On-board Computer Research Articles

A Novel Hybrid XAI Solution for Autonomous Vehicles: Real-Time Interpretability Through LIME–SHAP Integration

The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP). Our framework combines the precision and globality of SHAP and low computational requirements of LIME, creating a balanced approach for onboard deployment with enhanced transparency. We evaluate the proposed framework on three different state-of-the-art models: ResNet-18, ResNet-50, and SegNet-50 on the KITTI dataset. The results demonstrate that our hybrid approach consistently outperforms traditional approaches by achieving a fidelity rate of more than 85%, interpretability factor of more than 80%, and consistency of more than 70%, surpassing the conventional methods. Furthermore, the inference time of our proposed framework with ResNet-18 was 0.28 s; for ResNet-50, it was 0.571 s; and that for SegNet was 3.889 s with XAI layers. This is optimal for onboard computations and deployment. This research establishes a strong foundation for the deployment of XAI in safety-critical AV with balanced tradeoffs for real-time decision-making.

UAV Hunter: A Net-Capturing UAV System with Improved Detection and Tracking Methods for Anti-UAV Defense

The abuse of UAVs poses a potential risk to social security, necessitating the investigation of anti-UAV methods to safeguard critical areas. However, the existing UAV countermeasures face challenges such as high environmental impact, restricted spatial deployment, and low cost-effectiveness. To address these limitations, we developed a novel anti-UAV system known as UAV Hunter, which adopts an airborne tether-net capture device with visual aids to counter unauthorized UAVs. This system employs an “Anti-UAV with UAV” scheme, comprising a ground control station and a net-capturing UAV. The operator utilizes the ground control station to determine the mission area and flight path and then controls the flight of the net-capturing UAV. During flight, the net-capturing UAV leverages its dual-mode sensor to continuously monitor the target area. Simultaneously, the onboard computer executes a UAV detection and tracking algorithm to search for unauthorized UAVs in real time. The results are relayed to the operator in real time, facilitating precise adjustments for the net-capturing UAV to launch the rope net accurately. The system successfully realizes the functions of dual-mode real-time detection and tracking, precise net capture, and efficient integrated control. Compared with existing methods, the developed system exhibits accurate recognition, rapid action, diverse application scenarios, and an enhanced human–machine interaction experience. Test results in the open environment further validate the feasibility and functional integrity of the system, demonstrating its capability to effectively capture low-altitude unauthorized UAVs.

Software Architecture Design of ASV Rybitwa: Development of an Autonomous Surface Vehicle for Dynamic Navigation and Task Execution

Abstract This paper introduces the software design of an Autonomous Surface Vehicle (ASV) named ASV Rybitwa. It was designed as an easily transportable platform capable of autonomous navigation and performing tasks during the RoboBoat 2024 competition, with emphasis on its modularity, generality and extendibility. The paper presents a software architecture for a small autonomous surface vehicle using novel tools that provide easy system integration and expansion. To meet such requirements, a generic control box connecting an autopilot (PX4) and a onboard computer (NVIDIA Jetson Nano) was developed so as to host the Robot Operating System 2 (ROS2) and to interact with sensors and actuators. Computer vision from three Oak-D cameras, equipped with AI algorithm support (YOLOv8), was used for navigation and obstacle avoidance. The decision-making system was designed using behavioural trees and computer vision, allowing the vehicle’s capabilities to be adapted to the needs of the current tasks and mission. In addition, an Omni X propulsion system was proposed, providing full holonomy and enhancing dynamic positioning and navigation capabilities in complex navigational conditions. In addition this paper describes lessons learned from ASV Rybitwa’s real-world testing during the aforementioned competition.

