Raspberry Pi Zero Research Articles

Home Surveillance and Alert System using Raspberry Pi Zero W and GSM Modem with MQTT Protocol

Today’s modern era has made it essential for everyone, even for the privacy of their homes, to have controllable, affordable, and easy-to-use security systems. The creation of a cost-effective, straightforward, and feature-rich home security alarm system using a Raspberry Pi Zero W microprocessor and MQTT protocol is discussed in this paper. The proposed system is primarily intended for outlying areas with limited or insufficient network bandwidths. Together with sensors such a PIR (passive infrared sensor), sound sensor, gas leakage sensor, obstacle/proximity sensor (vibration sensor), fire/flame sensor, GSM module, and a surveillance pi camera, it makes use of a Raspberry Pi Zero W as a microprocessor. [1]. In addition, the system will promptly alert the homeowner through phone calls, SMS, or mail anytime it senses the presence of an intruder or other safety hazards. Hence, anyone who wants to make their space (bank, home, workplace, jewelry shops, and cabins) safe and to protect themselves from theft and infiltration can use this system at a fair price.

A Low-Cost AI-Empowered Stethoscope and a Lightweight Model for Detecting Cardiac and Respiratory Diseases from Lung and Heart Auscultation Sounds

Cardiac and respiratory diseases are the primary causes of health problems. If we can automate anomalous heart and lung sound diagnosis, we can improve the early detection of disease and enable the screening of a wider population than possible with manual screening. We propose a lightweight yet powerful model for simultaneous lung and heart sound diagnosis, which is deployable in an embedded low-cost device and is valuable in remote areas or developing countries where Internet access may not be available. We trained and tested the proposed model with the ICBHI and the Yaseen datasets. The experimental results showed that our 11-class prediction model could achieve 99.94% accuracy, 99.84% precision, 99.89% specificity, 99.66% sensitivity, and 99.72% F1 score. We designed a digital stethoscope (around USD 5) and connected it to a low-cost, single-board-computer Raspberry Pi Zero 2W (around USD 20), on which our pretrained model can be smoothly run. This AI-empowered digital stethoscope is beneficial for anyone in the medical field, as it can automatically provide diagnostic results and produce digital audio records for further analysis.

Method to Achieve High Speed and High Recognition Rate of Goal from Long Distance for CanSat

A structure is proposed for high-speed goal recognition by SSD MobileNet using the Coral USB Accelerator. It was confirmed that the goal recognition rate from a long distance is equal to or better than that of conventional methods. Using this method, two CanSat contests were won. The aim of the CanSat competitions is to guide the CanSat to a distance of 0 m from the goal. It is initially guided by GPS but must eventually employ an image recognition model to identify the nearby goal. Zero-meter guidance to the goal was achieved in the Tanegashima Rocket Contest 2018 using a method that recognized the color of the goal by the color of the image, but it was vulnerable to changes in lighting conditions, such as weather changes. Therefore, a deep-learning method for CanSat goal recognition was applied for the first time at ARLISS 2019, and the zero-distance goal was achieved, winning the competition. However, it took more than 10 s for recognition owing to the CPU calculations, making it time consuming to reach the goal. The conventional method uses image classification to recognize the location of a goal by preparing multiple regions of interest (ROIs) in the image and repeating the recognition operations for each ROI. However, this method has a complex algorithm and requires the recognition of more ROIs to recognize goals over long distances, which is computationally time consuming. Object detection is an effective method to identify the location of the target object in an image. However, even if the lightest SSD MobileNet V1 and V2 and a hardware accelerator are used, the computation time may not be short enough because the computer is a Raspberry Pi Zero, the weakest class of Linux computers. In addition, if SSD MobileNet V1 and V2 do not have a sufficiently high recognition rate at long distances from the goal compared with conventional methods, it will be difficult to adapt them to a CanSat. To clarify this, SSD MobileNet V1 and V2 were applied to a Raspberry Pi Zero connected to a Coral USB Accelerator, and the recognition rate and recognition time were investigated at long distances from the goal. It was found that the recognition rate was equivalent to or better than that of the conventional method, even at long distances from the goal, and that the recognition time was sufficiently short (approximately 0.2 s). The effectiveness of the proposed method was evaluated at the Noshiro Space Event 2021 and Asagiri CanSat Drop Test (ACTS) 2021, and the 0-m goal was achieved at both events.

