Camera Module Research Articles

Image quality performance of CMOS image sensor equipped with Nano Prism

Smartphones with 100 million pixel sensor are on the market. After that, it is expected to mount a higher resolution mobile camera module of 200 million pixels or more. In order to develop high resolution sensor products by mounting more pixels in a limited space, it is necessary to reduce the size of the pixels. There are currently sensors on the market with 0.64um pixels. It is expected that sensors with smaller pixels will be developed in the future. In terms of image quality, the smaller the pixel size, the smaller the amount of light received. Therefore, the image quality deteriorates in terms of noise and crosstalk. To overcome this limitation, various high sensitivity sensors are being developed, and it is advantageous to mount Nano Prism in the development of high sensitivity sensor. In this paper, we introduce the image quality performance of CMOS image sensor equipped with Nano Prism.

Axial and radial crushing behaviour of thin-walled carbon fiber-reinforced polymer tubes fabricated by the real-time winding angle measurement system

This paper proposed a real-time winding angle measurement device embedded in the portable 3-axis winding machine, which was used to detect and measure the winding angle during the winding process. The device was completely designed, fabricated, tested, and divided into hardware and software sections. The hardware section was adopted Raspberry Pi 3B+ and Arducam 5MP OV5647 camera modules, and the software section was used the OpenCV software. The experimental test was carried out to validate the measurement data on ±45°, ±60°, and ±75° winding angles, and filament-wound carbon fiber-reinforced polymer tubes were performed the quasi-static radial and axial compression tests. It was found that the real-time measurement data from the system had approximately 0.45±0.02 comparative error value than the targeted winding angle, which was more accurate than traditional measurement data. Moreover, it was shown that peak crushing loading significantly increased with increasing winding angle under radial compression, which had the inverse decreasing trend under axial loading. In addition, the crushing behaviour of thin-walled CFRP tubes was studied.

PACKAGE RECEIVER BOX BASED ON IOT USING FUZZY MAMDANI AND MOBILE APPLICATION

The development of information technology is currently growing, where now everything has been transformed digitally. Consumers today to shop no longer need to come to the market. Consumers shop through e-commerce applications and just wait for the goods between the couriers from the expedition to arrive at their doorstep. Based on a survey conducted on March 10, 2021, with 100 respondents, it was concluded that 62% of the problems experienced were the process of receiving packages and security risks during package delivery, where the courier did not find the owner of the package at home. Answering the IUI problem requires an IoT-based package receiving box that helps make it easier for couriers to send goods and helps package owners to monitor packages that have been delivered by couriers. This package recipient box is accompanied by an ESP32 Cam camera module to scan the QR code receipt number, ESP8266 helps connectivity and android-based mobile applications with a firebase real-time database to make it easier for package recipients to monitor packages. In helping to facilitate decision making, Mamdani fuzzy logic is used. From the research experiment, it was found that the system can carry out the package storage process with an average time of 11 seconds and the time required to update to firebase is around an average of ±3 seconds.

River Flood Early Warning System Based on Internet of Things in Binjai City

Flood is an event of inundation of land, which is usually dry, by water originating from water sources around the land. Binjai City is an area prone to flash floods. This is because in Binjai City there are 5 (five) upstream rivers namely the Bingei river, Mencirim river, Bangkatan river, Diski river and Rambai river. A flood early warning system is a series of systems that function to notify an impending flood disaster. With the existence of a flood early warning system, it can provide information to the community and can reduce victims or losses due to the community's unpreparedness in dealing with flood disasters. This study aims to create a prototype of a river flood early warning system based on the internet of things (IoT). The method used is to design and create a flood early warning system prototype, then perform system testing. The system is designed using the Arduino Mega2560 microcontroller as the system control center, the HC-SR04 ultrasonic sensors and the ESP32-Cam camera module as system input, as well as buzzer, LCD and website as system output. The transmission medium used is wireless via a 4G WiFi Modem connected to the internet. System prototype testing will be carried out in the Bangkatan river area in Binjai City. From the results of the discussion, it was found that the river flood warning system using the HC-SR04 ultrasonic sensor and the ESP32-Cam camera module based on the Internet of Things was successfully designed and implemented in prototype form and worked well. Ultrasonic sensors work well in measuring river water level with an average error percentage of 3.642%. The ESP32-Cam camera module works well in capturing images of river water conditions up to a distance of 200 cm. (9 pt).