Design of motion control system for unmanned wheeled electric vehicle

Abstract The unmanned wheeled electric vehicle is a weapon platform that draws significant attention in current and future battlefields, and research on its motion control is fundamental. This paper introduces the design of a 6×6 unmanned wheeled electric vehicle and discusses the motion control methods applicable to this vehicle. The electric drive system of this vehicle comprises six independent asynchronous motors and corresponding motor controllers. A comprehensive controller designed based on DSP connects the six motor controllers. As it is an experimental platform, the motion control algorithms are deployed on an onboard computer, which connects to the comprehensive controller. Using the Backstepping method, the kinematic control algorithm for the 6×6 unmanned wheeled electric vehicle is designed. The global asymptotic stability of the control system is proven through the Lyapunov function, and its control performance is verified via simulation experiments. Finally, the system is deployed and tested on the actual vehicle. Experimental results show that the motion control system designed in this paper enables the unmanned wheeled electric vehicle to achieve effective motion control. The control laws derived from the Backstepping method are relatively simple and have low requirements for the sampling period. In practical systems, output speed magnitude and rate of change must be limited to stay within allowable system parameters.

Design and Verification of Thermal Control System of Communication Satellite

The multiple working modes, complex working conditions, frequent changes in external heat flux, and high power consumption of communication satellites all pose great difficulties to their thermal design. This paper mainly describes the design of a thermal control system for high-power communication satellites. Firstly, new efficient heat transfer technologies and thermal control materials for spacecraft are introduced. Secondly, the structure and internal heat source of the satellite are introduced. Thirdly, the external heat fluxes are analyzed, and the position of the heat dissipation surface and extreme conditions are confirmed. Then, a thermal control system is designed around the difficulties of thermal control. With heat pipes, the temperature uniformity of +Y deck, −Y deck, and +Z deck increased by 8 °C, 9.9 °C, and 34.2 °C, respectively. Furthermore, the maximum temperature of the power controller, secondary power supply, bidirectional frequency converter, and solid discharge decreased by 32.5 °C, 22.0 °C, 14.0 °C, and 164 °C, respectively. Finally, a thermal balance test is performed. The test results show that the temperatures of the solid-state power amplifier, on-board computer, power controller, secondary power supply, and bidirectional frequency converter meet the requirements of the thermal control indices. In addition, the temperature of thermal-sensitive components such as batteries and the storage tank also meets the requirements. The thermal design scheme is reasonable and feasible, and the thermal balance test verifies the correctness of the thermal design.

Development and research of an autonomous mobile platform with reduced computing power requirements

Today, the market is saturated with a variety of autonomous robotic devices that offer a variety of solutions to opti-mize workflows in various industries. One of the leading areas of their application is the automation of warehouse operations, where store robots move and optimize warehouse processes, reducing labor costs and speeding up task completion. These technologies reveal some limitations and shortcomings. For example, categories of robots such as transport carts, forklifts, and automated towing vehicles are limited in their functionality, often resulting in the need to use multiple types of devices for different tasks. This leads to equipment redundancy and complicates integration into existing warehouse systems. Another significant aspect is the need to improve the routing and nav-igation algorithms for these devices. Modern autonomous robot systems often face route optimization challenges, especially in dynamic and complex warehouse environments. This can lead to inefficiencies and increased time delays [1]. The goal of this work is to develop an autonomous mobile platform that would solve possible problems with navigation, have well-developed routing algorithms combined with low cost for affordable use in small busi-nesses. The research methods involve conducting various experiments on a real mobile platform to collect data and evaluate performance, including measuring the speed, accuracy, and power consumption of transportation tasks. Development and optimization of new algorithms for managing a mobile platform, planning routes, analyz-ing signals from sensors, processing images and video from a camera. The result of the work is that the possibility of compatibility of the on-board computer (single board computer) with the flight controller for movement on the ground surface has been investigated. On this basis, a mobile platform was created, which is presented in this arti-cle. This configuration is suitable for moving the platform either autonomously or using a control panel. The role of the technical vision system in providing navigation has been studied. The results of the work highlight innovation and the use of model functionality to create effective autonomous land navigation systems, including coordinates. The mobile platform can be used for transporting goods in warehouses, for working in extreme situations, for environmental monitoring or for educational purposes.

