Microscale robots have a variety of potential applications in medicine, environmental monitoring, and tissue engineering, due to their small size and capabilities of sensing and manipulation at the small scale [1]. Recent research has demonstrated their potential in applications ranging from ocular drug delivery and in vitro fertilization to root canal prevention and tumor treatment [2, 3]. The most common actuation methods for microscale robots are acoustic and electromagnetic actuation [4]. Acoustic microrobots, for instance, can be manipulated using sound waves to achieve precise movements, while electromagnetic microrobots rely on magnetic fields for their actuation and control. Traditional open-loop control systems for acoustic and magnetic microrobots often fail to provide the necessary accuracy and reliability required for the above applications [5].

Aquaculture involves cultivating marine and freshwater organisms, with real-time monitoring of aquatic parameters being crucial in fish farming. This thesis proposes an IoT-based framework using sensors and Arduino for efficient monitoring and control of water quality. Different sensors including pH, temperature, and turbidity are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a CSV file in an IoT cloud named Thingspeak through the Arduino Microcontroller. In the experimental part, we collected data from 5 ponds with various sizes and environments. After getting the real-time data, we compared these with the standard reference values. As a result, we can make the decision about which ponds are satisfactory for cultivating fish and what is not. After that, we labeled the data with 11 fish categories including Katla, sing, prawn, rui, koi, pangas, tilapia, silvercarp, karpio, magur, and shrimp. In addition, the data were analyzed using 10 machine learning (ML) algorithms containing J48, Random Forest, K-NN, K*, LMT, REPTree, JRIP, PART, Decision Table, and Logit boost. After experimental evaluation, it was observed among 5 ponds, only three ponds were perfect for fish farming, where these 3 ponds only satisfied the standard reference values of pH (6.5-8.5), Temperature (16-24)oC, Turbidity (below 10)ntu, Conductivity (970-1825){\mu}S/cm, and Depth (1-4) meter. Among the state-of-the-art machine learning algorithms, Random Forest achieved the highest score of performance metrics as accuracy 94.42%, kappa statistics 93.5%, and Avg. TP Rate 94.4%. In addition, we calculated the BOD, COD, and DO for one scenario. This study includes details of the proposed IoT system's prototype hardware.

With continual advancements in technology, efforts to develop robots simulating human behavior have intensified. Cognitive robotics, combined with artificial intelligence (AI), has proven effective in surveying and research analysis. However, despite progress, human intervention remains necessary, and incorporating AI into robotic systems continues to pose challenges. This paper explores methodologies to integrate AI into robotic designs, aiming to enhance human-robot interactions. Several approaches are proposed to improve robotic performance, including routines for efficient control in varied environments and the incorporation of digital image processing for enhanced line-of-sight capabilities. A key contribution of this work is testing robotic systems in real-time environments to assess efficiency relative to existing models. Additionally, the paper introduces a robotic system with universal control capabilities, suitable for industrial applications, developed and programmed on the Arduino platform. Features such as GPS control for safe operations and progressive memory algorithms for efficient memory management are presented, offering advancements in both industrial and research applications.

To achieve real-world functionality, robots must have the ability to carry out decision-making computations. However, soft robots stretch and therefore need a solution other than rigid computers. Examples of embedding computing capacity into soft robots currently include appending rigid printed circuit boards (PCBs) to the robot, integrating soft logic gates, and exploiting material responses for material-embedded computation. Although promising, these approaches introduce limitations such as rigidity, tethers, or low logic gate density. The field of stretchable electronics has sought to solve these challenges, but a complete pipeline for direct integration of single-board computers, microcontrollers, and other complex circuitry into soft robots has remained elusive. We present a generalized method to translate any complex two-layer circuit into a soft, stretchable form. This enabled the creation of stretchable single-board microcontrollers (including Arduinos) and other commercial circuits (including Sparkfun circuits), without design simplifications. As demonstrations of the method's utility, we embed highly stretchable (>300% strain) Arduino Pro Minis into the bodies of multiple soft robots. This makes use of otherwise inert structural material, fulfilling the promise of the stretchable electronics field to integrate state-of-the-art computational power into robust, stretchable systems during active use.

Fecal incontinence (FI) is a significant health issue with various underlying causes. Research in this field is limited by social stigma and the lack of effective replication models. To address these challenges, we developed a sophisticated rectal simulator that integrates power, control, and data acquisition systems with soft pouch actuators. The system comprises four key subsystems: mechanical, electrical, pneumatic, and control and data acquisition. The mechanical subsystem utilizes common materials such as aluminum frames, wooden boards, and compact structural components to facilitate the installation and adjustment of electrical and control components. The electrical subsystem supplies power to regulators and sensors. The pneumatic system provides compressed air to actuators, enabling the simulation of FI. The control and data acquisition subsystem collects pressure data and regulates actuator movement. This comprehensive approach allows the robot to accurately replicate human defecation, managing various feces types including liquid, solid, and extremely solid. This innovation enhances our understanding of defecation and holds potential for advancing quality-of-life devices related to this condition.