A Real-Time Analytic Face Thermal Recognition System Integrated with Email Notification

COVID-19 is a contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease has spread worldwide, leading to an ongoing pandemic. The most common symptom of COVID-19 is fever which can be detected using various manual screening techniques that have the risk of exposing the personnel. Since the virus has globally spread, a reliable system to detect COVID-19-infected people, especially before entering any premises and buildings, is in high demand. The most common symptom that can be detected is fever, even though people with fever might not have COVID-19. Thus, a real-time analytic face thermal recognition system integrated with email notification that has the capability to scan the person’s temperature and simultaneously analyze the measured temperature with the recorded/stored information/data is presented in this paper. The proposed system is also able to send an email notification to the relevant authorities during the real-time analytical process. Besides that, this information is also recorded in the system database for continuous monitoring of the respective person’s health status. The development of the proposed system is integrated with a Thermal Module AMG8833, Pi camera, and Raspberry Pi Zero Wireless. The proposed system has been tested and the captured results successfully accomplished the development objectives.

Vehicular Flow Characterization: An Internet of Video Things-Based Solution

Intelligent transportation systems (ITS) have emerged as the optimal solution to address urban mobility challenges. However, to effectively implement ITS, detailed traffic flow statistics are imperative. Various solutions have been proposed, including intrusive/non-intrusive sensors and compute vision-based solutions. However, these solutions have limitations in the number of measured traffic flow parameters, cost or performance under different traffic conditions. To overcome these limitations, we propose an Internet-of-Video-Things (IoVT) based solution. The sensor node (fabricated using Raspberry Pi Zero W, Pi camera, power bank, and Wi-Fi device) can live-streaming roadside traffic video to a remote Dell server located at our lab with Camlytics (commercially available traffic analysis software) installed. The proposed solution was field tested with a 45-minute live-streamed video of 720p at 25 frames per second. Results show that the proposed solution can measure more traffic flow parameters than intrusive and non-intrusive sensors, with an accuracy of 84.3% for vehicle count and speed estimation. Other parameters were also calculated, such as time/distance headway, spatial/temporal densities, heat maps, and trajectories. Additionally, the proposed solution can count pedestrians with an accuracy of 76.3%.

Portable Anterior Eye Segment Imaging System for Teleophthalmology.

This study aims to compare a new prototype for a portable anterior eye segment imaging system with the standard method for ophthalmology examination. The new imaging system consisted of two IMX219 Arducam autofocus sensors (Arducam, China, Nanjing) for Raspberry Pi V2 camera module connected to a Raspberry Pi Zero W (Raspberry Pi Foundation, UK, Cambridge) that clips to a wearable headset. The 2D videos of the anterior eye segment were recorded with the new system and a 720p FaceTime HD camera (Apple, Cupertino, CA). Afterward, ophthalmologists evaluated the videos using a standard clinical eye examination form. These evaluations were compared with the standard slit-lamp clinical assessment performed during the patient's visit. Thirty-five eyes were evaluated. The sensitivity and specificity percentages were statistically significant between the two imaging modalities (P ≤ 0.001). The evaluations performed from videos obtained with the new imaging system had better sensitivity and specificity percentages overall. However, statistically significant differences were only observed in cornea, anterior chamber, iris, and lens. Specificity percentages were higher than sensitivity percentages in both imaging modalities, indicating that video evaluations are less accurate for pathological screening. Nevertheless, the new system evaluations were significantly better than the webcam evaluations. This study presented an alternative system to assess eye conditions for telemedicine, one that provides more details than the current standard and uses new wearable headsets technologies.

