by taking into account the statistical dependencies between the wavelet coefficients. It is shown that the vector-based hidden Markov model (VB-HMM) is capable of capturing both the subband marginal distribution and the inter-scale, intra-scale and cross orientation dependencies of the wavelet coefficients. In view of this, we propose a new maximum a posteriori estimator using the VB-HMM as a prior for the wavelet coefficients of images. This is realized by deriving an efficient closed-form expression for the shrinkage function. Experimental results are performed to evaluate the performance of the proposed denoising method. The results demonstrate that the proposed method outperforms some of the state-of-the-art techniques in terms of both the peak signal to noise ratio and perceptual quality.

The wavelet coefficients of images show heavy-tailed marginal statistics as well as strong inter- and intra-subbands and across orientations dependencies. The vector-based hidden Markov model (HMM) has been shown to be an effective statistical model for wavelet coefficients, which is capable of capturing both the subband marginal distribution and the inter-scale and intra-scale dependencies of the wavelet coefficients. In this paper, we propose a locally-optimum watermark detector using the HMM model for image wavelet coefficients. The performance of the proposed detector is studied through simulation and is shown to be superior to that of other detectors in terms of the imperceptibility of the embedded watermark and detection rate.