Internet of Things (IoT) is an integration of the Sensor, Embedded, Computing, and Communication technologies. The purpose of the IoT is to provide seamless services to anything, anytime at any place. IoT technologies play a crucial role everywhere, which brings the fourth revolution of disruptive technologies after the internet and Information and Communication Technology (ICT). The Research & Development community has predicted that the impact of IoT will be more than the internet and ICT on society, which improves the wellbeing of society and industries. Addressing the predominant system-level design aspects like energy efficiency, robustness, scalability, interoperability, and security issues result in the use of a potential IoT system. This paper presents the current state of art of the functional pillars of IoT and its emerging applications to motivate academicians and researches to develop real-time, energy-efficient, scalable, reliable, and secure IoT applications. This paper summarizes the architecture of IoT, with the contemporary status of IoT architectures. Highlights of the IoT system-level issues to develop more advanced real-time IoT applications have been discussed. Millions of devices exchange information using different communication standards, and interoperability between them is a significant issue. This paper provides the current status of the communication standards and application layer protocols used in IoT with the detailed analysis. The computing paradigms like Cloud, Cloudlet, Fog, and Edge computing facilitate IoT with various services like data offloading, resource and device management, etc. In this paper, an exhaustive analysis of Edge Computing in IoT with different edge computing architectures and existing status are deliberated. The widespread adoption of IoT in society has resulted in privacy and security issues. This paper emphasizes on analyzing the security challenges, privacy and security threats, conventional mitigation techniques, and further scope for IoT security. The features like fewer memory footprints, scheduling, real-time task execution, fewer interrupt, and thread switching latency of Real-Time Operating Systems (RTOS) enables the development of time critical IoT applications. Also, this review offers the analysis of the RTOS's suitable for IoT with the current status and networking stack. Finally, open research issues in IoT system development are discussed.

I ncreasingly, researchers and practitioners have been using 3D models in a number of different areas. Boeing has more than 25 terabytes of online and near-line 3D geometry describing just the shape of its commercial airplane products. Archived model storage reaches into the hundreds of terabytes. Other companies that build physical products such as automobiles, appliances, aerospace vehicles, buildings, and so on rely on 3D geometry as the master for their product data information. The gaming and entertainment industries generate huge amounts of 3D geometry and visual environments that millions of people view daily. Three-dimensional visualization techniques have become valuable for people who try to understand and analyze vast quantities of data or derive relationships from seemingly unrelated sources. The number of people who need to visually comprehend 3D models of physical products has increased dramatically. In industrial settings, engineers and designers were often the only people who needed to view complex 3D models. That situation has changed. Salespeople rely on 3D mock-ups to show customers how a new product will look. Assembly mechanics look at 3D models to understand how parts will fit. Maintainability experts explore part assembly and disassembly sequences using 3D images augmented by haptic feedback. 1 Better software, more available storage, and increased computing power support the user base's expansion. The "geometry software" industry has matured to the point where, while some computation problems and long-term data viability still remain, 2 geometry kernels-such as Parasolid (http://www.

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This paper describes the development of an efficient speech recognition system using different techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and Hidden Markov Model (HMM). This paper explains how speaker recognition followed by speech recognition is used to recognize the speech faster, efficiently and accurately. MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. Then HMM is used on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values for the spoken word.