Mask and Temperature Detection and Automatic Sanitizer dispenser Using Raspberry Pi

In past few years, automatic remote systems for monitoring a wide range of environment or process parameters have exposed a rapid growth concerning the technology issues. Entering the era of Internet of Things (IoTs), a highly dynamic and radically distributed networked system, usage of small, cheap, flexible and end-user programmable computer hardware becomes apparent. These objects, deployed in various environments, are able to communicate and to interact among themselves, with end-users or other entities in the network. Using such objects, tasks like monitoring, control or data processing can be easily done. But, in some case, the target environments are often too dangerous, hazardous, unwired, or difficult to access. In such unfriendly environments there are many challenges for monitoring systems (wireless sensor networks) deployment and maintenance. As the possible solution, in this paper we have suggested the creation of an economical remote monitoring system, based on cheap computer board (Raspberry Pi), wireless sensors and GSM/GPRS (Global System for Mobile Communications/General packet radio service). In other words, the Raspberry Pi platform is utilized as central processing unit which provides a set of services for accessing sensor data, and communicates with end users, while different types of sensors (depending of target parameters) can constitute the detection module. 3G/4G USB Modem or GSM/GPRS shields can be used as GSM/GPRS communication module in order to ensure Internet connection. A whole system presents an IoT solution, precisely a Sensor Web node, which can be accessed all over the world, and provide information from a monitored environment. Performed research shows a possibility of setting up this system for a remote communication, control function and monitoring in unfriendly environments. The usage of low cost pieces of hardware verifies that the Raspberry Pi is highly suitable for numerous IoT concept applications and especially in the unfriendly environments.

Our review paper highlights the design and creation of voice assistance and facial recognition smart glasses using raspberry pi for the benefit of those who are blind or visually impaired. It helps them recognize everyone around them even when their known person is silent. Also, it includes some excellent features like web browsing and songs playing. This idea makes their life simpler and works as a digital assistant. This device has the feature of recognize the face of that people or that object which is already inserted in our system. It takes our face as an input and it provide the output by speaking. We can implement our software by using Raspberry PI, it is also a extremely powerful ARM based system on a chip (SOC). The Raspberry Pi can execute Python and C++ programming and do graphical calculations using its built-in graphics. In this project we have used Raspberry pi 4 face recognition, speaker as a buzzer, google text to speech, Ultrasonic sensor for object detection and ESP 32 camera web server. Our suggested system is superior, extremely effective, extremely affordable, user-friendly, and light weight.

This paper describes in-service post-primary school teacher reactions towards the provision of a Raspberry Pi Continuing Professional Development (CPD) Programme designed to build capacity in python programming, circuitry and hardware configuration. The Bridge21 social constructivist model of teaching and learning was used for workshop delivery, and teachers were encouraged to use the same model to enhance Raspberry Pi delivery on return to the classroom. This paper presents results from the analysis of four (N = 4) one day Bridge21 Raspberry Pi CPD workshops delivered in the authors' home institution over the 2013/2014 academic year. This paper examines teacher reactions towards the use of the Bridge21 model for learning about using the Raspberry Pi. The authors' administered a mixed methods evaluation instrument to a self-selecting sample of N = 61 teachers to capture reactions towards use of the Bridge21 model for Raspberry Pi CPD delivery. Qualitative coding of text responses combined with statistical analysis of Likert scales gathered from the evaluation instrument indicate that (a) teacher reactions towards the use of the Bridge21 model for learning Raspberry Pi technologies was positive and (b) teachers intended to use the same model to enhance their own Raspberry Pi delivery in the school classroom.

Nowadays, many people adopt a pet; not for taking care of the house but as a companion. However, many pet owners may not be able to spend time taking care of their pets, especially when they have a business trip. A stay-at-home pet has no, or less, survival skill to look for food outside and could not survive on its own, unlike stray animals. Therefore, usually, pet owners would ask their friends to take care for them; or look for a real-time food dispensing system that can feed the pet at scheduled times with monitoring functions. This paper proposes a monitoring system which comes with an automatic food dispenser, and which comes with several other useful functions to assist the pet owners. The proposed system uses the Raspberry Pi which controls and interconnects with several subsystems such as a (1) real-time monitoring subsystem, (2) door managing subsystem with software support help to ease the burden, and (3) food dispensing system. The wireless food dispensing system allows the owner to feed their pet automatically, according to a schedule or manually, according to the owner’s preference. The real-time monitoring subsystem allows the owner to monitor their pet and check if other stray pets tried to enter the cage through a camera. Finally, the door managing subsystem allows the owner to control the door lock/unlock, thus giving their pet some freedom time. This system proves to be useful to be implemented to reduce the chance of the pets being abandoned.