Vaccination identification system using raspberry Pi

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causes Covid-19, an infectious illness. A methodology was created to track the vaccination history of people with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that causes Covid-19, an infectious illness. The system operates on a Raspberry Pi processor that is designed to authenticate the vaccination records of individuals. The Vaccination Identification System consists of various components connected to the Raspberry Pi Zero 2W microprocessor, Pi camera, an LCD display, LED indicators, a buzzer, a DC servo motor, and a PCB converter. The proposed system grants access to vaccinated individuals and denies access to those who are not vaccinated.

A Novel Plug-in Board for Remote Insect Monitoring

The conventional approach to monitoring insect swarming is based on traps that are periodically checked by human operators. However, human checking of trap contents is expensive, and in many environments, the pest species most frequently encountered in the traps can be detected and monitored automatically. To achieve this goal, a dedicated data acquisition device is necessary, which makes real-time and online pest monitoring possible from a distant location. In addition, it is beneficial for the device to run machine learning algorithms that count and identify insects automatically from pictures. Thanks to the advantages of integrated circuits, more systems have been designed to improve integrated pest management in the context of precision agriculture. However, in our opinion, all of those systems have one or more disadvantages, such as high cost, low power autonomy, low picture quality, a WIFI coverage requirement, intensive human control, and poor software support. Therefore, the aim of this work is to present a novel plug-in board for automatic pest detection and counting. The plug-in board is dedicated to Raspberry Pi devices, especially the Raspberry Pi Zero. The proposed board, in combination with a Raspberry Pi device and a Pi camera, overcomes the limitations of other prototypes found in the literature. In this paper, a detailed description can be found about the schematic and characteristics of the board with a deep-learning-based insect-counting method.

Framework for Building Low-Cost OBD-II Data-Logging Systems for Battery Electric Vehicles

With the electrification of transport (BEVs) and the growing benefits of smart vehicles, there is a need for a simple solution to perform real-time monitoring of the BEV and its battery for diagnostics and coordinated charging. The On-Board Diagnostics (OBD) system, originally designed for internal combustion engine cars (ICE), can be used to extract the necessary BEV data. This paper presents a developed framework for a low-cost solution to online monitoring of BEVs. A Raspberry Pi Zero W, along with other auxiliary components, was installed in two Hyundai Ioniq Battery Electric cars to communicate with the vehicles via the OBD-II port. A python script was developed to periodically request the vehicle data by sending various Parameter IDs to the vehicles and storing the raw response data. A web server was created to process the hexadecimal encoded data and visualize the data on a dashboard. The key parameters, such as the battery state of health (SOH), state of charge (SOC), battery temperature, cell voltages and cumulative energy consumption, were successfully captured and recorded, which can now facilitate trending for battery diagnostics and future integration with smart chargers for coordinated charging.

Bio-Inspired Model-Based Design and Control of Bipedal Robot

The article aims to describe stages of development of a mobile, anthropomorphic robot. The individual phases of the bio-inspired model-based design of a two-legged robot are described, from the analysis of human walking patterns, through the creation of a simulation model and a control system to the creation and testing the physical model of such a robot. The bio-inspired design is based on an analysis of the movements of the individual parts of a lower human body during walking locomotion. Based on this analysis, the chosen motion model is described as a combination of passive and dynamic walking with a controlled, linear inverted pendulum model. When creating the simulation model, an open kinematic chain consisting of a base (the frame) and two effectors (the legs) is used. The simulation of the virtual model is realized using the software tool Matlab with its toolboxes Simulink and Simscape, and the results confirm the correctness of the design. The feasibility of the design is confirmed by creation of a physical robot skeleton using 3D printing and by a commissioning of the control system based on Atmel ATmega2560 and Raspberry Pi Zero W microcontrollers.