Design and Implementation of a Smart Surveillance Security System

Home security is essential for occupant’s conveniences and protection. This research project designed and implemented a comparatively inexpensive smart surveillance security system that automatically captures an intruder’s image through a raspberry pi camera module and PIR sensor and sends mail to the user via Wi-Fi using the users registered email address. This system operates by triggering the Pi Camera through Raspberry Pi whenever an intruder comes in range using PIR sensor. The Pi camera will capture the image, save it and send the image of the intruder via mail to the user through the help of the command codes embedded in the Raspberry pi microcontroller. This research project will enable home/supermarket and office owners to secure their facilities and monitor the activities of their employers at any location at cheaper cost which is the earnest desire of an Engineer. Proteus 2022 was used as the simulation tool. Keywords: Security, Spy camera, Raspberry pi, IOT, smart security system.

Perception of multimodal objects in NLP through computer vision

This project is based on voice interaction and object detecting properties. It will allow the users to do voice interaction with the artificial intelligence and it will reply with the system voice. That is how users will use their voice to command as a trigger to find out the category of any object by showing it using the camera module. At first, the user will show an object with the help of a camera and ask for identifying it in the system. The object detection system then captures a frame from the camera and predicts through the structure to identify which class the object belongs to by extracting the feature from there. The process of this application is to search the database to match the structural data to find out the exact category of the object. When this system approximately matches with the information of a category then the application will suggest the category for the object by mentioning the category name through voice. This application can also give some basic information by asking for it. Our general-purpose approach can be effective in interpreting the structure and properties of objects in different networks through natural language processing.

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.

Smart Assistive System for the Visually Impaired

Recent advances in innovation have made ordinary people's lives richer, simpler, and easier. Many people suffer from vision impairment, which causes many difficulties in their daily work, according to World Health Organization (WHO) statistics. Our goal is to develop a framework that is unobtrusive, safe, portable, and versatile in order to assist blind people in their daily lives. It intends to develop an effective system for assisting the visually impaired with obstacle detection and scene classification. The Raspberry-Pi 3B+ and a camera module are used in the proposed method. It photographs the scene and then preprocesses the images with Viola-Jones and Yolov5 object detection algorithms. The aforementioned techniques are used to detect people and objects. We also used a sound system that speaks when an object or person is detected. The presented study implements simple computations and detects obstacles with high efficiency. Unlike other frameworks, this framework is lightweight and easy to transport

Voice Control Robot with Object Detection and Picking

Automation and robotics continue to revolutionize industries, enhancing efficiency and productivity across various domains. In pursuit of this automation, the development of voice-controlled robots with object detection and picking capabilities represents a promising frontier. This project explores the convergence of hardware and software technologies to create a versatile robot that responds to voice commands, perceives its environment through object detection, and executes tasks such as object picking and placement. The hardware foundation of the project centres on the ESP32-S3 microcontroller, integrating sensors, motors, and a camera module. The software stack encompasses speech recognition for natural language voice commands and object detection powered by deep learning models. The project aims to design an intuitive user interface for remote control, providing users with the ability to command the robot seamlessly. Through a comprehensive literature review, we delve into the evolution of speech recognition, object detection, and human-robot interaction, shedding light on the theoretical and practical aspects of the project. Real-world applications across industries underscore the project's potential, from warehouse automation to healthcare assistance. However, challenges such as real-time processing, accurate object recognition, and human-robot interaction complexity are acknowledged. The project's future directions emphasize the need for ongoing research to refine the technology's capabilities and overcome existing limitations. This voice-controlled robot with object detection and picking project presents a compelling fusion of cutting-edge technology and practical utility, contributing to the ever-expanding field of robotics automation.