 
 
 
 
 
 
 
 
 
 
 Nowadays, utilizing surviellence cameras was very popular because is user friendly and it has many usefull as a monitoring and as a part of security system at home, institution, office, etc. The existing surviellence camera using computer as a control system and as a media storage so it’s need a energy and high cost for implementation. Surveillance cameras are designed using the Raspberry Pi B, PIR sensor, webcam Logitech C 170, SD Card, Twitter, Raspbian operating system, and python programming. Automatic input data is performed by PIR sensor that will be detected human around of range area frequently. Output 3.3 - 5 volt (High) will be generated when sensor detected the human around of range area. Otherwise 0 volts output ( Low) will be generated when sensor not detected the human around of range area frequently. Then, The input will be processing by Raspberry Pi using python programming that has made to a command to capture or not.implemetation outcome is a surveillance camera that will capture image in (.jpg) format when the input system detected human around of range frequently. Capture result will save to SD Card that have integrated system. Then the sistem will provide immediately report the situation in a real-time notification through a twitter application device.

In the modern age of digital security, conventional door lock systems are creasingly susceptible to breaches. This paper proposes a robust, multi-factor door lock system that integrates fingerprint recognition, facial detection, and a PIN-based unlocking mechanism using a Raspberry Pi microcontroller. It enhances security by requiring three authentication methods before granting access. The system incorporates a fingerprint sensor via UART, a USB-connected webcam for facial recognition, and a keypad for PIN entry. A servo motor acts as the locking mechanism, controlled through GPIO pins of the Raspberry Pi. By combining biometric, visual, and numeric credentials, this system mitigates the weaknesses of single-layer authentication methods. Python scripts and OpenCV libraries are employed for facial recognition, while fingerprint templates are stored in a secure database. The system is energy-efficient, cost-effective, and can be integrated into existing home automation setups. The solution addresses key concerns office security, offering both reliability and scalability. This project thus presents a feasible alternative to expensive commercial smart lock systems, offering a customizable and user-friendly experience. Keywords: Raspberry Pi, Fingerprint Sensor, Webcam, Keyboard

The purpose of this study to build a home security system based on object motion detection using Robot Operating System 2 (ROS2) and Raspberry Pi. ROS2 function in this study as a software that used to read and process the camera data then control the buzzer sound, while the Raspberry Pi hardware function to running the ROS2 on the top of Linux Ubuntu 18.04 operating system. Camera function to read the image and video data can be developing for input device to control the home security systems based on the object motion detection. The method has used to detect the object motion is Binary-Image Comparison (BIC), where this method worked by compare the value of the binary image object with the binary master to use it as a decision-making algorithm when the camera detecting the object movement based on object color. Object colors detected in this study are red, yellow, green, and blue. Each object colors are processes using OpenCV library in the ROS2 node service, after that all of the nodes will communicate through topics to communicating and exchanging the message data. This study result is the prototype has developed can give a buzzer sound warning to the user when the camera detecting the object motion based on the object's color.

Many highway toll collection systems have already been developed and are widely used in India. Some of these include Manual toll collection, RF tags, Barcodes, Number plate recognition. All these systems have disadvantages that lead to some errors in the corresponding system. This paper presents a brief review of toll collection systems present in India, their advantages and disadvantages and also aims to design and develop a new efficient toll collection system which will be a good low cost alternative among all other systems. The system is based on Computer Vision vehicle detection using OpenCV library in Embedded Linux platform. The system is designed using Embedded Linux development kit (Raspberry pi). In this system, a camera captures images of vehicles passing through toll booth thus a vehicle is detected through camera. Depending on the area occupied by the vehicle, classification of vehicles as light and heavy is done. Further this information is passed to the Raspberry pi which is having web server set up on it. When raspberry pi comes to know the vehicle, then it access the web server information and according to the type of the vehicle, appropriate toll is charged. This system can also made to count moving vehicles from pre-recorded videos or stored videos by using the same algorithm and procedure that we follow in this paper.

Object tracking is the main task in the field of computer vision. This chapter proposes and compares a new method for recognition and tracking by using CamShift and color detection. All the presented algorithms were programmed with Python programming language supported by OpenCV libraries, and executed with a credit card–size computer board called Raspberry Pi with attached external camera. A camera mimics and uses real-time digital videos for object recognition and tracking. The chapter also proposes a color detection algorithm in very low light conditions is proposed for the enhancement of target tracking. Mean shift is a nonparametric feature-space analysis technique for locating the maxima of a density function. The CamShift algorithm is dependent on an adaptation of mean shift algorithm. Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram.

In this study, we use explainable artificial intelligence (XAI) based on class activation map (CAM) techniques. Specifically, we use Grad-CAM, Grad-CAM++, and ScoreCAM to analyze outdoor physical agricultural (agri-) worker image datasets. In previous studies, we developed body-sensing systems to analyze human dynamics with the aim of enhancing agri-techniques, training methodologies, and worker development. These include distant, visual data-based sensing systems that capture image and movie datasets related to agri-worker motion and posture. For this study, we first obtained the aforementioned image datasets for researcher review. Then, we developed and executed Python programs with Open-Source Computer Vision (OpenCV) libraries and PyTorch to run XAI-oriented systems based on CAM techniques and obtained heat map-pictures of the visual explanations. Besides, we implement optical flow-based image analyses using our Visual C++ programs with OpenCV libraries, automatically set and chase the characteristic points related to the video datasets. Next, we analyze the dataset features and compare experienced and inexperienced subject groups. We investigate the output’s features, accuracies, and robustness to be able to make recommendations for real agri-workers, managers, product-developers, and researchers. Our findings indicate that the visualized output datasets are especially useful and may support further development of applied methods for these groups.

