Academic literature on the topic 'Raspberry pi pico'

Commutation in a traffic ridden neighborhood has been a major problem for students to reach classes on time. Relying on public modes of transport does not assure punctuality to the students as well as the faculty. Introduction of e-bicycle rental system helps our students to access university owned bicycles in an efficient and eco-friendly way. We propose to introduce a bicycle rental system which will monitor the location of bicycles and the duration of rental. Password enabled locks ensure anti-theft. This approach can reduce the time and money wasted by the students for short distance commutation in and around the campus, in a healthy, eco-friendly way. Proposed range of accessibility for the rental shall be a perimeter of few hundred meters from the school/college campus. There shall be specified docks to park the cycle from where the students can pick up their rental cycle or drop it off after their rental use. In future the project can be expanded for a wider outreach to general public. The proposed system can be improvised with dock less parking, which will increase the availability of cycles.

The drone noise mainly comes from its rotating blades, providing plentiful information of the status of the drone. In the production line, the abnormal sound detection system has the advantages of no contact and simple deployment and can help to locate the fault products at relatively low costs. Therefore, this paper develops an abnormal drone noise detection system based on the microphone array and self-supervised learning. The microphone array is a part of the data acquisition module to pick up the drone noise. There are eight microphones in the array, forming four differential microphone pairs. Each of them is pointing to a blade of the drone. A four-channel noise sample is recorded and then analyzed. It is worth noting that drone noise samples are extremely unbalanced, because abnormal samples are difficult to encounter. Hence, a self-supervised learning strategy is adopted by creating auxiliary classification tasks to fine tune representations of the normal drone noise samples. With the consideration of low-complexity, the trained neural network models can be finally deployed even on a Raspberry Pi system with no graphic cards.

This paper proposed an innovative mechanical design using the Rocker-bogie mechanism for resilient Trash-Collecting Robots. Mask-RCNN, YOLOV4, and YOLOv4-tiny were experimented on and analyzed for trash detection. The Trash-Collecting Robot was developed to be completely autonomous as it was able to detect trash, move towards it, and pick it up while avoiding any obstacles along the way. Sensors including a camera, ultrasonic sensor, and GPS module played an imperative role in automation. The brain of the Robot, namely, Raspberry Pi and Arduino, processed the data from the sensors and performed path-planning and consequent motion of the robot through actuation of motors. Three models for object detection were tested for potential use in the robot: Mask-RCNN, YOLOv4, and YOLOv4-tiny. Mask-RCNN achieved an average precision (mAP) of over 83% and detection time (DT) of 3973.29 ms, YOLOv4 achieved 97.1% (mAP) and 32.76 DT, and YOLOv4-tiny achieved 95.2% and 5.21 ms DT. The YOLOv4-tiny was selected as it offered a very similar mAP to YOLOv4, but with a much lower DT. The design was simulated on different terrains and behaved as expected.

Sorting is the process of systematic selection and arrangement. Sorting involves intense labor work. The use of Artificial Intelligence in recognizing the objects by their color makes the process of sorting completely autonomous. Modern Industries require modern solutions for the problems encountered during the process of sorting. With the advent of Artificial Intelligence, the machines that can recognize an object by their color proves to be a primary solution that can completely automate the process of sorting. This paper presents a five-axis arm mounted on a robotic model that makes use of a color sorting technique. It performs pick and place operations in realtime. The color sorting technique detects the color of the object in the frame captured by the camera. The frame size is used to detect the position of the object in the real world. The robot model moves according to the frame size of the object. Raspberry Pi microcontroller drives the servo motor and dc motor to move the five-axis arm and the robotic model to sort and perform pick and place operation based on their color. The color sorting algorithm is based on the Hue-Saturation-Value model. This model finds its application in places where sorting is done based on color and not the object itself. For example, it is used to sort objects like different colored clothes, food items, etc. It also finds its application in very large scale warehouses such as Amazon, Flipkart, etc which focusses on smart automated warehouses that reduce the labor requirements

This paper presents low cost automation system for textile industries where colour and shape are detected along with pick and place robotic arm. Edge detection techniques and Contour approximation algorithmare used for pattern detection.The main goal is to count the number of samples of each pattern or shapes. This system makes use of raspberry pi with a PI camera. The PI cam is used for capturing the image of the textiles being moved on a conveyor belt. The system is programmed using open CV platform. The simulation results using OpenCV environment coded with Python are presented