Smart system for early detection of agricultural plant diseases in the vegetation period

This paper presents a smart system for the early detection of agricultural plant diseases in the vegetation period. The proposed smart system allows the detection of three types of wheat diseases, particularly yellow rust, powdery mildew, and Septoria at an early stage, and significantly improves the soil and ecology by locally spraying harmful chemicals just on sick plants. The proposed disease-detecting method is based on the structure of a convolutional neural network (CNN) using the Pycharm program based on the C ++ programming language. The basic structure of the smart system consists of Raspberry PI 4 MODULE, Raspberry Pi camera module v2, buzzer, HC-SR04 distance sensor, rotor driver, AC motor, power supply, relay microelectronics, and some digital devices.

Deep Cleaner—A Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning

Images are increasingly used for AI-based diagnosis and analysis of many diseases like cervical cancer, mouth cancer, glucose analysis from retina etc. In many cases, data collection is done by specialised camera modules which capture images of affected areas. As with any other sources of data, this process is also error-prone and may contain unwanted objects and regions that may require cleaning by removing them. Outliers in these kinds of dataset may adversely affect the performance of machine learning models. Manually cleaning would be a tedious task, especially when the data is collated from different sources. Hence, cleaning the data before training the model is of utmost importance. In this paper, we propose a Few-Shot learning based model pre-trained in supervised contrastive learning settings to automate the process of data cleaning. Our model learns the dataset distribution and distinguishes the accurate data points from noisy data points. We also show that scaling up the model can greatly improve the Few-Shot performance. On the noisy MobileODT cervical data, which was collected from Kaggle, our model obtained 52% accuracy without cleaning data using an EfficientNet architecture for the classification task. Whereas the same architecture with ROI cropping achieved an accuracy of 76.56% after cleaning through the proposed Deep Cleaner approach that requires only 100 clean images. The proposed approach performs 2.74% better than a denoising auto-encoder, which is considered a powerful anomaly detection technique.

A Novel Architecture for the Smart Pedestrian Crossing in Cities Using IoT‐Based Approach

Pedestrian crossings have also been highlighted as one of the most dangerous locations in the transportation field. Because people and vehicles share the road, a crosswalk improves the road’s efficiency in a densely populated region. However, as the population grows, more accidents and serious injuries occur, and as a result, nationalities are attempting to reduce these incidents through marketing and legal fines. Various architectures and developmental models have been proposed by authors focusing on the safeguarding of pedestrians crossing the intersections and vehicles passing by. Few proposed machine learning and deep learning‐based solutions to the pedestrian lanes; others provided an Internet of Things‐ (IoT‐) based solution to the situation. Various challenges are left unresolved, such as evidence recording, image capturing, and recognition in case of an emergency. In the proposed scenario, an IoT‐based technology is utilized to assist the vehicles passing by to act over the signals depicted as a red light focusing on a real‐time architecture. The proposed system will be mounted along the roadside at the traffic light pole. The system comprises various refined quality components, such as a gesture control module, High Definition camera module, etc. Based on the decision drawn from the gesture module, a specific signal will be displayed with the help of a traffic light to assist the vehicles passing by to safeguard the people crossing the proposed smart pedestrian crossing.