USV-Tracker: A novel USV tracking system for surface investigation with limited resources

This paper introduces USV-Tracker, a novel tracking system for Unmanned Surface Vehicles (USVs) tailored for practical applications such as surface investigation and target tracking. The system tackles three pivotal challenges: perception robustness, tracking concealment, and planning efficiency. The contributions of this work are manifold: (1) A multi-sensor fusion framework utilizing an Extended Kalman Filter (EKF) to enhance target detection and positioning accuracy, integrating data from cameras, LiDAR, GPS, and IMU sensors. (2) A two-stage path planning algorithm that generates occlusion avoidance trajectories and employs a virtual elastic force constraint to maintain appropriate relative positioning. In dense obstacle environments, the algorithm tends to get closer to the target and incorporates FOV orientation constraints to ensure stable perception. (3) A visibility-aware control strategy that ensures continuous target observability through EKF-based trajectory prediction. Simulations in Gazebo and corresponding physical experiments validate the system’s effectiveness and robustness, demonstrating its applicability in real-world scenarios. The computational workload is managed on a constrained on-board computer, underscoring the system’s practicality.

A Control System Design and Implementation for Autonomous Quadrotors with Real-Time Re-Planning Capability

Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning capability, including the hardware pipeline for the hardware–software integration to realize the proposed real-time re-planning algorithm. The framework is based on a modified version of the PX4 Autopilot and a Raspberry Pi 5 companion computer. The planning algorithm utilizes minimum-snap trajectory generation, taking advantage of the differential flatness property of quadrotors, to realize computationally light, real-time re-planning using an onboard computer. We first verify the control system and the planning algorithm through simulation experiments, followed by implementing and demonstrating the system on hardware using a quadcopter.

Detection of rice panicle density for unmanned harvesters via RP-YOLO

Rice panicle density is one of the essential bases for the automatic speed regulation of unmanned harvesters, making density detection crucial for intelligent upgrades. Currently, existing methods for detecting rice panicle density do not meet actual harvesting scenarios and struggle to meet real-time requirements. To address this, we developed a real-time rice panicle density detection method for unmanned harvesters. This method includes a panicle detection model based on YOLOv5n (RP-YOLO) and a rice panicle density calculation based on coordinate transformations. RP-YOLO was optimized through various techniques, such as enhancing the target detection head, reconfiguring the backbone network and downsampling module, introducing an attention mechanism, and refining the loss function. Based on coordinate conversion, we converted the world coordinates of the detection frame vertex to image coordinates and calculated the panicle density. We established the RP-1668 dataset for japonica rice and trained and tested the model. Compared to the original YOLOv5n model, our modifications reduced floating-point operations per second (FLOPs) by 33.33 %, decreased model size by 31.90 %, increased detection speed by 12.63 %, and improved accuracy (AP0.5) by 3.82 % (AP0.5:0.95, 6.96 %). RP-YOLO achieved superior accuracy and detection speed compared to both conventional lightweight and non-lightweight models. In field applications, the error in density detection was less than 10 % compared to manual counting, and the results clearly reflected changes in rice panicle density. For a 1.4 m × 1.0 m rice field imaging area (with a resolution of 2560 × 1280), the method detects at 15 fps on an on-board industrial computer, providing reliable data support for adjusting the operating speed of driverless harvesters.

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of the total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives on the development of onboard neuromorphic computers that mimic the operation of a biological brain using the nonlinear–dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs are a semi-classical technology, their technical simplicity and low cost compared to quantum computers make them ideally suited for applications in autonomous AI systems. Providing a perspective on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

Intelligent sequential multi-impulse collision avoidance method for non-cooperative spacecraft based on an improved search tree algorithm

The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years. Non-cooperative targets exhibit uncertain states and unpredictable behaviors, making collision avoidance significantly more challenging than that for space debris. Much existing research focuses on the continuous thrust model, whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions. Additionally, it is important to minimize the impact on the original mission while avoiding non-cooperative targets. On the other hand, the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer, posing challenges for practical engineering applications. To conquer these difficulties, this paper makes the following key contributions: (A) a turn-based (sequential decision-making) limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time; (B) a novel Selection Probability Learning Adaptive Search-depth Search Tree (SPL-ASST) algorithm is proposed for non-cooperative target avoidance, which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search (MCTS). Numerical simulations confirm the effectiveness and efficiency of the proposed method.

