Non-local means (NL-means) is a state-of-the-art image denoising algorithm that leverages self-similarity by averaging similar patches weighted by the classic $L_2$-norm distance. In this work, we extend the similarity measure to arbitrary $L_p$-norms ($1 \le p \le \infty$) and investigate their impact on denoising performance. We implement and evaluate NL-means with $p = 1, 2, 3, 4, \infty$ and compare via quantitative metrics (MSE, MAE, PSNR, SSIM), residual analysis, and visual inspection. Experiments on the \emph{Lena} image corrupted with AWGN ($\sigma = 20$), a widely used benchmark setting in the denoising literature, show that while $L_2$-norm remains optimal overall, other norms off... More >

Poultry farming plays a vital role in global food production, requiring efficient management to ensure productivity and animal welfare. Traditional methods, largely based on manual monitoring, are often inefficient, error-prone, and costly. With the rise of Internet of Things (IoT) technologies, intelligent systems now enable remote monitoring and management of environmental conditions, farm operations, and disease prevention. Platforms such as ThingSpeak allow for real-time data collection, processing, and visualization, offering a cost-effective solution for poultry farm management. By integrating sensors to measure temperature, humidity, air quality, and feeding, and by leveraging ThingSp... More >