In recent years, significant advancements in the area of non-invasive and wearable technology have paved the way for widespread applications of personalized and remote health monitoring. Health monitoring enables proa...
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ISBN:
(数字)9798350374711
ISBN:
(纸本)9798350374728
In recent years, significant advancements in the area of non-invasive and wearable technology have paved the way for widespread applications of personalized and remote health monitoring. Health monitoring enables proactive management of one's well-being, and enhances overall health outcomes. With the advent of such technologies there are lots of platforms developed. But such platforms are not successful in providing the followings: 1) the ability to monitor all major health vitals in one unified application, & 2) the provision of a holistic health score derived from these non-invasive vitals for fitness measurement. In response, this paper presents a novel solution featuring: 1) An IoT-ML enabled platform for self-monitoring of important health vitals, and 2) A fitness assessment score derived through ML algorithm using the health vitals of a person. By integrating data on various health parameters including heart rate, blood oxygen levels, and sleep patterns, advanced machine learning models are developed to analyze this data and predict the fitness score that accurately reflects an individual's overall health and fitness level. The performance of the proposed estimation model is evaluated using R2 and mean square error (MSE) and the best value is achieved using the XGBoost regressor of values 0.967 and 0.0033. This solution empowers individuals to take control of their well-being and improve their overall quality of life. Overall, the integration of non-invasive methods and IoT sensors for health vitals monitoring and fitness assessment presents a novel approach with significant potential to revolutionize health care. By empowering individuals with real-time insights and a comprehensive fitness score, this solution facilitates informed decision-making and enables individuals to proactively manage their health and well-being, ultimately enhancing their overall quality of life.
Because of developments in the online sector, the occurrence of data breaches in web-based business systems is rising quickly. The attackers employ a range of different methods, including Distributed-Denial-of-Service...
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In the evolving landscape of telecom networks, the integration of 5G networks with 3D Non-Terrestrial Networks (NTN) presents pivotal use cases focussed on ensuring service continuity, ubiquity, and scalability. The s...
In the evolving landscape of telecom networks, the integration of 5G networks with 3D Non-Terrestrial Networks (NTN) presents pivotal use cases focussed on ensuring service continuity, ubiquity, and scalability. The scope of 3GPP Rel-17 addresses these challenges, emphasizing ubiquitous service coverage and extending 5G connectivity to remote and under-served areas. The recent adoption of Network Function Virtualization (NFV) and Software Defined Networking (SDN) empowers service providers to deliver resilient network services through flexible and programmable infrastructures. This work introduces an experimental testbed leveraging Multi-access Edge Computing (MEC) nodes to maintain service availability in the integrated 3D NTN networks. The deployed MEC-based 3D network demonstrates achieved service availability when terrestrial access is lost (emergency scenarios) or outside the network coverage area. In the scenario, a User Equipment (UE) initially connected to a terrestrial access network experiences a disruption in connectivity due to sudden damage, after which it seamlessly transitions to establish a connection with a satellite access network. A Low Earth Orbit (LEO) satellite constellation operating in regenerative mode with gNB as a payload is considered. Using edge computing functionalities close to UE, the performance of the deployed 5G NTN network improves the Quality of Service (QoS) with an acceptable range. The outcomes of this research will provide valuable insights into the design and implementation of resilient 5G service deployment in the 3D NTN networks.
The step to the success of startups is through overcoming competitors by adopting software innovations that improve businesses. Serverless computing model, recently, has intrigued a sizable number of startup professio...
The step to the success of startups is through overcoming competitors by adopting software innovations that improve businesses. Serverless computing model, recently, has intrigued a sizable number of startup professionals belonging to various sectors, including financial or IoT-enabled application developers. One of the main flaws is its heavy dependency on cloud providers, which can still result in hefty pricing to startups and stalling functions in applications. This article proposes a penaltyenabled serverless architecture for startups. The architecture can boost the economy of startups and can analyze the serverlessoriented cost-saving options in applications. The penalty-oriented approach could enable cloud architects, developers, and startups, to rethink the utilization of serverless functions; to gleam of with future innovations.
Object detection is an important task in computer vision. In earlier studies, object detection mostly works on single-modal. But recently, in order to improve detection efficiency, researchers start adding more modals...
Object detection is an important task in computer vision. In earlier studies, object detection mostly works on single-modal. But recently, in order to improve detection efficiency, researchers start adding more modals for improving the robustness of detection result. In this paper, we assume that all input modals which can convert to image style are able to complete object detection task through a unified model. We choose YOLOv7 as our baseline model and use RGB and infrared images from the public dataset Camel to test our hypothesis. Based on our experiment results, training RGB and IR images together obtains better performance than training individually, which shows that all image style modalities are possibly handled by a unified model and even achieve better results.
In this paper, we propose a novel approach for information concealment within speech signals encoded using the iLBC codec. Our method leverages the start state's residual samples. We explore the utilization of the...
