Digital watermarking is a technique of hiding information for copyright protection and authentication. In this paper, an invisible digital watermarking algorithm on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) domains is presented. Herein, a binary watermark image was embedded in the middle sub-band coefficients of a video stream. The experimental results of proposed algorithm indicates that the PSNR values of the watermarked videos were as high as almost up to 37 dB with the optimal watermarking strength. The proposed algorithm was proven robust against HEVC stream compression. It is hence anticipated that it could be effectively applied for copyright protection and authentication.

Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM

Copyright protection and authentication have become increasingly more important in daily life. The digital watermark is one of the techniques invented to tackle this issue. In this paper, a digitally invisible watermark is embedded in a QR code image by means of wavelet transform. In the embedding process, a binary image, logo, is transformed into a corresponding watermark and then embeded into a selected subband. The experimental results illustrated that, for all the cases considered in this paper is more robustness to attacks and as such it can serve as a viable copyright protection and authentication tool.

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems.

Digital watermarking techniques embed information in multimedia, such as image, audio and video. The watermark has been resolved copyright ownership and verifying the integrity of content. This paper proposed a watermarking technique based on wavelet transform and binary watermark. The embedded algorithm has been done in two bands of video. Furthermore, the watermark extraction process does not need the original video sequence. The experimental results show the algorithm is more robust and it can achieve copyright protection and authentication.

Plastic waste has become one of the most prevailing environmental concerns in modern society. Without proper environmental control policies and regulations in place, we have witnessed plastic garbage being irresponsibly disposed of into rivers and other water reservoirs, causing blockage of water passage as well as toxic contamination. Since plastic materials are unable to degrade in nature so quickly as biomaterials, human workers are typically required to collect plastic garbage, either manually or mechanically assisted. In some conditions, the maneuvers could cause personal injuries and infections. To resolve this issue, this paper proposes the design and development of a prototype water surface garbage collecting robot. The prototype was implemented on a Raspberry Pi board and controllable from an Android device via wireless (WiFi™) channel. Controlling robot movement, speed, and conveyor belt could be done either manually or automatically. The former was enabled by intuitive user interface, while the latter relied on deep learning of local scene acquired within active range. Experimental results of automatic operation, driven by deep learning models demonstrated garbage collecting performance at 83.25% accuracy.