Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) Ambient sensors, researchers engineers have sought to take advantage these advances accurately efficiently detect classify human activities. This research paper presents an advanced methodology for localization recognition, utilizing smartphone IMU, Ambient, GPS, Audio...

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm research. This boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite considerable volume existing research, greater portion it has primarily concentrated on activities. Comparatively less emphasis been placed recognition localization patterns. In current study, we...

Vehicle detection and classification are the most significant challenging activities of an intelligent traffic monitoring system. Traditional methods highly computationally expensive also impose restrictions when mode data collection changes. This research proposes a new approach for vehicle over aerial image sequences. The proposed model consists five stages. All images preprocessed in first stage to reduce noise raise brightness level. foreground items then extracted from these using...

Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers gyroscopes. Initially, these were used for device feature advancement, but now, they can be a variety applications. Human activity recognition (HAR) is an interesting research area that many applications like health monitoring, sports, fitness, medical purposes, etc. In this research, we designed advanced...

Advancements in smartphone sensor technologies have significantly enriched the field of human activity recognition, facilitating a wide array applications from health monitoring to personal navigation. This study utilized such advancements explore locomotion and localization recognition using data accelerometers, microphones, gyroscopes, magnetometers, GPS, applying Deep Polynomial Neural Networks (DPNN) Multilayer Perceptron (MLP) across three datasets: Continuous In-The-Wild Smart Watch...

Parkinson disease affect bodily functions and there is a growing need for advanced solutions to offer therapeutic advice patients. A framework using arti- facial intelligence machine learning techniques has been proposed address this. The system employs combination of RGB, inertial, depth sensors data. inertial signals have filtered notch filter obtain the optimal wearable sensor data by examining upper lower cutoff frequencies. Multiple features calculated, including mel frequency cepstral...

Machines need to be able recognize and understand complex visual surroundings function at their best in a variety of contexts. Here, we address the difficult problem multi-object recognition obtain sophisticated knowledge environments, tackling issues such as size, occlusion, fluctuations object traits, complicated backdrops. Our contribution is provide novel methods (Gaussian mixture model mean-shift algorithms) for inferring multiple segmentation visuals, introducing unique multiclass...

Systems must be capable of detecting and tracking autonomous vehicles for intelligent management control transportation. Even though several methods are used to create systems traffic monitoring, this article explains how detect track using pixel labeling particle filter algorithms. We suggested a novel technique that segments the image segmentation retrieve foreground objects. have divided our proposed model into following steps: at first, geo-referencing is find exact location; secondly,...

Hand gestures are an effective communication tool that may convey a wealth of information in variety sectors, including medical and education. E-learning has grown significantly the last several years is now essential resource for many businesses. Still, there not been much research conducted on use hand e-learning. Similar to this, frequently used by professionals help with diagnosis treatment.

The accurate classification of aerial images is a crucial task in remote sensing, with applications ranging from land cover mapping and urban planning to disaster response environmental monitoring. However, challenges such as limited labeled data, inherent data complexity, high computational demands often hinder the performance traditional methods. To address these challenges, we present an innovative framework that combines advanced segmentation techniques, diverse feature extraction...

Lossless hyperspectral images have the advantage of reducing data size, hence saving on storage and transmission costs. This study presents a dynamic pipeline hardware design for compressing decompressing using Joint Photographic Experts Group-Lossless (JPEG2000) algorithm. The proposed architecture was specifically tailored implementation Field Programmable Gate Array (FPGA) to accomplish efficient image processing. introduction pause mechanism effectively resolves issue coding errors...

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery decision support has recently been a popular area research exploration. Its usefulness is paramount because its relationship to the continuous involvement in improvement healthcare other related disciplines. As result this, huge amount have collected analyzed. These are made available community various shapes formats; their representation study form graphs or networks also an which many scholars...

Introduction Unmanned aerial vehicles (UAVs) are widely used in various computer vision applications, especially intelligent traffic monitoring, as they agile and simplify operations while boosting efficiency. However, automating these procedures is still a significant challenge due to the difficulty of extracting foreground (vehicle) information from complex scenes. Methods This paper presents unique method for autonomous vehicle surveillance that uses FCM segment images. YOLOv8, which...

Advanced traffic monitoring systems face significant challenges in vehicle detection and classification. Conventional methods often require substantial computational resources struggle to adapt diverse data collection methods.

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes. Various technologies, such as augmented reality-driven scene integration, robotic navigation, autonomous driving, guided tour systems, heavily rely this type of comprehension. This paper presents novel segmentation approach based the UNet network model, aimed at recognizing within an image. methodology begins with acquisition preprocessing...

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related e-healthcare. Different measurement conditions affect the daily routine monitoring, including sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present this domain that include a variety techniques pre-processing, descriptor extraction, reduction, along with classification...

Object-oriented analysis is a significant step that plays vital role in the success of software development. The planning and management stages, particular, profoundly rely on deliverance an accurate estimate takes software's complexity size into consideration. Today, several industries are transforming their development methodologies to Agile due its ability deliver value short time cost efficiency. However, methods prevent heavyweight modeling depend user stories drive estimation process....

Advances in machine vision systems have revolutionized applications such as autonomous driving, robotic navigation, and augmented reality.Despite substantial progress, challenges persist, including dynamic backgrounds, occlusion, limited labeled data.To address these challenges, we introduce a comprehensive methodology to enhance image classification object detection accuracy.The proposed approach involves the integration of multiple methods complementary way.The process commences with...