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With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments.

This study considers the applicability and effectiveness of the Viola–Jones method to automatically distinguish zooplankton particles from the background in images reconstructed from digital holograms obtained in natural conditions. For the first time, this algorithm is applied to holographic images containing coherent noise and residual defocusing. The method was trained on 880 annotated (marked) holographic images of Daphnia magna along with 120 background frames. It was then tested on independent laboratory and field datasets, including morphologically related taxa. With optimized settings, the precision of the algorithm reached ~90% and F1~85% on noisy holographic images, and the algorithm also demonstrated the preliminary ability to recognize similar taxa without retraining. The algorithm is well suited for analyzing holographic data as a fast and resource-efficient pre-filter—it effectively separates particles from the background and thereby allows subsequent classification or its application in real-time aquatic environment monitoring systems. The article presents experimental results demonstrating the efficiency of this algorithm during plankton monitoring in situ.

This paper introduces the Color Histogram Contouring (CHC) method, a new training-less approach to object detection that emphasizes the distinctive features in chrominance components. By building a chrominance-rich feature vector with a bin size of 1, the proposed CHC method exploits the precise information in chrominance features without increasing bin sizes, which can lead to false detections. This feature vector demonstrates invariance to lighting changes and is designed to mimic the opponent color axes used by the human visual system. The proposed CHC algorithm iterates over non-zero histogram bins of unique color features in the model, creating a feature vector for each, and emphasizes those matching in both the scene and model histograms. When both model and scene histograms for these unique features align, it ensures the presence of the model in the scene image. Extensive experiments across various scenarios show that the proposed CHC technique outperforms the benchmark training-less Swain and Ballard method and the algorithm of Viola and Jones. Additionally, a comparative experiment with the state-of-the-art You Only Look Once (YOLO) technique reveals that the proposed CHC technique surpasses YOLO in scenarios with limited training data, highlighting a significant advancement in training-less object detection. This approach offers a valuable addition to computer vision, providing an effective training-less solution for real-time autonomous robot localization and mapping in unknown environments.

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices.

One major challenge of friction stir processing (FSP) is its sensitivity to parameters like advancing and rotational speeds. This study examined the effect of tool travel speed on the microstructural evolution and mechanical properties of a new-generation Al-6Mg alloy. Optical and electron microscopy, EBSD, and shear-punch testing (SPT) were used. Two travel speeds, 50 and 120 mm/min, revealed significant differences in microstructure and properties at ambient temperature. EBSD provided misorientation maps and boundary fraction data. Microstructure analysis showed continuous dynamic recrystallization in the nugget zone, with finer grains observed at the higher speed. Microhardness was greater on both sides at 120 mm/min. The TMAZ showed elongated grains at 120 mm/min, while recrystallized grains were more prominent at 50 mm/min. In the HAZ, partial recrystallization occurred at 120 mm/min, whereas extensive recrystallization was observed at 50 mm/min. The SPT results indicated variations in stiffness between advancing and retreating sides, especially 2 mm from the nugget center. At 10 and 20 mm from the center, higher stiffness and strength were recorded at 120 mm/min. This study established correlations between joint stiffness, grain misorientation, and travel speed.

Human–computer interaction (HCI) is a multidisciplinary field that investigates the interactions between humans and computer systems. HCI has facilitated the development of various digital technologies that aim to deliver optimal user experiences. Gaze recognition is a critical aspect of HCI, as it can provide valuable insights into basic human behavior. The gaze-matching method is a reliable approach that can identify the area at which a user is looking. Early methods of gaze tracking required users to wear glasses with a tracking function and limited tracking to a small monitoring area. Additionally, gaze estimation was restricted to a fixed posture within a narrow range. In this study, we proposed a novel non-contact gaze-mapping system that could overcome the physical limitations of previous methods and be applied in real-world environments. Our experimental results demonstrated an average gaze-mapping accuracy of 92.9% across 9 different test environments. Moreover, we introduced the GIST gaze-mapping (GGM) dataset, which served as a valuable resource for learning and evaluating gaze-mapping techniques.

\"Obviously we're devastated, the whole family's devastated,\" he said. \"I'm just asking anybody out there who may have seen his wallet or his keys laying around to come forward.\" \"They're cowardly thugs really, two onto one, on a bloke that is probably affected (by alcohol).\"

\"It's hard for families to plan long term, so it's very important to give them the information they need,\" said Tammy Severino with Annandale Village, a Suwanee, Ga., community serving adults with developmental disabilities. \"Unfortunately, families may not know what's out there. It's a great way for people to learn about us and for us to learn what they need.\" \"By training a dog like [Jerry] to assist a person with their daily activities, we can dramatically increase a person's independence,\" said Ramona Nichols, the program's director. \"Fairs are very important to raise awareness about service dogs. Since we're a relatively new program, we're trying to get the word out about what these dogs can do and how they can help.\"

\"Every mentor's job can be different based on what their county's needs are,\" said Ms. [Michal Jones], who is in her fourth year as a parent mentor. Jessica Henry, parent involvement coordinator at Cherokee Ridge Elementary in Chickamauga, Ga., met Ms. Jones when she became parent mentor. Mrs. Henry's 13-year-old son also is autistic and attends LaFayette Middle School. \"It's great to meet other parents in our county,\" Mrs. Henry said. \"It's where you can go and be open and be yourself and no one judges.\" For those interested in the parent mentor program, Michal Jones can be reached by mail at Walker County Schools, 205 Jenkins Road, Rossville, GA 30741; by phone at 706-866-9778, ext. 122; or by email at michaljones@walkerschools.org.