The objective of the research project is to develop nanocrystalline Porous Silicon (PSi) coating through the electrochemical etching on the research neuro-type silicon substrates. In identifying the size and structure of nanoscale properties, the X-ray diffraction analysis, Fourier-transform infrared spectroscopy and atomic force microscopy have been entirely employed to characterize the produced material in nanoscale. Synthesis of colloidal nanoparticles involved electrolytic reaction with the alumina nanoparticles (Al2O3) and preparation of the alumina nanoparticles as a thin film was done by drop-casting technique. The nanoparticles of Al2O3 were studied in an appropriate manner in terms of their morphology, structure and optical properties. It was disclosed that when these nanoparticles were incorporated in the porous silicon photovoltaic system, the solar cell system of Al2O3/PSi/Si/Al had phenomenal results concerning the performance indicators of the system.

This paper resents an enhanced scheme utilizing Rate-Distortion Optimized Quantization Index Modulation (RDO-QIM) for image watermarking, emphasizing its ability to balance imperceptibility, robustness and computational efficiency. Unlike conventional QIM-based methods that embed uniformly across all transform coefficients, the proposed approach employs a Lagrangian Rate-Distortion (R-D) criterion to adaptively quantize the coefficients. This dynamically balances embedding distortion and payload rate, achieving superior visual quality without compromising robustness. The algorithm jointly exploits cosine and wavelet coefficients to capture both local and global image features, while a perceptual energy/saliency model directs embedding toward visually tolerant regions. Experimental results on the RSICD dataset and eight standard images confirm significant improvements over a recently published DCT/DWT domains baseline, with PSNR exceeding 50 dB and SSIM above 0.99 on average, alongside a notable reduction in bit error rate under various attacks. Although the method introduces moderate computational overhead (≈25% higher runtime) due to the R-D optimization loop, it maintains full blindness and requires no side information for watermark extraction. These findings demonstrate that RDO-QIM is a highly practical and efficient solution for secure, high-fidelity multimedia watermarking.

In the current work, the Landsat data were applied to monitor vegetation in the governorate of Wasit in Iraq country as the region of interest during the period between 2012 and 2024. NDVI was calculated and analyzed by using Landsat Satellite Image in GIS environment which was used to get binary images through analysis. Vegetation changes in this area were estimated and explored with the help of the Normalized Difference Vegetation Index (NDVI) as a vegetation index. The findings display minor increment in the vegetation rates. The binary image extraction method was applied to estimate the area of every period.

Innovation in photovoltaic technology is driven by the search for sustainable and effective energy production. In this study, we investigate how Anti-Reflective Coatings (ARCs) made of copper oxide nanoparticles (CuO NPs) might be integrated into Porous Silicon (PS) solar cells to increase photovoltaic efficiency. The main goal is to reduce incident sunlight loss from reflection, which will increase light absorption and energy conversion. Colloidal nanoparticles, or CuO NPs, were synthesized via the electrolysis process and coated onto glass substrates and p-type PS to create films through the use of the spin coating method. The Electrochemical Etching process (ECE) is used to prepare PS. X-ray Diffraction (XRD) and an Atomic Force Microscope (AFM) have been used to study the structural and morphological properties of copper oxide nanostructures (CuO NSs) that have been deposited on glass substrates, respectively. Scanning Electron Microscope (SEM) was investigated for CuO NSs. The optical properties of CuO NPs are studied. The photovoltaic characteristics of CuO NSs/PS/Si/Al solar cells were reporte and the efficiency of solar cells was calculated before and after the ARCs were added. The efficiency of PS/Si/Al solar cells is increasing from 0.268 to 2.97% after depositing CuO NPs.