Low-Cost Home Security Notification System Using IoT and Telegram Bot: A Design and Implementation

Home security is a critical issue, especially for the civilians to protect their property from harm. Currently, the increase in home breaking usually occurs during school holidays and public holiday seasons. Therefore, to overcome the problem and the main objective is to design and develop a low-cost home security notification system using PIR sensor to detect movement of intruders, Raspberry Pi Camera, GPS module integrated with Raspberry Pi Zero WH to send an alert notification message with image, date, location, and time via Telegram Bot to the house owner’s and people nearby through a smart phone. The system utilizes the System Development Life Cycle (SDLC) by implementing the waterfall model as the methodology. Three experiments were conducted to examine the effectiveness of the system which is sensor detection range, response time and user acceptance test (UAT). The finding indicates that the home security notification system was efficient, effective, low-cost, and easy to use. Besides, the system can detect the presence of the intruder and send the notification message in a reasonable time. Meanwhile, the result from the UAT indicates that the proposed system has a positive impact and to be well accepted by the majority of the users. Hence, the system can help the house owners to take immediate action such as calling the neighbourhood association or police department authority when the system detects an intruder in the house.

Development of a Cost-Effective IoT-Based Weather Monitoring System

This work explores the development and deployment of a cost-effective IoT platform to monitor and archival weather data, namely temperature, humidity, atmospheric pressure, and dust particles in a residential area, using open-source technologies. The IoT device sends the data to a remote virtual private server (VPS) over the Internet. A server application runs 24x7 to collect the data and logs it into a database. The necessary steps to set up a VPS server, secure it and install the IoT server application implementing the message queuing telemetry transport protocol are also described. The complete system is verified by real-time implementation using IoT devices, namely NodeMCU ESP 8266 and Raspberry Pi Zero W, along with suitable sensors. The work demonstrates the proof-of-concept feasibility of developing and deploying a scalable weather station from scratch using open-source technologies for faster time to market and the privacy and safety concerns with the end product.

A remote-controlled global navigation satellite system based rover for accurate video-assisted cadastral surveys

<span>One of the main tasks of a cadastral surveyor is to accurately determine property boundaries by measuring control points and calculating their coordinates. This paper proposes the development of a remotely-controlled tracking system to perform cadastral measurements. A Bluetooth-controlled rover was developed, including a Raspberry Pi Zero W module that acquires position data from a VBOX 3iSR global navigation satellite system (GNSS) receiver, equipped with a specific modem to download real-time kinematic (RTK) corrections from the internet. Besides, the Raspberry board measures the rover speed with a hall sensor mounted on a track, adjusting the acquisition rate to collect data at a fixed distance. Position and inertial data are shared with a cloud platform, enabling their remote monitoring and storing. Besides, the power supply section was designed to power the different components included in the acquisition section, ensuring 2 hours of energy autonomy. Finally, a mobile application was developed to drive the rover and real-time monitor the travelled path. The tests indicated a good agreement between rover measurements and those obtained by a Trimble R10 GNSS receiver (+0.25% mean error) and proved the superiority of the presented system over a traditional metric wheel.</span>

Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers

Structural Health Monitoring (SHM) is critical in the observation and analysis of our national infrastructure of bridges. Due to the ease of measuring bridge rotation, bridge SHM using rotation measurements is becoming more popular, as even a single DC accelerometer placed at each end of span can accurately capture bridge deformations. Event detection methods for SHM typically entail additional instrumentation, such as strain gauges or continuously recording video cameras, and thus the additional cost limits their utility in resource-constrained environments and for wider deployment. Herein, we present a more cost-effective event detection method which exploits the existing bridge rotation instrumentation (tri-axial MEMS accelerometers) to also act as a trigger for subsequent stages of the SHM system and thus obviates the need for additional vehicle detection equipment. We show how the generalised variance over a short sliding window can be used to robustly discriminate individual vehicle loading events, both in time and magnitude, from raw acceleration data. Numerical simulation results examine the operation of the event detector under varying operating conditions, including vehicle types and sensor locations. The method’s application is demonstrated for two case studies involving in-service bridges experiencing live free-flow traffic. An initial implementation on a Raspberry Pi Zero 2 shows that the proposed functionality can be realised in less than 400 ARM A32 instructions with a latency of 47 microseconds.

