Open Access

With the advancement of technology in 21st century, the marketspace of various categories like Consumer electronics, Automotive, Healthcare & medical devices, Industrial Automation, Telecommunication and Défense & Aerospace is being disrupted with the fast-paced changing technology. Companies aiming on faster real-time processing, AI integration and better energy efficiency. Embedded systems have played a major role in the growth of such industries. An Embedded system is a computer system designed for a specific function within a larger mechanical or electrical system [1]. With an aim to perform a specific task, the devices are designed using a microprocessor or microcontroller with required memory, Input/Output interface and an integrated development environment for software coding. With the advent of technology, Embedded systems gained popularity for performing specific task at a faster rate, slowly and steadily the manual labour/machines are reduced/replaced with the addition of technology. Sensors/electronic devices have also played a pivotal role as a ground for an Embedded system. With the development of devices, the researchers have also stress on finding more efficiency, memory, affordability, reliability, complexity and reusability of the devices. IoT devices rely on embedded systems for data processing and control. With the popularity of IoT devices like Smart home devices (Amazon Echo, Philips Smart bulbs), wearables devices, smart agricultural devices, connected vehicles and transport devices, the embedded system has gained grounds in the technological space and transforming every day’s life. With the success of embedded system in various categories the demand and the requirements grew from customers and complex device requirement for multitasking with efficiency became a necessity. Traditionally, C was used for most of the embedded devices due to its efficiency, portability and hardware access as C provided control to low level hardware using pointers and allowed manipulation of peripheral settings. C also provided the advantage of saving space as the code size is not large and uses embedded compilers like GCC, Keil etc. Furthermore, it is compatible with Real time operating system. With the enhancement of embedded hardware from 1960s [2], it provided room to perform complex tasks. With C, the code modularity and its reusability, code maintainability, bugs detection was not very efficient for complex embedded system, hence it allowed programming languages like C++ which supports object-oriented programming concept to gain grounds as it allows the code to use features like Encapsulation, Inheritance and Polymorphism. Additionally, it allowed better bug detection & correction along with code maintainability. The work will reflect the advantages and disadvantages of C++ in resource restricted environment and provide better code optimization across the real time application.

Enhanced signal transmission into the ground is essential for reliable subsurface imaging applications, including geological surveys, utility detection, and archaeological investigations. This work presents the design of a ground-coupled antenna integrated with a metamaterial structure to enhance efficiency and performance of Ground-Penetrating Radar (GPR) systems operating over a broad frequency range. The objective is to develop a compact size ultra-wideband antenna operational at low frequencies with good radiation characteristics. The design approach incorporates a single-layer metasurface with a cavity-backed Bowtie antenna to suppress impedance mismatch between the antenna and various ground conditions such as dry sand, wet sand, and clay. By modifying the effective permittivity of the metasurface to match the ground's dielectric properties, the design minimizes air-ground interface reflections and maximizes wave transmission into the subsurface. The proposed antenna achieves a relatively compact form factor with robust performance from 150 MHz to 3 GHz, making it suitable for deep-penetration and hence high-resolution applications. Extensive simulations have demonstrated good radiation efficiency, stable radiation pattern and gain at low frequencies in ground-coupled mode and also in free-space. While the overall antenna size is observed to increase at extremely low frequencies due to the cavity structure, an improved metasurface based on advanced unit cells promises miniaturized and highly efficient GPR antennas.

