Short-range reliable and secure communication is a major priority in the tactical, military and disaster response settings where the traditional communication infrastructure is either off-line or prone to interception. Current VHF/UHF radios and software-defined radios are popular but large-sized devices and require lots of power, making them not suitable to be used as lightweight wearable devices with seamless hand-free use. In this paper, the design and theoretical framework of a miniature, LoRa based encrypted intercommunication device that can be used in secure field communication over a range of 1-1.5km and under line-of-sight conditions is provided. The suggested system consists of a voice-activated acquisition block, digital audio compression, an embedded microcontroller processor, and AES-128 encryption followed by a low-power transmission via the LoRa protocol. Through the ability of chirp spread spectrum modulation to utilize the long-range and low-energy properties, the system is guaranteed reliable communications coupled with low power consumption and low electromagnetic footprint. The theoretical analysis of the proposed communication range is justified using a link-budget that justifies the practicability of the communication range in the real propagation conditions. This architecture focuses on infrastructural agnosticism, peer-to-peer security as well as wearable ergonomics. The given scheme shows the possibilities of LoRa technology in the scope of other traditional IoT telemetry, and it can be further extended to include secure tactical voice communication platforms.

Validating Electronic Control Units (ECUs) for vehicle dynamics normally depends on real vehicles, which makes testing costly, time-consuming, and difficult to repeat. To address these issues, a simulation framework has been developed in the CANoe (Controller Area Network open environment) that combines CAPL (Communication Access Programming Language) scripting with Panel Designer. This setup reproduces key driving operations-such as gear shifting, braking, and creeping-and displays system responses through an interactive interface. Unlike earlier approaches that relied only on CANoe and CAPL, the inclusion of Panel Designer provides both input generation and clear output visualization within the same environment. A comparison with manual vehicle-level testing highlights improvements in flexibility, repeatability, and efficiency, while reducing dependence on physical prototypes. The framework offers a practical tool for engineers to accelerate ECU validation and strengthen safety assessments in the development of advanced and autonomous vehicles.

Traditional robotic grippers designed for collaborative robots have often been constrained to specific part geometries or low-temperature operations, creating a need for more adaptable solutions. A high-temperature gripper has been developed with fingers made from alloy steel 4140 and an aluminium oxide ceramic insulator to support automation during postprocessing in metal additive manufacturing (AM). Mechanical and thermal characterization tests have been performed to validate the insulator's performance under extreme conditions. Thermal simulations have indicated a temperature difference of 767.58 degrees C across the insulator when subjected to a 1000 degrees C steel plate, confirming its role as an effective thermal barrier. The gripper has been designed to withstand temperatures up to 1000 degrees C and integrated with thermocouples for continuous temperature monitoring during manipulation. This advancement has enabled safe handling of heated components, reduced risks to human operators, and supported greater automation in high-temperature environments, thereby improving safety and productivity in demanding industrial settings.

Magnetic Resonance Imaging (MRI) is a powerful diagnostic tool, but its slow acquisition speed poses challenges in clinical efficiency, cost, and patient comfort. Deep learning (DL) has emerged as a transformative approach to reconstruct high-quality MR images from undersampled k-space data, significantly accelerating scan times without sacrificing image fidelity. This paper explores recent advances in DL-based MRI reconstruction, emphasizing the need for robust, efficient, and clinically viable solutions. We examine key aspects such as architectural innovations, loss functions, data consistency enforcement, and hybrid-domain modeling, while highlighting the limitations of conventional evaluation metrics like SSIM and MSE. Emphasis is placed on diagnostic accuracy, robustness to noise and artifacts, and adaptability across different scanners and protocols. In the context of CyberPhysical Systems (CPS), we envision integrating these DL models with IoT-enabled healthcare platforms, enabling real-time, intelligent imaging workflows. We also address data scarcity and transfer learning as enablers for broad clinical adoption. By aligning deep learning strategies with CPS principles, this work contributes toward building scalable and sustainable medical imaging systems. Our findings aim to support the development of next-generation smart healthcare infrastructure by bridging advanced computation with clinical utility.