We present an IoT environmental monitoring system that  uses three innovative features to improve traditional methods: ESP32-based sensor nodes with DHT22 sensors (30-second sampling,  ±0.5°C/±2% RH accuracy, JSON payloads), real-time alerting via MQTT (QoS 1 + TLS) and Node-RED (SMS/email in <5s), and predictive analytics using linear regression  (85% accuracy for >2°C/5% RH trends). The Raspberry Pi-based system with Mosquito broker demonstrates 99.8% message reliability and 99.2% uptime during 30-days tests across residential place. Our solution costs less than $50 per node while using open-source tools (InfluxDB/SQLite) to achieve incident response time reduction from hours to seconds and risk forecasting accuracy of 20-40 minutes ahead of existing passive monitoring systems.

Image De-Noising is essential for images transmitted through a noisy communications channel. Several approaches have been presented to address this scenario in different communications mediums and for different noise types. In this work, a de-noising scheme is proposed in which an adaptive algorithm is utilized to retain certain Cosine coefficients. The selection of these coefficients, based on a data-driven threshold, makes the resultant image less noisy for Gaussian noise. The adaptive algorithm decompose the noisy image into Cosine and Haar domains and the decomposition process halts when a predefined limit is reached. The proposed scheme is tested with grey scale images that are publicly available and compared with the state-of-the-art systems. Peak Signal to Noise Ratio (PSNR) is used as a metric besides the human visual inspection. As shown in the results, the proposed system performs better than the system under comparison in terms of the used metrics.

The eye is our primary sensory organ, providing the primary channel for perceiving the world around us. Daily tasks become difficult for people with visual impairments, as they rely on their senses of touch, hearing, smell and taste to navigate their environment. Modern technology has provided smart tools and applications to assist the visually impaired. This project draws its inspiration from how the human eye, along with brain cells, analyzes images to identify objects. The system uses a Raspberry Pi with a built-in camera, the Picamera2 library, to capture real-time images during execution. The system is based on the SSD MobileNet V3 algorithm, which applies deep learning through convolutional neural networks (CNNs) for object recognition. The COCO dataset serves as the basis for training the model, demonstrating superior performance in object recognition across different categories. The OpenCV library was also used to enhance detection accuracy, while the NumPy library enables complex calculations, including distance estimation using focal length proportional laws. The audio feedback generation is based on the pyttsx3 library, which converts the names of recognized objects, along with distances, into spoken words. These techniques allow the system to learn automatically and improve its performance over time, resulting in better recognition accuracy. The Raspberry Pi acts as the central processing unit, managing all tasks, from image capture to analysis and response generation. The system is flexible and scalable, allowing for expanded features with text recognition and color identification capabilities. This advanced technology represents a breakthrough in the field of assistive technology, enhancing the independence of visually impaired people and supporting their active participation in various environments. This solution provides a real-time object recognition system.

About 7% of greenhouse gas emissions globally are attributable to the cement sector into the environment. Alternative binders must be developed to address the costs associated with the building sector and the environmental effects of cement manufacturing, as well as to make the concrete industry sustainable. The partial replacement of cement in concrete with paper ash was investigated as a solution to these research gaps. Sixty concrete cubes with a standard size of 100 mm were also cast and they were immersed in water for 7, 14, 21 and 28 days to cure. The results show that the specific gravity and sand's moisture content and paper ash used were 1.34 and 1.66 and 4.23 and 3.04, respectively. For every replacement pattern, the compressive strength findings showed that strength increased with increasing curing time. 11.61 and 15.02 N/mm² were the compressive strengths after 7 and 28 days for 0%, whereas 6.32 and 8.10 N/mm² were obtained for 20%. Strength development for the control concrete was faster a little more than 76% of its 28-day strength was attained in 7 days compared to 62% for 20% replacement level. Though a downward trend in strength development was noticed as the percentage of Paper ash increases, there is the prospect of Paper ash concrete to equal and exceeds control strength as depicted by their convergence towards the control line. In conclusion, paper ash can be utilized to partially substitute cement in concrete due to its pozzolanic qualities. Considering the outcomes, 5% replacement level is recommended.