As the automotive industry continues to evolve, the integration of automatic components driven by microcontrollers has become increasingly prevalent. In safety-critical systems such as seat belt mechanisms, the reliability and responsiveness of these components are paramount to ensuring the safety of both drivers and pedestrians. Central to this concern is the assessment of the CPU load of real-time operating system (RTOS) microcontrollers, as their ability to execute tasks within specified time constraints directly impacts the system performance and, ultimately, safety outcomes. This research endeavors to address the need for rigorous evaluation methodologies for CPU load analysis in RTOS microcontrollers within automotive seat belt systems. The study commences by scrutinizing the current methodologies employed for CPU load analysis, with a particular focus on the prevalent pin toggling and Picoscope methods. While these methods have demonstrated efficacy in assessing CPU load, they often entail significant setup time and data extraction procedures, thus presenting a potential bottleneck in the evaluation process. To address these limitations, a novel methodology is proposed, leveraging the capabilities of the Lauterbach debugger and its Software Trace32 tool. This methodology aims to streamline the CPU load analysis process by enabling precise timing profiling of program functions and variables. By scripting within Trace32, data extraction is automated, reducing the time overhead associated with manual methods and enhancing overall efficiency. The proposed methodology holds promise for improving the accuracy and efficiency of CPU load analysis in RTOS microcontrollers. By providing a comprehensive understanding of the temporal behavior of program execution, it facilitates more nuanced insights into system performance and resource utilization, thereby enabling better-informed decision-making in the development and deployment of automotive safety systems. Empirical validation of the proposed methodology is conducted through a comparative analysis with existing approaches. Data is collected and analyzed from both the traditional pin toggling and Picoscope methods and the proposed Lauterbach Trace32-based methodology. By assessing the consistency and accuracy of results obtained from each approach, insights are gleaned into the efficacy of the proposed methodology and its potential for time savings. Through this research, I seek to contribute to the advancement of methodologies for CPU load analysis in RTOS microcontrollers, particularly within the context of automotive safety systems. By offering a more efficient and effective means of evaluating CPU load, my work aims to enhance the reliability and responsiveness of automotive seat belt systems, ultimately contributing to improved safety outcomes on the road.

As the Industry 4.0 is evolving, the previously separated Operational Technology (OT) and Information Technology (IT) is converging. Connecting devices in the industrial setting to the Internet exposes these systems to a broader spectrum of cyber-attacks. The reason is that since OT does not have much security measures as much as IT, it is more vulnerable from the attacker's perspective. Another factor contributing to the vulnerability of OT is that, when it comes to cybersecurity, industries have focused on protecting information technology and less prioritizing the control systems. The consequences of a security breach in an OT system can be more adverse as it can lead to physical damage, industrial accidents and physical harm to human beings. Hence, for the OT networks, certificate-based authentication is implemented. This involves stages of managing credentials in their communication endpoints. In the previous works of ivESK, a solution was developed for managing credentials. This involves a CANopen-based physical demonstrator where the certificate management processes were developed. The extended feature set involving certificate management will be based on the existing solution. The thesis aims to significantly improve such a solution by addressing two key areas that is enhancing functionality and optimizing real-time performance. Regarding the first goal, firstly, an analysis of the existing feature set shall be carried out, where the correct functionality shall be guaranteed. The limitations from the previously implemented system will be addressed and to make sure it can be applied to real world scenarios, it will be implemented and tested in the physical demonstrator. This will lay a concrete foundation that these certificate management processes can be used in the industries in large-scale networks. Implementation of features like revocation mechanism for certificates, automated renewal of the credentials and authorization attribute checks for the certificate management will be implemented. Regarding the second goal, the impact of credential management processes on the ongoing CANopen real-time traffic shall be a studied. Since in real life scenarios, mission-critical applications like Industrial control systems, medical devices, and transportation networks rely on real-time communication for reliable operation, delays or disruptions caused by credential management processes can have severe consequences. Optimizing these processes is crucial for maintaining system integrity and safety. The effect to minimize the disturbance of the credential management processes on the normal operation of the CANopen network shall be characterized. This shall comprise testing real-time parameters in the network such as CPU load, network load and average delay. Results obtained from each of these tests will be studied.

In the past ten years, applications of artificial neural networks have changed dramatically. outperforming earlier predictions in domains like robotics, computer vision, natural language processing, healthcare, and finance. Future research and advancements in CNN architectures, Algorithms and applications are expected to revolutionize various industries and daily life further. Our task is to find current products that resemble the given product image and description. Deep learning-based automatic product identification is a multi-step process that starts with data collection and continues with model training, deployment, and continuous improvement. The caliber and variety of the dataset, the design selected, and ongoing testing and improvement all affect the model's effectiveness. We achieved 81.47% training accuracy and 72.43% validation accuracy for our combined text and image classification model. Additionally, we have discussed the outcomes from the other dataset and numerous methods for creating an appropriate model.