A Method for Eliminating Pig Face Recognition Errors Caused by Too Short Pig Growth Cycle

In the process of modern large-scale pig breeding, it is necessary to distinguish the identity of each pig and real-time detect its health status, weight change, dietary status, and other parameters. Traditional methods waste a lot of resources, while the high quality of pork cannot be effectively guaranteed. This project is based on convolutional neural networks to design and develop a pig face recognition system. This system uses an overhead camera suspended above the pig house to monitor the pig house for 24 hours and identify and track each pig. Due to the rapid growth cycle of the pig, the facial image information changes rapidly, which has a significant impact on the pig face recognition model. The acquisition camera module is designed to correct monitoring and tracking data. The acquisition camera is installed in the necessary place of the pig every day to collect real-time information and upload the collected information to the server. By comparing the data of individual pigs at different developmental stages with the server, the identification information of individual pigs is determined, and tracking data is corrected in a timely manner. At the same time, the monitoring and identification screen is displayed on the screen, and behavioral information parameters are recorded to facilitate the information management and breeding of the farm.

Internet of Everything System Based on Linux and Harmony

This project intends to design a Linux and Harmony based Internet of Everything system, which can form a complete Internet of Everything system by controlling the main control board to operate various modules and sensors. The main control chip of this project adopts I.MX6ULL as the central processor, and controls peripheral configuration modules such as ZigBee module, fingerprint lock module, camera module module, and language broadcast module. Through the collaboration of each module to serve the community and family, it makes the community management and service more efficient and the residents' life more convenient, creating a smart community and smart home combined with the Internet of Everything system, reducing the consumption of human resources, making technology services to life and improving work efficiency.

Evaluating Plant Drought Resistance with a Raspberry Pi and Time-lapse Photography.

Identifying genetic variations or treatments that confer greater resistance to drought is paramount to ensuring sustainable crop productivity. Accurate and reproducible measurement of drought stress symptoms can be achieved via automated, image-based phenotyping. Many phenotyping platforms are either cost-prohibitive, require specific technical expertise, or are simply more complex than necessary to effectively evaluate drought resistance. Certain mutations, allelic variations, or treatments result in plants that constitutively use less water. To accurately identify genetic differences or treatments that confer a drought phenotype, plants from all experimental groups must be subjected to equal levels of drought stress. This can be easily achieved by growing and imaging plants that are grown in the same pot. Here, we provide a detailed protocol to configure a Raspberry Pi computer and camera module to image seedlings of multiple genotypes growing in shared pots and to transfer images and metadata via the cloud for downstream analyses. Also detailed is a method to calculate percent soil water content of pots while being imaged to allow for comparison of stress symptoms with water availability. This protocol was recently used to uncouple differential water usage from drought resistance in a dwarf Arabidopsis thaliana mutant chiquita1-1/cost1 compared to the wild-type control. It is cost effective, suitable for any plant species, customizable to various biological questions, and requires no prior experience with electronics or basic software programming.

Implementation of License Plate Recognition Monitoring System using Neural Network on Solar Powered Microcontroller

One automatic system for monitoring the presence of vehicles in a parking zone is an indispensable mean of an area such as services building, institutions, and other organizations, which accomodated many vehicles. A tracking record is the most important matter when the vehicle leaves the parking area. Manually, one parking officer will be needed to do the job. However, when using such an automated parking system, then this officers' job can be replaced. The vehicle license plate recording system is designed to use electronic-vision devices as a fundamental device for detecting the presence of vehicle. Vehicle license plates are detected using a digital camera which captured by the camera module on the Raspberry-Pi mini-pc microcontroller device, in addition to the detection of ultrasonic sensors that capture the position of vehicle objects. The process of reading vehicle numbers in an intelligent system of artificial neural networks to extracts each character of the license plate so that each number and letter can be recognized. Meanwhile, the detection of the ultrasonic parking sensor is a complementary confirmation indicating the presence of a vehicle object being monitored. The combination of solar power as the power supply for this automatic system is an important set-up that makes the system's electricity able to run independently. This monitoring system is prepared to help increase vehicle security automatically.