This paper provides insights into energy-efficient operation and power control schemes for maximising the performance of 6G communication systems. The power consumption model, energy maximisation, and power control optimisation techniques have been explored in depth. The literature review indicates an unprecedented growth in high data rate demands, leading to a significant increase in energy consumption. Moreover, green design and dense deployment of massive antenna arrays make it necessary to consider the power aspects of 6G and suggest valuable recommendations for future research in next-generation communication systems. This research explores the Filtered Bank Multicarrier (FBMC) technique to advance next-generation communication models. The investigation encompasses fundamental aspects of both downlink and uplink communication strategies, ultimately aiming to optimise communication resources under equitable constraints. Three primary FBMC versions, namely, FBMC-Quadrature Amplitude Modulation (QAM), FBMC-Offset QAM, and block spread FBMC-OQAM, in conjunction with the conventional Orthogonal Frequency Division Multiplexing (OFDM). Experimental results from a testbed confirm the effectiveness of the data-spreading approach despite increased computational complexity and validate its feasibility. Furthermore, the document proposes enhancements to the data spreading method through an algorithm facilitating spreading across arbitrary time-frequency positions within the spectrum. These improvements contribute to a more resilient and efficient communication framework, showcasing the potential for FBMC to be a valuable tool in the evolution of communication technologies.

The Internet of Things (IoT) has revolutionized global device interconnectivity, fostering seamless data exchange through expansive sensing networks. Similarly, swarm robotics emulates the collective behaviors observed in nature, such as bird flocks and ant colonies, aiming to adapt these principles for practical use. This paper explores the dynamics of robotic swarms operating under a centralized network model using master-slave communication. Two implementation approaches are examined: direct, small-scale interactions utilizing device MAC addresses and large-scale connectivity facilitated by an IoT-based cloud server. Our experiments employ the NodeMCU ESP8266 platform, which utilizes the IEEE 802.11 Wi-Fi standard for data handling and transfer. For cloud-based communication, we utilize the ThingSpeak server to manage data flow. This study evaluates the strengths and limitations of these approaches in fostering swarm behavior, aiming to derive strategies for enhancing the performance and coordination of swarm robotic systems.

The automotive industry faces a significant challenge in the validation process, wherein engineers must physically visit vehicles to test and simulate faults from previous software iterations. These faults are then replicated in updated software to assess rectifications. The discussed process entails turning on the ignition, cranking the vehicle, performing brake presses, using the shifter for input commands, and scrutinizing Instrument Panel Cluster outputs while observing CAN signals graphically to evaluate Electronic Control Unit (ECU) functionality. This manual approach is time-consuming and often hindered by vehicle availability, necessitating a shift towards bench-level testing. This paper introduces a model that demonstrates vehicle functionality using panel design and CAPL (Communication Access Programming Language) within the CANoe environment. The model encompasses gear engagement and disengagement, vehicle speed variations, and various engine states. By utilizing bench-level testing and simulation techniques, engineers can efficiently evaluate software updates, identify faults, and verify ECU functionality without relying on physical vehicle access. This approach not only streamlines the validation process but also enhances accuracy and control in software modification assessments.

In this generation, deep learning techniques are widely used for sign language prediction. In this paper, a deep learning model is proposed for American sign language detection using webcam images and transfer learning. The particular model is designed for a real-time sign language detection. The author claims 98% of accuracy for this designed model, when trained with a total of 15 images for each gesture. Jupyter notebook is used as the environment for working out this research. Cuda, cudnn graphic processor upgraders are also utilised in this research for training the model. To make real-time detections easy, a local environment is used rather than a cloud system for implementing the code. The main aim of this research work is to create a model in order to identify and detect the sign language alphabets and some very frequently used gestures. The model is designed based on deep learning by using the convolutional neural networks and single shot detector algorithm to surpass the difficulty that is faced by the speech impaired and normal people.