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ISBN:
(数字)9798350361513
ISBN:
(纸本)9798350372304
In this paper, we propose a novel approach for information concealment within speech signals encoded using the iLBC codec. Our method leverages the start state's residual samples. We explore the utilization of the 3-bit quantized start state residual signal as an information hiding field. By strategically selecting specific quantization table indices based on the hidden bits, we achieved a remarkable four-fold enhancement in information hiding capacity while maintaining a high level of Perceptual Evaluation of Speech Quality (PESQ). This method offers a flexible balance between capacity and imperceptibility, allowing adaptation according to varying real-world scenarios. This adaptability empowers customization of information hiding techniques, making it highly applicable in diverse contexts.
New antenna technologies like phased array antennas are crucial to enhance high-speed data transmission in 5G and 6G high-frequency communications (mmWave/Terahertz). By exploiting spatial-temporal beamforming space (...
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New antenna technologies like phased array antennas are crucial to enhance high-speed data transmission in 5G and 6G high-frequency communications (mmWave/Terahertz). By exploiting spatial-temporal beamforming space (e.g., angle of arrival, angle of departure, time of arrival), directional antenna technologies can enable precise radio positioning. The feature has tremendous potential to be used in many futuristic applications, such as behavioral monitoring or assisting rescue teams moving inside a dark building. However, besides that positive side, precise radio positioning potentially poses significant tracking risks to mobile users. In this work, we demonstrate a case study of tracking a user in a restricted access building based on passively received signals. We then build an efficient mechanism to regenerate the trajectory of a target device by exploiting the characteristics of directional signals. We also exploit the capability of simultaneous localization and mapping using multipath channel information. Through theoretical analysis and simulation, we found that tracking risks of radio positioning techniques are real and challenging to resolve.
The 3rd Generation Partnership Project (3GPP) is actively working on incorporating non-terrestrial networks (NTNs) into the 5G system. NTNs integrate various aerial and space components like uncrewed aerial vehicles (...
The 3rd Generation Partnership Project (3GPP) is actively working on incorporating non-terrestrial networks (NTNs) into the 5G system. NTNs integrate various aerial and space components like uncrewed aerial vehicles (UAVs), airships, and satellites to offer flexible solutions for extending the ground systems. In the context of aerial radio access networks (ARANs), aerial base stations (ABSs) are deployed in clusters. However, due to the dynamic nature of ABSs, additional identity authentication is required between user equipment (UE), core network (CN), and ABSs. It is crucial to design lightweight authentication protocols that are subject to the limited computing resources and energy constraints of UAVs. We observe that aerial facilities possess multiple attributes, which can be leveraged to integrate identity authentication of the involved entities based on specific attribute sets. This study proposes an attribute-based authentication and key agreement protocol for ABSs and the CN, utilizing ciphertext-policy attribute-based encryption (CP-ABE). This approach enables access control based on physical attributes, ensuring secure communication and identity authentication in the ARANs. The protocol reduces the need for repeated execution of the authentication and key agreement (AKA) protocols between UE and the CN during ABS replacement. It employs elliptic curve cryptography (ECC) and fixed-length keys to minimize computational overhead during decryption, and also supports mutual authentication between ABSs during UE handover. We simulated the protocol on standard curve P-256, P -384, P-521 encryption performance, and analyze the relationship with attribute universe size. The design aligns with the 3GPP specifications, aiming for practical field application in the future.
Wireless networks have become a vital factor in our modern life. However, besides providing connectivity for many civil applications, wireless networks are the subjects of radio localization and user tracking. This wo...
Wireless networks have become a vital factor in our modern life. However, besides providing connectivity for many civil applications, wireless networks are the subjects of radio localization and user tracking. This work introduces a novel technique that exploits passive radio localization on multiple users for regenerating a restricted-access building drawing. Initially, from the data points generated by our built-in signal-to-image-based localization, we generate data points in an empty building drawing. Then by estimating the absolute distance among nearby data points, we group data points by a rectangle which represents the accessible area by human walk. Finally, we connect the center points of the generated rectangles to illustrate the accessible areas. The evaluation results show that the overall accessible area regeneration accuracy can reach up to 80%. Our technique can apply to many potential applications. For example, the police can safely determine the entrance/exit routes or walkable places to intrude in the drug raid mission. Another case is to expose the common visit places of the criminals or reveal the functions of an area in the restricted-access building.
This study delves into the application of Electroencephalogram (EEG)-based techniques for emotion and consciousness recognition, focusing on disorders of consciousness (DOC). By evaluating existing approaches in terms...
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ISBN:
(数字)9798350376517
ISBN:
(纸本)9798350376524
This study delves into the application of Electroencephalogram (EEG)-based techniques for emotion and consciousness recognition, focusing on disorders of consciousness (DOC). By evaluating existing approaches in terms of techniques, methodologies, and accuracies, the research highlights the efficacy of both machine learning and deep learning models. The key challenges were identified such as noise removal due to artifacts and variability in patient responses. The findings underscore the potential of advanced computational methods in enhancing the reliability and precision of EEG-based diagnostic tools, paving the way for better clinical interventions and deeper understanding of human emotions. Also, the findings show that using deep learning, the current works have achieved better results for identification of emotion.
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