Interpretable Autonomous Flight Via Compact Visualizable Neural Circuit Policies

We learn interpretable end-to-end controllers based on Neural Circuit Policies (NCPs) to enable goal reaching and dynamic obstacle avoidance in flight domains. In addition to being able to learn high-quality control, NCP networks are designed with a small number of neurons. This property allows for the learned policies to be interpreted at the neuron level and interrogated, leading to more robust understanding of why the artificial agents make the decisions that they do. We also demonstrate transfer of the learned policy to physical flight hardware by deploying a small NCP (200 KB of memory) capable of real-time inference on a Raspberry Pi Zero controlling a DJI Tello drone. Designing interpretable artificial agents is crucial for building trustworthy AIs, both as fully autonomous systems and also for parallel autonomy, where humans and AIs work on collaboratively solving problems in the same environment.

Interoperability of Sensors in Buildings for Monitoring the Search for live Victims after Earthquakes

This paper describes Rescue in Collapsed Building (RICB) a telemetry system for recovering victims inside a collapsed building due to an earthquake. The use of Artificial Neural Networks (ANN) with Raspberry Pi and sensors are being used in several fields, for example: to recognize pedestrians on streets, to identify human activities such as running, walking and sitting, and to collect garbage collection. RICB’s field is human support and rescue. RICB is proposed as an Artificial Intelligence tool to improve the identification of human patterns according to the readings of sensors such as movement, sound and temperature. These measurements will be collected from a Raspberry Pi Zero W device inside a transparent acrylic sphere with a Human Radiating Sensor (HRS) that is our own elaboration in this paper. Thereafter, it will be analyzed by the ANN to determine if there is a body of data in the set of transmitted data to a server. That application lets an expert monitor and manipulate RICB to read the measurements of sensors. When RICB processes this data, the user can see the probability of the victim being alive to enable communication with him/her and local rescue teams.

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

The heart rate variability signal is a valuable tool for cardiovascular system diagnostics. Processing this signal detects arrhythmia during long-term cardiac monitoring. It is also analyzed to recognize abnormalities within the autonomic nervous system. Processing this signal helps in detecting various pathologies, such as atrial fibrillation (AF), supraventricular tachycardia (SVT), and congestive heart failure (CHF). As a beneficial alternative to the commonly used HRV spectrum analysis, quadratic time–frequency analysis of HRV signals could be helpful in heart pathology detection. Indeed, in this study, we have created a client-server paradigm deployed as a telemedical platform for real-time remote monitoring of the cardiovascular function in patients suffering from arrhythmia. This platform detects arrhythmia in real-time by deploying time–frequency analysis, feature extraction, feature selection, and classification of Heart Rate Variability (HRV) signals. We gathered all these functionalities in a Graphical User Interface (GUI) in addition to data acquisition. As a client, a Raspberry Pi Zero ensures data acquisition and connects to a server over TCP/IP that involves a 4G/3G connection encrypted through the transport layer security (TLS). This telemedical tool continuously monitors the heart rate variability. In the case of an alarm, medical professionals may immediately interact with their patients in the hospital or at home.