<p class="HeaderAbs"><strong>Abstract</strong></p><p class="Abstract">Coffee has been cultivated as the secondary produce for decades in Girimarto, Wonogiri, however, the selective picking practice remain alient for local farmers. Selective picking is considered impractical due to time consumption and laborous work that farmers should carry out. The community service project designs and implements image recognition technology to help acquire coffee-cheery ripeness condition. Adopting a geolocation, the appropriate routing strategies, would enable farmers to selectively pick the red cherries in a systematic sequence. The image processing technology was applied by adopting Raspberry Pi microcomputer, <em>Raspberry Pi Camera Board, </em>version <em>2</em>, and OpenCV programming language. The transition to selective picking and the subsequent post-harvest technology would likely produce high-quality green bean coffee. It is expected that the income of smallholder coffee farmers will gradually be increasing.</p><br /><p class="HeaderAbs"><strong>ABSTRAK</strong></p><p class="Abstract">Praktek petik buah merah dalam budidaya kopi belum menjadi prosedur baku pada perkebunan kopi rakyat di wilayah Girimarto, Wonogiri. Petani menganggap praktek petik merah pada panen buah kopi merepotkan dan memakan waktu karena dalam satu kunjungan ke kebun mereka hanya memetik buah kopi yang benar-benar matang. Pengabdian ini mendesain dan menerapkan perangkat pemantau kematangan buah kopi untuk membantu petani memperoleh informasi lokasi buah matang dan estimasi jumlahnya sehingga petani bisa merencanakan jalur pemetikan kopi berdasarkan lokasi batang pohon kopi tingkat kematangannya. Teknologi <em>image processing</em> diterapkan dengan mengadopsi penggunaan komputer mikro <em>Raspberry Pi</em>, modul kamera <em>Raspberry Pi Camera Board, </em>versi <em>2</em>, dan bahasa pemrograman <em>OpenCV. </em>Perubahan pola panen buah kopi dari petik sembarang (petik racutan_ menjadi panen petik merah diikuti dengan perbaikan proses fermentasi buah kopi diharapkan menghasilkan kualitas green bean menjadi lebih baik dan harga jual yang lebih tinggi.</p>

Coffee has been cultivated as the secondary produce for decades in Girimarto, Wonogiri, however, the selective picking practice remain alient for local farmers. Selective picking is considered impractical due to time consumption and laborous work that farmers should carry out. The community service project designs and implements image recognition technology to help acquire coffee-cheery ripeness condition. Adopting a geolocation, the appropriate routing strategies, would enable farmers to selectively pick the red cherries in a systematic sequence. The image processing technology was applied by adopting Raspberry Pi microcomputer, IP camera <em>ReoLink</em> RLC411WS, and OpenCV programming language. The transition to selective picking and the subsequent post-harvest technology would likely produce high-quality green bean coffee. It is expected that the income of smallholder coffee farmers will gradually be increasing.

Main objective of proposed system is to develop a cloud based smart system for school bus/vehicles that track and monitor the student entering in to the bus and by using cloud computing, alert their parents and school administration about student entering or leaving school bus as well as about any emergency occurs. As all know that in today’s fast lifestyle parents don’t have more time to drop and pick their children at bus stop or school. There are number of problem occur in the society about safe transportation of children from home to school and vice versa. Parents have always tension about transportation through buses/vehicle. To avoid this problem this system is proposed. Main aim is to develop a system which is beneficial for society, reduce waiting time on the bus stop, reduce crime against student and increase the safe transportation of student from home to school and vice versa. System monitors every student get entered into the school bus. The proposed system will be definitely helpful for real time tracking of school bus. Cloud based system is designed that is configured with Raspberry Pi IOT module for fast processing and data access. This module has to be attached to every bus to capture the real time data. And the data from many buses are well managed by ThingSpeak cloud from MATHWORK. Monitoring and alerting through cloud computing.

