The growth of automotive industry not only focus on power efficiency and carbon emission reduction, it also has makes further progress in the areas of advanced driver assistance systems (ADAS) and autonomous driving (...
ISBN:
(纸本)9798350329575
The growth of automotive industry not only focus on power efficiency and carbon emission reduction, it also has makes further progress in the areas of advanced driver assistance systems (ADAS) and autonomous driving (AD) technologies. With the progressive technology breakthrough and heavily studies in these two areas, the demand for imaging cameras is steadily increasing to support applications such as lane detection, traffic sign detection, pedestrian/vehicle recognition and driver monitoring. Consequently, this drives higher demand for image sensor packages to meet stringent automotive reliability requirements. In the past, high reliability image sensor packages are typically with ceramic based packages, these tend to have considerably higher costs and longer development cycles than laminate-based packages which are normally used in high speed, RF or MEMS market segment. Therefore, it is important to ensure a high reliability for the laminate-based packages by studying the package design. This can be done by finite element analysis (FEA) which is a fast and cost-efficient method in analyzing the stress experienced by the package. This paper discusses the effects of different package designs on the package stress using the FEA simulation method.
Software Defined Wide Area Network (SD-WAN) is rapidly becoming an attractive solution for enterprise networks as it offers several benefits such as cost efficiency, increased bandwidth, and improved application perfo...
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ISBN:
(纸本)9798350399806
Software Defined Wide Area Network (SD-WAN) is rapidly becoming an attractive solution for enterprise networks as it offers several benefits such as cost efficiency, increased bandwidth, and improved application performance. However, SD-WAN also brings new challenges that must be addressed for effective deployment (i.e. openness, interoperability, network automation, monitoring, QoS guarantees, scalability and security). In this paper, we highlight the criticalities of this technology and analyze the solutions proposed by the state of the art. We then present a scalable framework based on distributed Reinforcement Learning agents for guaranteeing availability and QoS to business applications. We believe that our work provides valuable insights into the opportunities and challenges of SD-WAN technology and offers new perspectives for future research in this area.
The rapid advancement of Intelligent Transportation systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless sensor Networks (WSNs). However, congestion in data-heavy Internet o...
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ISBN:
(纸本)9798331527549
The rapid advancement of Intelligent Transportation systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model’s adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework’s advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc
We propose an interferometric fiber-optic sensor and investigate its ability to measure the partial discharge in electrical applications. Preferable performance can be achieved under system self-noise level of 10−6 ra...
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In this paper, a distributed algorithm is presented that localizes large number of sensors in localizable wireless sensor networks. It is well known that a network is localizable if and only if the underlying graph is...
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Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses t...
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ISBN:
(纸本)9791188428137
Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses the challenge of securing UAM networks while also emphasizing generalizability of the security solution to protect heterogeneous UAVs against threats that compromise their stability, reliability and can cause catastrophic failures such as a crash landing. The deployment of traditional machine learning (ML) based intrusion detection systems (IDSs) is often hampered in real-world applications due to a lack of generalizability of the security solution. As a result, the system fails to provide adequate security across the varying models and platforms of UAVs, each with its unique statistical properties and data distributions. To address these challenges, we focus on employing a comprehensive set of UAV sensor parameters, tailored feature engineering and selection to develop multi-stage cross-validated ensemble learning systems to facilitate generalized detection of attack and non-attack cases. For additional analysis, we cross-validate the models using two different cross-validation techniques. The proposed stacking ensemble systems provide the overall best performance, with AUC within the range of 92% to 100% across different crossvalidations. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.
Wearable devices have transformed from novelties into indispensable companions for millions, finding applications in health and wellness, empowering individuals to proactively manage their well-being. Among these, pul...
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The proliferation of IoT-enabled wireless sensor networks (WSNs) has ushered in a multitude of applications but has also heightened concerns regarding data integrity and security. This underscores the critical necessi...
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The growing demand for soft intelligent systems, which have the potential to be used in a variety of fields such as wearable technology and human-robot interaction systems, has spurred the development of advanced soft...
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The growing demand for soft intelligent systems, which have the potential to be used in a variety of fields such as wearable technology and human-robot interaction systems, has spurred the development of advanced soft transducers. Among soft systems, sensor-actuator hybrid systems are considered the most promising due to their effective and efficient performance, resulting from the synergistic and complementary interaction between their sensor and actuator components. Recent research on integrated sensor and actuator systems has resulted in a range of conceptual and practical soft systems. This review article provides a comprehensive analysis of recent advances in sensor and actuator integrated systems, which are grouped into three categories based on their primary functions: i) actuator-assisted sensors for intelligent detection, ii) sensor-assisted actuators for intelligent movement, and iii) sensor-actuator interactive devices for a hybrid of intelligent detection and movement. In addition, several bottlenecks in current studies are discussed, and prospective outlooks, including potential applications, are presented. This categorization and analysis will pave the way for the advancement and commercialization of sensor and actuator-integrated systems. This review highlights the recently demanding soft sensor-actuator hybrid intelligent systems and groups based on their primary functions: i) actuator-assisted sensors for intelligent detection, ii) sensor-assisted actuators for intelligent movement, and iii) sensor-actuator interactive devices for hybrid of intelligent detection and movement. In addition, several bottlenecks are discussed, and prospective outlooks, including potential applications, are ***
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