Real-Time Deep Learning Framework for Accurate Speed Estimation of Surrounding Vehicles in Autonomous Driving

Accurate speed estimation of surrounding vehicles is of paramount importance for autonomous driving to prevent potential hazards. This paper emphasizes the critical role of precise speed estimation and presents a novel real-time framework based on deep learning to achieve this from images captured by an onboard camera. The system detects and tracks vehicles using convolutional neural networks and analyzes their trajectories with a tracking algorithm. Vehicle speeds are then accurately estimated using a regression model based on random sample consensus. A synthetic dataset using the CARLA simulator has been generated to validate the presented methodology. The system can simultaneously estimate the speed of multiple vehicles and can be easily integrated into onboard computer systems, providing a cost-effective solution for real-time speed estimation. This technology holds significant potential for enhancing vehicle safety systems, driver assistance, and autonomous driving.

TROG: a fast and robust scene-matching algorithm for geo-referenced images

ABSTRACT In a GNSS-denied/challenged environment, the scene-matching navigation system (SMNS) is a vital autonomous technology for unmanned aerial vehicles (UAV). This paper proposes a novel template-matching framework for multimodal images since UAV navigation requires precision and real-time performance while utilizing onboard computers with low computational power. Specifically, first, the local descriptor is extracted to form a pixel-wise feature representation of an image. Then, the Fast Fourier Transform is applied to measure the similarity based on the feature representation. In the feature extraction part, a gradient-like pixel-level feature descriptor is designed, which is reconstructed by weighting it according to the gradient-like angles, named the three-dimensional reconstruction oriented gradient-like (TROG) descriptor. An optimized similarity measurement template is introduced in the matching part, which improves the traditional feature-based similarity measurement algorithm defined using Fast Fourier Transform (FFT) in the frequency domain. This strategy eliminates redundant computations during the matching process. To verify the effectiveness of the proposed algorithm, satellite imagery data from Google Earth are used as a reference images, and sensed images (including optical, IR, SAR, and Hyperspectral) are captured by UAV and satellites for image matching to testify to the registration accuracy, robustness, and computational efficiency. The experiment demonstrates that TROG is accurate, robust, and attains high real-time performance, making it applicable to UAV navigation and positioning. Additionally, field-flight experiments evaluate scene-matching navigation under satellite-denied conditions and low computational power conditions for UAVs, demonstrating that our scene-matching navigation system can achieve precise positioning with a positioning error of less than 1.637 m, which is comparable to satellite/inertial navigation systems. The experimental results from outdoor flight experiments highlight the value of our proposed algorithm in engineering applications under satellite denial conditions.

Providing spatial isolation for Mixed-Criticality Systems

Hard real-time systems, characterized by stringent timeliness requirements, occur in an increasing variety of industrial sectors. Some such domains carry important safety-critical concerns, notably avionics, space, and automotive. One common design trend across those domains seeks to reduce the number of computing devices embedded in them by integrating software applications of different criticality levels into one and the same onboard computer. A safety-savvy design approach however requires isolation among components of different criticality, to prevent unintended reciprocal interference across them. Isolation is traditionally achieved through partitioning. Partitioning, however, incurs low resource utilization as cautionary margins are used to inflate partition budgets over their anticipated needs. This situation has prompted research into alternative ways to integration that can safely afford higher levels of utilization. The Mixed-Criticality (MC) approach, which concentrates on the CPU scheduling problem, has yielded a large body of research results that show considerable gains in sustained utilization, but it has yet to meet all of the isolation requirements of safety-critical systems. This work presents a solution to augment a state-of-the-art MC solution with efficient and effective spatial isolation capabilities. Experimental results show that our solution provides adequate guarantees of temporal and spatial isolation with very small runtime overhead.