A low-cost and open-source measurement system to determine the Young’s and shear moduli and Poisson's ratio of soft materials using a Raspberry Pi camera module and 3D printed parts

Advances in biomedical and engineering fields have greatly increased the need for understanding of soft structures. Soft materials such as gelatin and gelatin-based hydrogels have grown in popularity for use in a wide variety of applications including tissue engineering, biofabrication, and organ transplantation. With hydrogel structures being used in such demanding applications, it is crucial to properly characterize the dynamic behavior of these soft structures. Although there have been major improvements in measurement technology for determining the mechanical properties of soft, translucent materials, it remains quite challenging to reliably measure the Young’s and shear moduli of these materials in a way that remains straightforward, low-cost, and non-contact. This research aims to address the weaknesses in modern measurement methods and develop a system suitable for characterizing the elastic moduli of soft materials that requires only four, inexpensive, off-the-shelf components. Utilizing a Raspberry Pi, stepping motor, and an inexpensive camera, the Young’s and shear moduli of a gelatin column is measured five times. The standard deviation between measurement was observed to be less than 0.15% with high accuracy having an error of less than 4.6% when compared to relatively expensive, conventional measurement techniques.

Smart Helmet-Based Proximity Warning System to Improve Occupational Safety on the Road Using Image Sensor and Artificial Intelligence

Recently, collisions between equipment and workers occur frequently on the road in construction and surface mining sites. To prevent such accidents, we developed a smart helmet-based proximity warning system (PWS) that facilitates visual and tactile proximity warnings. In this system, a smart helmet comprising an Arduino Uno board and a camera module with built-in Wi-Fi was used to transmit images captured by the camera to a smartphone via Wi-Fi. When the image was analyzed through object detection and a heavy-duty truck or other vehicle was detected in an image, the smartphone transmitted a signal to the Arduino via Bluetooth and, when a signal was received, an LED strip with a three-color LED visually alerted the worker and the equipment operator. The performance of the system tested the recognition distance of the helmet according to the pixel size of the detected image in an outdoor environment. The proposed personal PWS can directly produce visual proximity warnings to both workers and operators enabling them to quickly identify and evacuate from dangerous situations.

Tiny Piezoelectric Multi-Layered Actuators with Application in a Compact Camera Module-Design, Fabrication, Assembling and Testing Issues.

Piezoelectric actuators with multi-layer structures have largely gained attention from academic and industry experts. This is due to its distinctive advantages of fast response time, huge generative force and the inherent good planar electromechanical coupling factor, as well as other mechanical qualities. Typically, lead zirconate titanate (PZT) is one of the most represented piezoelectric ceramic materials that have been used for multi-layer piezoelectric actuators. Piezoelectric multi-layered actuators (PMLAs) were developed vigorously in the past decades due to the emergence of portable devices, such as smartphones with a highly compact camera module (CCM) and an image stabilizer (IS). This study reviewed the progress made in the field of PMLA applications, with a particular focus on the miniaturized dimensions and associated generated output force, speed and maximum output power requirement for various loads. Several commercial attempts, such as Helimorph, Lobster and the two-degrees-of-freedom ultrasonic motor (USM), were investigated. The proposed simple bimorph and multi-layer bimorph USMs experimentally showed thrust as high as 3.08 N and 2.57 N with good free speed and structural thicknesses of 0.7 and 0.6 mm, respectively. When compared with the other 14 reported linear USMs, they ranked as the top 1 and 2 in terms of the thrust-to-volume ratio. The proposed design shows great potential for cellphone camera module application, especially in moving sensor image stabilization. This study also provided outlooks for future developments for piezoelectric materials, configurations, fabrication and applications.

Iot Based Security System for bank Vaults

The main objective of our paper is to design and implement a bank vault security system based on IOT technology. The proposed system is designed which implements a security system to handle the cash lockers by the authorized person. It is developed using Arduino Microcontroller which is interfaced to the RFID reader to read the code, keypad module for password authentication and camera module for face recognition. If unauthorized person tries to break or open the lock, it is automatically monitored and detected using embedded and IOT technology. After detection, GSM and IOT module is used to send information immediately to the respective authorities and the person is captured. The proposed system found to be cost effective with better performance and easy to implement