The study in this research investigates the application of a PID controller for achieving precise control of a robotic arm, with a focus on emulating human-like motions. The results demonstrate the successful generation of accurate joint angles during arm motion, showcasing the effectiveness of PID control in determining correct angles for seamless joint movements. The PID controller’s mechanism, evaluating the variance between desired and observed positions, ensures minimal deviation, leading to error-free arm motions marked by smoothness and precision. The robotic arm, developed in our laboratory, integrates the PID controller for controlled motion, offering a user-friendly and efficient system. Notably, the study extends the application of PID control by integrating an MPU9250 sensor onto an individual’s hand, enhancing the interaction between the robotic arm and the user. This integration enables real-time motion tracking and feedback, allowing the robotic arm to respond accurately to the user’s gestures and movements. The PID-controlled robotic arm not only excels in accuracy but also demonstrates real-time adjustability, adaptability, and stability, showcasing its versatility for potential applications in diverse fields. This research lays a foundation for advancing PID-controlled robotic systems, emphasizing precision, adaptability, and intuitive operation, with implications for industrial automation, medical assistance, and interactive human-robot collaboration.

A robotic left arm has been developed in this research to assist individuals in safely navigating hazardous environments, utilizing Internet of Things (IoT) technology to synchronize its movements with those of a human operator in real-time. The primary objective of this work was to enhance the precision and responsiveness of the arm. Sensors and microcontrollers (ESP32, Arduino UNO) were integrated into the robotic arm to collect and transmit movement data. Advanced machine learning algorithms, including Inverse Kinematics, Reinforcement Learning, and Deep Learning, were employed to facilitate the arm's learning process and improve its actions. The system was evaluated in simulated hazardous scenarios to assess its performance. The results indicate that the robotic arm can adjust and move accurately, aligning with the human operator's arm movements. The robotic arm safely manages hazardous materials, demonstrating high precision in executing complex tasks. These findings underscore the effectiveness and safety enhancement achieved by integrating IoT and intelligent algorithms into robotic systems for high-risk occupations. This technology significantly enhances emergency response capabilities and critical operations, delivering dependable and precise robotic assistance. Overall, this research underscores the potential of IoT-enabled robotic arms to revolutionize human-robot interactions in demanding environments. By showcasing how real-time synchronization and advanced algorithms enhance the functionality and safety of robotic systems, this research presents a promising solution for strengthening the operational safety and efficiency in hazardous situations. The development and utilization of this technology represent substantial progress in fields necessitating precise and dependable robotic assistance, ultimately promoting improved collaboration between humans and robots in high-risk contexts.

Swarm robotics involves coordinating the behaviour of multiple robots to perform complex tasks collaboratively. Our study explores using low-cost NodeMCU ESP8266 devices to establish master-slave communication in swarm robotics utilizing the Internet of Things (IoT), focusing on different network topologies: star, mesh, and ring. Unlike existing research that uses more expensive or less accessible technologies, our approach demonstrates that these affordable devices can effectively manage communication and coordination in swarm systems. Our experiments reveal that the star topology is the most efficient for master-slave communication, allowing for aggregation, pattern formation, and coordinated motion tasks. This research highlights the potential of using low- cost devices for efficient communication in swarm robotics, offering significant implications for various industries.

Day by day, because of the increase in the population, energy use also increases, leading this generation towards the end of Non-Renewable sources. A free quotation which is completely unutilized, is available, but very few people are interested in using it, which is Solar Energy. In Solar Energy, this generation can have one-time investments and utilize a free energy source for a long time. The main problem is trust and past investments, which few governments support by giving multiple subsidies and benefits to those who want to use them. In this chapter, the author enlightens the technical know-how of Solar energy systems and their advantages and disadvantages from a technical perspective.

Big Data is a massive collection of data that continues to grow dramatically over time. It is a data set that is so huge and complicated that no standard data management technologies can effectively store or process it. Big data is similar to regular data, but it is much larger. “There's no doubt that the volumes of data presently available are vast, but that's not the essential element of this new data ecosystem,” says one expert. The term “big data” is now commonly used to refer to the application of predictive analytics. New correlations can be discovered by analyzing data sets to “identify economic trends, prevent diseases, combat crime, and so on.” In sectors such as Internet searches, financial technology, healthcare analytics, geographic information systems, urban informatics, and business informatics, scientists, corporate executives, medical practitioners, advertising, and governments all face challenges with enormous data sets.