Emotion detection from images is one of the most high-powered tasks in today’s world and AI detection plays a fundamental role, this project aims to understand human emotions which play a major role in well-being, this project aims to dive deep into human emotions and provide a variety of solutions, through Human-computer interaction takes place, emotion detection and voice signals are input signal The project is classified into two major components: one where a human emo- tion is detected through camera is detected and facial recognition takes place. Secondly, where a solution is provided to a recorded emotion using a voice model, it creates a conversation which result turn provides a solution based on the collected database. The paper presents a theory-based framework of technologies discusses challenges and behavioural considerations, and gives directions for future practices and use. Index Terms—Voice GPT, Artificial intelligence (AI), Facial emotion recognition (FER), Emotion Detection (ED), Open CV Library (OCL) Convolutional neural networks (CNN), Machine Learning (ML), Tensor flow, GTTP, Keras

This research paper presents research conducted to control the sound level of the system using hand gestures. The proposed system is designed using Python programming language and OpenCV library. The development of programs that can be controlled using natural gestures is an important area of research in the field of human-computer interaction. The system captures the video feed from the camera and analyzes it to detect the hand gestures. The captured frames are preprocessed to remove noise and simplify processing. The image is then converted to grayscale, thresholded to separate the hand from the background, and contours are found in the thresholded image. The detected gestures are then mapped to control the sound level of the system. The proposed system is implemented and tested on a laptop computer with satisfactory results. The program is tested on a range of operating systems and environments to ensure compatibility and functionality. To optimize for speed and efficiency, the program can be parallelized and/or implemented using GPU acceleration.

<span>This study aimed to explore models to identify a human by using face recognition techniques. Data were collected from Cohn-Kanade dataset composed of 398 photos having face emotion labeled with eight emotions (i.e., neutral, angry, disgusted, fearful, happy, sad, and surprised). Multi-layer perceptron (MLP), support vector machine (SVM), and random forest were used in model accuracy comparisons. Model validation and evaluation were performed using Python programming. The results on F1 scores for each class in the dataset revealed that predictive classifiers do not perform well for some classes. The support vector machine (RBF kernel) and random forest showed the highest accuracies in both datasets. The results could be used to extract and identify emotional expressions from the Cohn-Kanade dataset. Furthermore, the approach could be applied in other contexts to enhance monitoring activities or facial assessments. </span>

An advanced vehicle monitoring and tracking system is designed for monitoring the school vehicle from any location A to location B at real time and provide safety environment to the traveler. The proposed system would make good use of new technology that based on Embedded Linux board namely Raspberry Pi and its advanced feature of storing database at real time. The proposed system works on Global Positioning System (GPS) and Global System for Mobile Communication (GSM) which is used for vehicle tracking and monitoring mechanism. For this purpose SIM908 Module is used which includes all the three things namely GPS GPRS GSM. The GPS gives current location of the vehicle; GPRS sends the tracking information to the server and the GSM is used for sending alert message to vehicle's owner mobile. The proposed system would place inside the vehicle whose position is to be determined on the web page and monitored at real time. In the proposed system, there is comparison between the current vehicle path and already specified path inside the file system of Raspberry pi. Hence if the driver drives the vehicle on the wrong path then the alert message will be sent from the proposed system to the vehicle's owner mobile and if the vehicle's speed goes beyond the specified value of the speed, then also the warning message will be sent from system to the owner mobile. The proposed system also took care of the traveler's safety by using LPG Gas leakage sensor MQ6 and temperature sensor DS18B20.

Nowadays, people have a very busy and hectic life; thus, taking care of an infant constantly is tough. Especially for working parents, a modernized IoT-enabled baby management system will be beneficial. In these modern times, most children are taken care of by their grandparents, housemaids, or babysitters during the daytimes. In this work, we are using a very efficient and user-friendly technology to implement automatic swinging of the baby bassinet with sound detection of the baby crying using sound sensor and playing lullaby through speakers. The humidity sensor has been used to know the diaper's moisture level, and notifications have been sent to parents with certain conditions through mobile calls and text messages. A webpage using HTML and CSS has been developed, where parents/guardians can supervise the baby in real-time. Finally, the system will detect if the baby is in the cradle using the face recognition technique. This exciting feature is implemented by using a Raspberry Pi 4 (Model B), which is equipped with a Pi camera that will also offer the parents a live-stream option. The proposed baby monitoring system is believed to be an improved technology for new and working parents and colossal help and riddance of unrequired tensions.