Occasionally intravenous insertion forms a challenge to a number of patients. Inserting an IV needle is a difficult task that requires a lot of skill. At the moment, only doctors and medical personnel are allowed to do this because it requires finding the right vein, inserting the needle properly, and carefully injecting fluids or drawing out blood. Even for trained professionals, this can be done incorrectly, which can cause bleeding, infection, or damage to the vein. It is especially difficult to do this on children, elderly people, and people with certain skin conditions. In these cases, the veins are harder to see, so it is less likely to be done correctly the first time and may cause blood clots. In this research, a low-cost embedded system utilizing Near-Infrared (NIR) light technology is developed, and two novel approaches are proposed to detect and select the best candidate veins. The two approaches utilize multiple computer vision tools and are based on contrast-limited adaptive histogram equalization (CLAHE). The accuracy of the proposed algorithm is 91.3% with an average 1.4 s processing time on Raspberry Pi 4 Model B.

Heart Rate Variability (HRV) is a measure of variation in the time interval between consecutive heart beats.HRV analysis is highly sensitive for risks linked with cardiovascular diseases which are main causes of death in Egypt and all over the Middle East. Early detection of cardiac arrhythmia diseases achieves effective treatment by making it easy to choose appropriate anti-arrhythmic drugs, also very important for improving arrhythmia therapy and preventing number of death in individuals. In this paper, an efficient cardiac arrhythmia detection algorithm is introduced. Different classifiers are deployed and examined on ECG signals. Various oversampling techniques are investigated to handle imbalanced dataset. The ensemble classifier; support vector machine and Random forest with random sampling show accuracy of 98.18 % in 0.145 sec which is the best accuracy among all other classifiers. In addition, this paper also proposes a mobile based system architecture integrated with the algorithm for diagnosis and classification of cardiac arrhythmia diseases. The proposed system can be easily used by patients to check their heart health remotely and easily.

The YOLO (You Only Look Once) object detection models have undergone rapid evolution, with each version introducing architectural enhancements aiming to improve speed, accuracy, and deployment. Simultaneously, single-board computers (SBCs) have advanced to support increasingly complex AI models in edge environments. This study presents a comprehensive benchmarking of YOLO versions 8 through 12 across a range of SBCs, including Raspberry Pi4/5, NVIDIA Jetson Nano, Jetson Orin, and LattePanda, under different power modes. Key performance metrics, including inference speed (FPS), detection accuracy (mAP), RAM usage, and computational complexity (FLOPs), are evaluated. These findings offer practical insights for developers and researchers to select optimal YOLO variants and SBC configurations for real-time edge deployment.

The YOLO (You Only Look Once) object detection models have undergone rapid evolution, with each version introducing architectural enhancements aiming to improve speed, accuracy, and deployment. Simultaneously, Single-Board Computers (SBCs) have advanced to support increasingly complex AI models in edge environments. This study presents a comprehensive benchmarking of YOLO versions 8 through 12 across a range of SBCs, including Raspberry Pi4/5, NVIDIA Jetson Nano, Jetson Orin, and LattePanda, under different power modes. Key performance metrics, including inference speed (FPS), detection accuracy (mAP), RAM usage, and computational complexity (FLOPs), are evaluated. These findings offer practical insights for developers and researchers to select optimal YOLO variants and SBC configurations for real-time edge deployment.

Autism is a challenging brain disorder affecting children at global and national scales. Applied behavior analysis is commonly conducted as an efficient medical therapy for children. This paper focused on one paradigm of applied behavior analysis, imitation, where children mimic certain lessons to enhance children’s social behavior and play skills. This paper introduces IMITASD, a practical monitoring assessment model designed to evaluate autistic children’s behaviors efficiently. The proposed model provides an efficient solution for clinics and homes equipped with mid-specification computers attached to webcams. IMITASD automates the scoring of autistic children’s videos while they imitate a series of lessons. The model integrates two core modules: attention estimation and imitation assessment. The attention module monitors the child’s position by tracking the child’s face and determining the head pose. The imitation module extracts a set of crucial key points from both the child’s head and arms to measure the similarity with a reference imitation lesson using dynamic time warping. The model was validated using a refined dataset of 268 videos collected from 11 Egyptian autistic children during conducting six imitation lessons. The analysis demonstrated that IMITASD provides fast scoring, takes less than three seconds, and shows a robust measure as it has a high correlation with scores given by medical therapists, about 0.9, highlighting its effectiveness for children’s training applications.

The use of mixed spatial resolutions in multi-view video coding is a promising approach for coding videos efficiently at low bitrates. It can achieve a perceived quality, which is close to the view with the highest quality, according to the suppression theory of binocular vision. The aim of the work reported in this paper is to develop a new multi-view video coding technique suitable for low bitrate applications in terms of coding efficiency, computational and memory complexity, when coding videos, which contain either a single or multiple scenes. The paper proposes a new prediction architecture that addresses deficiencies of prediction architectures for multi-view video coding based on H.264/AVC. The prediction architectures which are used in mixed spatial-resolution multi-view video coding (MSR-MVC) are afflicted with significant computational complexity and require significant memory size, with regards to coding time and to the minimum number of reference frames. The architecture proposed herein is based on a set of investigations, which explore the effect of different inter-view prediction directions on the coding efficiency of multi-view video coding, conduct a comparative study of different decimation and interpolation methods, in addition to analyzing block matching statistics. The proposed prediction architecture has been integrated with an adaptive reference frame ordering algorithm, to provide an efficient coding solution for multi-view videos with hard scene changes. The paper includes a comparative performance assessment of the proposed architecture against an extended architecture based on the 3D digital multimedia broadcast (3D-DMB) and the Hierarchical B-Picture (HBP) architecture, which are two most widely used architectures for MSR-MVC. The assessment experiments show that the proposed architecture needs less bitrate by on average 13.1 Kbps, less coding time by 14% and less memory consumption by 31.6%, compared to a corresponding codec, which deploys the extended 3D-DMB architecture when coding single-scene videos. Furthermore, the codec, which deploys the proposed architecture, accelerates coding by on average 57% and requires 52% less memory, compared to a corresponding codec, which uses the HBP architecture. On the other hand, multi-view video coding which uses the proposed architecture needs more bitrate by on average 24.9 Kbps compared to a corresponding codec that uses the HBP architecture. For coding a multi-view video which has hard scene changes, the proposed architecture yields less bitrate (by on average 28.7 to 35.4 Kbps), and accelerates coding time (by on average 64 and 33%), compared to the HBP and extended 3D-DMB architectures, respectively. The proposed architecture will thus be most beneficial in low bitrate applications, which require multi-view video coding for video content depicting hard scene changes.