Day by day, because of the increase in the population, energy use also increases, leading this generation towards the end of Non-Renewable sources. A free quotation which is completely unutilized, is available, but very few people are interested in using it, which is Solar Energy. In Solar Energy, this generation can have one-time investments and utilize a free energy source for a long time. The main problem is trust and past investments, which few governments support by giving multiple subsidies and benefits to those who want to use them. In this chapter, the author enlightens the technical know-how of Solar energy systems and their advantages and disadvantages from a technical perspective.<br>

In this generation of high-speed internet many research organizations are still busy finding the best candidate for the upcoming next generation of cellular mobile communication. These days mobile communication is divided into two one is calling service and second is internet connectivity. Though many waveforms have been suggested for the fifth generation of mobile communication but out of which getting a compared output results on the basis of channel estimation is required. Through this research paper, FBMC Aux, FBMC, and OFDM arc compared in terms of BER and PAPR to fmd our best candidate for future 5th generation mobile and data communication. A comparison of the time index is also done to verify the merits and demerits of the FBMC and FBMC Aux in this paper.

The mobile cellphone network in this generation has become one of the most effective networking innovations of the last several years. The introduction of the latest phones and devices in the past few decades has reflected in a massive increase in network traffic. Various new systems have already appeared to stay on top of wireless technology with the prevalence of more intelligent platforms that communicate with data centers and with each other via high-speed internet-connected devices. In the next generation, the data rate required will be very much more than that of the current data rate which we are currently using for our mobile communication. Fifth-generation mobile communication is still a concept with many ideas of the type of technology that can be used in it. The author will enlighten and discuss several technologies that can be used in 5th generation wireless communication in this paper.

The COVID-19 coronavirus pandemic is an unparalleled threat intoday's quickly developing climate, and we face it as a global community. Like climate change, it is challenging our resilience from environmental health, social security, and government, to knowledge exchange and economic policy in all sectors of the economy and all fields of growth. So much as climate change, everybody's coming together would require the initiative. Throughout Europe and America, several organizations have mobilized to ensure that the neediest are not left behind, encouraging emergencies and disruptions avoidance and preparedness. The coronavirus outbreak has highlighted the growing community's strengths and vulnerabilities that it has influenced, and has provided us with the ability to benefit from each other's accomplishments and shortcomings. The comparison graph has also been shown in this paper displaying European and American scenarios. The globe might feel smaller amid disaster states and global travel bans, but it is a period when teamwork and looking outward were never more relevant.

Next-generation applications in robotics and mechatronics will necessitate basic, flexible, compact, and affordable technologies. Technologies could be easy due to the virtual elimination of small-level feedback control, detectors, cabling, and computers. Traditional electric motors, including linear motors and drivetrain, are not really suitable for Boolean technology, though, because they become complicated, bulky as well as costly. Such automation was not well recognized and thus was encountered with main issues of usability. So thus in the next generation, the latest trend which is the Internet of things will be used with that of the devices and technologies which we use in our day-to-day life to do our work more efficiently and with very ease. Two-way communication which may be stated as full-duplex communication, where the device communicates with another device of its own and work accordingly, is been termed as the Internet of things and can be understood as stating it as an interconnection of things or devices.

Orthogonal frequency division multiplexing (OFDM) is a novel multi-carrier modulation scheme where linearly modulated streams of data are split into several sub-streams and each occupies much lesser bandwidth than the total bandwidth of the signal. As compared to other single-carrier transmission schemes, the prime benefit of OFDM is its capability to survive the critical channel conditions without the use of complex equalisers. Its inevitable role in prohibiting inter-symbol interference (ISI) and improving the signal-to-noise ratio (SNR) has enhanced the efficacy in communicating over large distances. As OFDM is favourable even in transmitting information involving very high rates of data and provides much better performance; therefore, it is the focal process behind the new generation wireless networks such as 4G mobile communications, digital television and audio broadcasting. M-ary quadrature amplitude modulation (M-QAM) is the most preferred modulation scheme in wireless communication popularly known for its high noise immunity, much more efficient utilisation of channel bandwidth (spectral efficiency) for the same average signal power and its reliability for higher data rate transmission over the same channel. The central concept of an OFDM system involving the QAM scheme for modulation of data has been discussed in this work. This study analyses and compares the bit error rate (BER) performance of OFDM systems against the parameters of signal-to-noise ratio (SNR) among different orders (‘M’ values) of QAM (i.e. 4, 16, 32, 64, 128 and 256-QAM) over AWGN channel. All simulations are performed in MATLAB only.