The specifics of the Galois field GF(257) and its use for digital signal processing

An algorithm of digital logarithm calculation for the Galois field GF(257)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GF(257)$$\\end{document} is proposed. It is shown that this field is coupled with one of the most important existing standards that uses a digital representation of the signal through 256 levels. It is shown that for this case it is advisable to use the specifics of quasi-Mersenne prime numbers, representable in the form p=2n+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${p=2}^{n}+1$$\\end{document}, which includes the number 257. For fields GF(2n+1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GF({2}^{n}+1)$$\\end{document}, an alternating encoding can be used, in which non-zero elements of the field are displayed through binary characters corresponding to the numbers + 1 and − 1. In such an encoding, multiplying a field element by 2 is reduced to a quasi-cyclic permutation of binary symbols (the permuted symbol changes sign). Proposed approach makes it possible to significantly simplify the design of computing devices for calculation of digital logarithm and multiplication of numbers modulo 257. A concrete scheme of a device for digital logarithm calculation in this field is presented. It is also shown that this circuit can be equipped with a universal adder modulo an arbitrary number, which makes it possible to implement any operations in the field under consideration. It is shown that proposed digital algorithm can also be used to reduce 256-valued logic operations to algebraic form. It is shown that the proposed approach is of significant interest for the development of UAV on-board computers operating as part of a group.

Comparative analysis of single-board computers for the development of a microarchitectural computing system for fire detection

Objectives. The purpose of the work is to select the basic computing microplatform of the onboard microarchitectural computing complex for the detection of anomalous situations in the territory of the Republic of Belarus from space on the basis of artificial intelligence methods.Methods. The method of comparative analysis is used to select a computing platform. A series of performance tests and comparative analysis (benchmarking) are performed on the selected equipment. The methods of comparative and benchmarking analysis are performed in accordance with the terms of reference to the current project.Results. A comparative analysis and performance testing of Raspberry Pi 4 Model B and Cool Pi 4 Model B single-board computers, as well as AI-accelerator Google Coral USB Accelerator with Google Edge TPU have been performed. The comparative analysis showed that Raspberry Pi 4 Model B and Cool Pi 4 Model B fully meet the terms of reference to the current project. At the same time Cool Pi 4 Model B handles neural network calculations well, but four times slower than similar calculations on Google Coral USB Accelerator. Neural network computations on the Raspberry Pi 4 Model B are 22 times slower than similar computations on the Google Coral USB Accelerator. Cool Pi 4 Model B outperforms Raspberry Pi 4 Model B by the factor of two to three for data copying and compression and almost six times faster for neural network computations.Conclusion. Despite the fact that Raspberry Pi 4 Model B meets the terms of reference to the project as a computational basis, when developing an on-board microarchitectural computing system for detecting anomalous situations, it is worth using more powerful alternatives with built-in AI-accelerators (e.g., Radxa Rock 5 Model A) or with an additional external AI-accelerator (e.g., a combination of Cool Pi 4 Model B and Google Coral USB Accelerator). Using a Raspberry Pi 4 Model B with an additional AI-accelerator is also acceptable and will speed up computations by several dozen times. AI-accelerators provide the fastest neural network computations, but there are features related to the novelty of the technology that will be explored in further development.

Багатокритеріальне оцінювання стандартизованих інтерфейсів бортових мереж наносупутників CubeSat

The technology of building CubeSat-class nanosatellites has created a breakthrough in exploring near space for research and education. The availability of commercial solutions for the rapid integration of hardware and software of a CubeSat under development and the launch of such a nano-satellite into Earth orbit as a parasitic payload has made this technology accessible and attractive to universities worldwide. Despite this popularity, the statistics of nanosatellite failures after launch require a systematic approach to their design and construction and the selection of off-the-shelf (COTS) components. The object of the study is to determine the overall efficiency of standardized network interfaces when used in the onboard network of CubeSat nanosatellites. The subject of the article is a method for comprehensively evaluating the effectiveness of standardized network interfaces for data exchange in the onboard network of CubeSat nanosatellites, taking into account typical scenarios for data and command exchange. The aim of the study was to substantiate the method of expert evaluation of the technical efficiency of standardized network interfaces for data exchange in the onboard network of CubeSat nanosatellites, taking into account the following factors: energy efficiency, multi-mastering capability, resistance to electromagnetic interference, redundancy, coverage following the OSI network model, data transmission speed, and complexity of network technology implementation by software. Objectives: To analyze modern methods of organizing onboard avionics networks of nanosatellites of the CubeSat class, formulate an expert comparison model, and compare the selected information onboard networks following the model and the list of selected networks and their protocols. Results: an expert model consisting of a 7-factor comparison was created, modern and most widely used in the industry onboard networks were selected, and each network was compared according to the proposed model. Conclusions: The two most common and efficient models, I2C and CAN, were selected as recommended for use in the avionics of the KhAI1Sat nanosatellite and the “Boryviter”/”Falco” onboard computer being developed by the authors. Although the difference in power consumption between these two types of networks is significant, each has certain advantages in terms of noise immunity, multicasting, and application.