Syftet med detta arbete var att få en djupare förståelse om fysiken och tekniken inom området analog elektronik och mikrokontrollerkort. Via simulering, utveckling och konstruktion av olika kretslösningar transformerades ljudsignaler efter önskad effekt. Projektet innebar att teoretiskt framtaga dessa kretsar med önskad utsignal och eftersökt effekt, däribland wah och fuzz. Dessa kretslösningar realiserades sedan praktiskt till den grad att dessa kopplades mellan instrument och förstärkare.Dessa effektpedaler producerades i både analog och digital tappning, vid den digitala med hjälp av mikrokontrollerkortet Raspberry Pi Pico och vidare digital ljudbehandling. Ena analoga pedalen, med fuzzeffekt, fungerade inte vid konstruktion. Andra analoga pedalen, wahpedalen, fungerade begränsat och den digitala pedalen uppfyllde de efterfrågade specifikationerna och var fullt användbar vid hopkoppling med elgitarr.

An embedded system integrated with sensors based on nanomaterial is proposed for closely monitoring and control microclimate parameters 24 hours a day to maximise production over the whole crop growth season by introducing greenhouse for the cultivation of plants or specific plant species. The system will also eliminate errors in human intervention to optimise production of crops. This system consists of sensors and actuators, an Analogue to Digital Converter (ADC) and a Raspberry Pi. The system will determine whether a defined threshold is passed by any climate parameter and systematically changes via the controller. The current work reduces human input through automated irrigation to optimally utilize a scarce resource, namely water. Climatic parameters for plant growth such as, moisture, humidity, temperature, water pressure in drip pipe, soil salinity etc. are monitored and optimized. Furthermore, work was extended to include GSM to control the entire farm remotely. For its success, it is very important to choose a greenhouse location. For instance, the problems are quite different when choosing an adjoining greenhouse, for instance a sunroom or greenhouse. The greenhouse location should be chosen for sunlight, proximity to power and water sources, wind, drain and freeze pockets, and the proximity of the garden and house. The intention behind accomplishment and devise of GSM based Fertigation System is to construct and evaluate the requirement of water in the yield as farming is the major resource of production which habitually depends on the water accessibility. Irrigation of water is usually done by manual method. To ease the work of the farmer GSM based automatic Fertigation (includes chemigation too) system can be implemented so that water wastage can be reduced and also the fertilizer can be added accordingly. Also the Soil Salinity can be checked and reduced if exceeds certain limit. By using GSM, only GSM command via GSM mobile can control the start and stop action of a motor that feeds the field with the water. GSM is used for controlling the entire process and the entire system backbone. It can be used from any distance to control irrigation. The results are assessed by electronic simulator PROTEUS using the desired optimised parameters, the design of this automated greenhouse system with PIC controller. As the inputs to the microcontroller and as an LCD screen record the respective outputs, the model produces a soil moisture sensor, light sensor and temperature sensor. The system performance is accurate and repeatable for measuring and controlling the four parameters that are crucial for plant growth - temperature, humidity, soil moisture and light intensity. With the reduction in electricity consumption, maintenance and complexity, and a flexible and precise environment control form for agriculture, the new system successfully cured quite a couple of defects in existing systems. Nano composite film sensors (Graphene and Graphene mixed in order to optimise the input of fertilisers for chemical composition determination. Using nano technology in agriculture enforces the firm bond between the engineer and farmer. Nano material film-based gas sensors were used to measure the presence of oxygen and CO2.using graphene nano composite sensors integrated into an embedded system, to detect the presence and levels of gases. Improve crop growth with combined red and blue light for lighting under the leavened and solar-powered LED lighting modules. This was achieved by graph/solar cells. The light was measured at the photosynthesis flux (PPFD) of 165 μmol m-2 s-1 by 10 cm of its LED module. LED lights were provided between 4:00 a.m. and 4:00 p.m. in the daytime treatments and night treatments from 10 to 10 hours. The use of the nighttime interlumination of LEDs was also economical than the interlumination of charts. Thus, nightlighting LEDs can effectively improve plant growth and output with less energy than the summer and winter times. Solar panels are best functioning during times of strong sunlight today, but begin to wan when they become too hot and cloudy. By allowing Solar Panels to produce electricity during harsh weather conditions and increase efficiency, a breakthrough in graphene-based solar panels can change everything. Ultimately with a fully autonomous system, agricultural productivity and efficiency, the length of the growing season, energy consumption and water consumption were recorded and monitored by exporting the data over GSM environment. With the steady decrease in the cost of high-performing hardware and software, the increased acceptance of self-employed farming systems, and the emerging agricultural system industry, the results will be reliable control systems covering various aspects of quality and production quantity.