Low-cost on-line monitoring system for agriculture based on raspberry pi zero

Based on data from the National Defense Agency, the trend of conversion of agricultural land in the 1990s reached around 30,000 ha/year. However, in 2011 there was an increase of 110,000 ha/year and reached 150,000 ha/year in 2019. In fact, with the growing human population, the need for food is increasing from year to year. On the other hand, the slow regeneration of farmers and the lack of dissemination & technological innovation to increase agricultural productivity are challenges in realizing food security. In line with the ongoing industrial revolution in the industrial era 4.0, it has brought very significant changes, not only the type of technology that has shifted, but more importantly a change in mindset in this industrial era. This has a real impact on the direction of national development and has an impact on the agricultural system. The agricultural system is no longer just a cultivation activity, but agriculture is also part of the industrial system which is characterized by a transformation, both in terms of raw materials, economy, social and environment. The development of modern agriculture is marked by mindset changing of using biological and non-biological resources by the needs. The precise use of resources in agricultural production systems is then called precision agriculture. Precision agriculture is an agricultural concept with a systems approach towards low-input, high-efficiency, and sustainable agriculture. In a previous study, a CNC-based precision farming system (Farming-BOT) was developed. The device could perform vegetable crop cultivation automatically. To properly observe the development of cultivation, an online monitoring device based on the minicomputer was developed. The Raspberry Pi Zero, equipped with a 5MP camera and a temperature & humidity sensor, is used. The device is placed on the top of the Farming-BOT device. Image data, temperature, and humidity are sent to the cloud database periodically every day. Users can access the data through the website and Android application. Based on the testing result, with a stable internet connection, data on environmental conditions can be monitored perfectly.

Performance Evaluation of Different Raspberry Pi Models for a Broad Spectrum of Interests

Now-a-days, Single Board Computers (SBCs), especially Raspberry Pi (RPi) devices, are extensively used due to their low cost, efficient use of energy, and successful implementation in a wide range of applications; therefore, evaluating their performance is critical to better understand the applicability of RPis to solve problems in different areas of knowledge. This paper describes a comparative and experimental study regarding the performance of five different models of the RPi family (RPi Zero W, RPi Zero 2 W, RPi 3B, RPi 3B+, and RPi 4B) in several scenarios and with different configurations. To conduct our multiple experiments on RPis, we used a self-developed and other existing open-source benchmarking tools allowing us to perform tests that mimic real-world needs, assessing important factors including CPU frequency and temperature during stressful activities, processor performance when executing CPU-intensive processes such as audio and file compressions as well as cryptographic operations, memory and microSD storage performance when executing read and write operations, TCP throughput in different WiFi bands, and TCP latency to send a specific payload from a source to a destination. Our experimental results showed that the RPi 4B significantly outperformed the other SBCs tested. In addition, our research indicated that the RPi Zero 2 W overclocked, RPi 3B, and RPi 3B+ had similar performance. Finally, the RPi Zero 2 W showed a much higher capacity than its predecessor, the RPi Zero W, and seems to be a perfect replacement when upgrading, since they have the same form factor and are physically interchangeable. With this study, we aim to guide researchers and hobbyists in selecting adequate RPis for their projects.

Remote video monitoring for offshore sea farms: Reliability and availability evaluation and image quality assessment via laboratory tests

<div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p class="Abstract">In this article, the availability and reliability of a remote video monitoring system for offshore sea farming plants are studied and tested in laboratory. The scope of the system is to ensure a video surveillance infrastructure so to supervise breeding cages along with the fish inside them, in order to contrast undesired phenomena like fish poaching as well as cages damages. The system is installed on a cage floating structure: it is mainly composed of an IP camera that is controlled by a Raspberry Pi Zero which is the core of the system. Images are streamed thanks to a 3G/4G dongle, while the overall system is powered via two photovoltaic panels charging a backup battery. Simulations are carried out considering two seasonal functioning periods (i.e., winter and summer): each of them is characterised by temperature trends defined according to the average temperatures of the system deployment site, 8 km offshore the city of Piombino, Italy. In order to optimise power consumption without hindering application scenario requirements, the system operates according to a duty cycle of 2 minutes out of 15 (i.e., 8 minutes of operation per hour). The performances of the system are then tested in laboratory exploiting a climatic chamber so to simulate different environmental conditions: variations on image quality are then analysed in order to identify possible dependencies on critical situations related to specific temperature and relative humidity values and to the presence of salt in the air.</p></div></div></div></div>