Residential outdoor water use in North America accounts for nearly 9 billion gallons daily, with approximately 50\% of this water wasted due to over-watering, particularly in lawns and gardens. This inefficiency highlights the need for smart, data-driven irrigation systems. Traditional approaches to reducing water wastage have focused on centralized data collection and processing, but such methods can raise privacy concerns and may not account for the diverse environmental conditions across different regions. In this paper, we propose a federated learning-based approach to optimize water usage in residential and agricultural settings. By integrating moisture sensors and actuators with a distributed network of edge devices, our system allows each user to locally train a model on their specific environmental data while sharing only model updates with a central server. This preserves user privacy and enables the creation of a global model that can adapt to varying conditions. Our implementation leverages low-cost hardware, including an Arduino Uno microcontroller and soil moisture sensors, to demonstrate how federated learning can be applied to reduce water wastage while maintaining efficient crop production. The proposed system not only addresses the need for water conservation but also provides a scalable, privacy-preserving solution adaptable to diverse environments.

This paper investigates the possibility of intuitive human-robot interaction through the application of Natural Language Processing (NLP) and Large Language Models (LLMs) in mobile robotics. We aim to explore the feasibility of using these technologies for edge-based deployment, where traditional cloud dependencies are eliminated. The study specifically contrasts the performance of GPT-4-Turbo, which requires cloud connectivity, with an offline-capable, quantized version of LLaMA 2 (LLaMA 2-7B.Q5 K M). Our results show that GPT-4-Turbo delivers superior performance in interpreting and executing complex commands accurately, whereas LLaMA 2 exhibits significant limitations in consistency and reliability of command execution. Communication between the control computer and the mobile robot is established via a Raspberry Pi Pico W, which wirelessly receives commands from the computer without internet dependency and transmits them through a wired connection to the robot's Arduino controller. This study highlights the potential and challenges of implementing LLMs and NLP at the edge, providing groundwork for future research into fully autonomous and network-independent robotic systems. For video demonstrations and source code, please refer to: https://tinyurl.com/MobileRobotGPT4LLaMA2024.

It is very important to locate survivors from collapsed buildings so that rescue operations can be arranged. Many lives are lost due to lack of competent systems to detect people in these collapsed buildings at the right time. So here we have designed a hand gesture controlled robot which is capable of detecting humans under these collapsed building parts. The proposed work can be used to access specific locations that are not humanly possible, and detect those humans trapped under the rubble of collapsed buildings. This information is then used to notify the rescue team to take adequate measures and initiate rescue operations accordingly.

Falls represent one of the most detrimental occurrences for the elderly. Given the continually increasing ageing demographic, there is a pressing demand for advancing fall detection systems. The swift progress in sensor networks and the Internet of Things (IoT) has made human-computer interaction through sensor fusion an acknowledged and potent approach for tackling the issue of fall detection. Even IoT-enabled systems can deliver economical health monitoring solutions tailored to pregnant women within their daily environments. Recent research indicates that these remote health monitoring setups have the potential to enhance the well-being of both the mother and the infant throughout the pregnancy and postpartum phases. One more emerging advancement is the integration of 'panic buttons,' which are gaining popularity due to the escalating emphasis on safety. These buttons instantly transmit the user's real-time location to pre-designated emergency contacts when activated. Our solution focuses on the above three challenges we see every day. Fall detection for the elderly helps the elderly in case they fall and have nobody around for help. Sleep pattern sensing is helpful for pregnant women based on the SPO2 sensors integrated within our device. It is also bundled with heart rate monitoring. Our third solution focuses on a panic situation; upon pressing the determined buttons, a panic alert would be sent to the emergency contacts listed. The device also comes with a mobile app developed using Flutter that takes care of all the heavy processing rather than the device itself.

A smart grid includes a broad range of sophisticated sensors that constantly assess the state of the grid and the electrical power demand and availability, with the aim of optimizing the energy supply. In the future, the power grid will evolve into a cyber-physical system where smart devices will allow advanced monitoring and control. In this context, along with the smart home, consumers play more active roles in the management of energy consumption, and Home Energy Management Systems (HEMSs) emerge as hardware and software technology to monitor and to provide feedback on energy consumption at home and, so, to support effective habits from the point of view of energy saving. HEMSs are a growing sector in the modern era of the smart grid and smart homes. Specifically, in Colombia, people have become increasingly dependent on energy, so that consumption is increasing year after year. Considering that the end user generates a notable amount of total energy consumption, and that the residential sector makes a considerable contribution, the DPN of the Colombian government (National Planning Department; by its acronym in Spanish) has included this sector in the energy efficiency goals in the country, for which reason HEMS technologies have been considered energy efficiency applications [1]. Those HEMSs have emerged as a solution that allows the residential sector to manage the energy consumption more efficiently. However, according to [2], energy management technologies for residential users have not yet been introduced in Colombia; [3] suggests that economic drawbacks are the main barrier to HEMS adoption.