Optimality principles in spacecraft neural guidance and control.

This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred on board, where controllers have the task of tracking the uploaded guidance profile. Here, we review recent trends based on the use of end-to-end networks, called guidance and control networks (G&CNets), which allow spacecraft to depart from such an architecture and to embrace the onboard computation of optimal actions. In this way, the sensor information is transformed in real time into optimal plans, thus increasing mission autonomy and robustness. We then analyze drone racing as an ideal gym environment to test these architectures on real robotic platforms and thus increase confidence in their use in future space exploration missions. Drone racing not only shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought but also entails different levels of uncertainties and unmodeled effects and a very different dynamical timescale.

Development of Communication System between TPMS and Server using Combination of OFDM and Convolutional Code Technique Based on SDR

The Tire Pressure Monitoring System (TPMS) has evolved into an essential element of contemporary vehicles, playing a pivotal role in enhancing road safety and the overall driving experience. Traditionally, TPMS systems rely on dedicated hardware components for the collection and transmission of tire pressure data to the vehicle's onboard computer and the data is visible only to the driver. In this research, we have developed a wireless communication system between TPMS and a server, enabling tire pressure data to be accessible not only to the driver but also remotely traceable by others. To build a reliable communication system, we utilized a combination of Orthogonal Frequency Division Multiplexing (OFDM) and Convolutional Code technologies. This system is implemented using Software-Defined Radio (SDR) technology. This communication method employs OFDM to enhance data throughput and integrates Convolutional Code to mitigate errors in received data. Consequently, this approach achieves a maximum throughput of 119.19MBps when utilizing the OFDM system alongside 16QAM modulation. The bit error rate for received data without coding stands at 5.77%, but the application of Convolutional Code with a 1/2 code rate effectively reduces this error rate to 3.85%. This system improves the reliability of TPMS communication with the server while also ensuring a consistently high throughput. It enhances road safety and remote monitoring capabilities.

SP-SAT: DEVELOPMENT OF AN EDUCATIONAL SATELLITE

n Bolivia, as in several other Latin American nations, there is a growing trend of burgeoning space projects and a rising interest among young students in the aerospace domain. Consequently, there arises an imperative to cultivate a new generation of space professionals. However, the exorbitant costs associated with the equipment utilized in these projects render them inaccessible to a vast majority of inter- ested students, including CubeSat kits, educational satellites, satellite kits, among others. Consequently, alternatives like educational sat- ellites have emerged to simulate the satellite design, construction, testing, launch, and operation processes on a scale conducive to student involvement. The primary challenge faced by students lies in integrating the essential subsystems of a satellite, encompassing power supply, sensors, and communication systems, within the confines of limited space. Considering this, the design and construction of an educational satellite with its respective signal reception equipment was developed. The equipment designed and manufactured is a low-cost one so that universities and educational centers could acquire it. This equipment educational kit is called SP-SAT (Space Program — educational satellite kit). This real satellite simulator offers a wide variety of educational activities. The kit’s equipment is distributed in two systems: 1) Space Segment, which consists of an Onboard Computer (OBC), Electrical Power System (EPS), Communications System (COMM), Experiment Plate (PYL), Complete PLA plastic structure (3D printed), rods, bolts, and nuts. 2) Ground segment: This segment includes all the equipment that is part of the ground station, which will receive data from the satellite.