Combination of Flex Sensor and Electromyography for Hybrid Control Robot

The alternative control methods of robot are very important to solved problems for people with special needs. In this research, a robot arm from the elbow to hand is designed based on human right arm. This robot robot is controlled by human left arm. The positions of flex sensors are studied to recognize the flexion-extension elbow, supination-pronation forearm, flexion-extension wrist and radial-ulnar wrist.The hand of robot has two function grasping and realeasing object. This robot has four joints and six flex sensors are attached to human left arm. Electromyography signals from face muscle contraction are used to classify grasping and releasing hand. The results show that the flex sensor accuracy is 3.54° with standard error is approximately 0.040 V. Seven operators completely tasks to take and release objects at three different locations: perpendicular to the robot, left-front and right-front of the robot. The average times to finish each task are 15.7 ssecond, 17.6 second and 17.1 second. This robot control system works in a real time function. This control method can substitute the right hand function to do taking and releasing object tasks

Perancangan Mesin Penghitung Benih Ikan Otomatis untuk Membantu Kinerja Peternak Ikan

Penghitungan benih ikan secara manual memiliki banyak kekurangan, yaitu proses yang lama, cenderung tidak akurat dan tidak efisien serta mengakibatkan kerugian bagi petani benih ikan. Seiring kemajuan teknologi, dikembangkan solusi terhadap permasalahan tersebut dengan menggunakan sensor inframerah untuk penghitungan benih ikan. Benih ikan dilewatkan dalam pipa yang berukuran 1 inchi dengan variasi kemiringan sebesar 0º, 15º, 30º dan 45º. Pada pengujian dengan kemiringan 30º memberikan hasil penghitungan yang akurat hingga 99% dengan waktu penghitungan 58 ms. Pemasangan katup diletakkan pada pangkal pipa sebelum sensor agar tidak terjadi kelebihan pada penghitungan. Katup dirancang untuk menutup secara otomatis saat benih ikan telah mencapai jumlah yang ditentukan. Dengan waktu penghitungan yang cepat dan akurat, alat dapat memberikan hasil yang optimal saat penghitungan benih ikan

Perancangan Perangkat Lunak Antivirus Dengan Metode Scanning Cyclic Redundancy Checksum-32.

Abstract— Perkembangan teknologi komputer yang sangat pesat telah memicu perkembangan suatu sistem otomatisasi computer vision. Salah satu aplikasi pengembangan teknologi berkaitan dengan computer vision adalah sebuah aplikasi yang dapat mengenali objek pada citra. Pengenalan citra huruf sering terkendala dari kemampuan sistem yang hanya mampu mengenali citra pada ukuran font dan jarak tertentu. Oleh karena itu dibuatlah sebuah aplikasi pengenalan citra huruf yang mampu mengenali citra huruf dengan variasi ukuran font dan jarak. Tahapan proses pengenenalan citra huruf yaitu capture, konversi citra RGB ke citra intensitas, segmentasi, pelabelan-filtering, ekstraksi ciri dengan metode zoning, pelatihan dengan jaringan syaraf tiruan backpropagation dan pengujian. Hasil pengujian menunjukkan dari 1500 data citra huruf yang digunakan, 1217 dikenali dan 283 tidak dikenali, dengan tingkat keberhasilan yang berhasil dicapai sebesar 84 %

ANN Models for Shoulder Pain Detection based on Human Facial Expression Covered by Mask

Facial expressions are a method to communicate if someone feels pain. Moreover, coding facial movements to assess pain requires extensive training and is time-consuming for clinical practice. In addition, in Covid 19 pandemic, it was difficult to determine this expression due to the mask on the face. There for, it needs to develop a system that can detect the pain from facial expressions when a person is wearing a mask. There are 41 points used to form 19 geometrical features. It used 20.000 frames of 24 respondents from the dataset as secondary data . From these data, training, and testing were carried out using the ANN (Artificial Neural Network) method with a variation of the number of neurons in the hidden layer, i.e., 5, 10, 15, and 20 neurons. The results obtained from testing these data are the highest accuracy of 86% with the number of 20 hidden layers

The Use of Artificial Neural Networks in Agricultural Plants

Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products

Hand Gesture to Control Virtual Keyboard using Neural Network

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character

Perancangan Platform Pengaduan Perundungan Berlandasarkan Bukti menggunakan Metode Agile

Penelitian ini bertujuan untuk membuat sebuah platform pengaduan perundungan berlandaskan bukti yang terhubung dengan institusi terkait. Orang ketiga dapat menggunakan platform ini untuk melaporkan kejadian perundungan. Platform ini juga dapat digunakan untuk mengetahui kesehatan mental penggunanya. Platform memiliki fitur konsultasi dalam jaringan melalui fitur chat serta artikel edukasi psikologi dengan berbasis Progressive Web App. Laporan dapat dilakukan oleh korban ataupun pihak ketiga. Laporan perundungan akan masuk ke sekolah korban dan diproses melalui admin sekolah. Pelapor dapat memantau status dari kasusnya. Sekolah dapat merekap laporan kasus dalam rentang waktu tertentu. Pengujian telah dilakukan bersama siswa SMP dan SMA, guru BK, mahasiswa, bagian kemahasiswaan perguruan tinggi dan masyarakat umum. Pengujian dilakukan dengan peran admin institusi, admin pengaduan dan pengguna dengan total responden sebanyak 81 orang. Pengujian dilakukan terkait fungsional menggunakan blackbox test dan uji performa dari platform berdasarkan beberapa aspek. Berdasarkan pengujian yang telah dilakukan, platform ini sudah berfungsi dengan baik. Nilai rata-rata pengujian untuk semua fungsi adalah 351 dari maksimal 400 poin. Pengujian memberikan nilai rentang kelompok tinggi terhadap performa platform karena sudah sesuai dengan kebutuhan pengguna dan sudah memiliki tampilan yang mudah dimengerti, nyaman digunakan. Platform ini bisa menjadi alternatif solusi untuk menyelesaikan kasus perundungan terutama di sekitar kita

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study aims to realize human-following autonomous movement based on human recognition. In addition, the distance from the robot to the person is measured with a stereo thermal camera that uses two thermal cameras. A coaxial two-wheeled robot that is compact and capable of super-credit turning is used as a mobile robot. Finally, we conduct an autonomous movement experiment of a coaxial mobile robot based on human recognition by combining these. We performed human-following experiments on a coaxial two-wheeled robot based on human recognition using a stereo thermal camera and confirmed that it moves appropriately to the location where the recognized person is in multiple use cases (scenarios). However, the accuracy of distance measurement by stereo vision is inferior to that of laser measurement. It is necessary to improve it in the case of movement that requires more accuracy

Digital Stethoscope to Examine Patients According to Technical Guidelines of Covid-19

Health professionals have to use Personal Protective Equipment (PPE) that could cover the entire body to prevent the transmission of the SARS-CoV-2 virus while examining patients. The current stethoscope is not compatible with the need of health professionals. Therefore, this project aims to create a digital stethoscope that can fulfill the requirement of health professionals to examine patients according to health service guidance in Covid-19 era. Three main objectives in designing the stethoscope are to meet the needs of health professionals, easy and convenient to use. The designed stethoscope has an amplifier circuit with 100 gain and a bandpass filter with 20 Hz and 700 Hz for low and high cut-off frequencies. It is also able to calculate the heart rate and displays it on the stethoscope screen. The stethoscope has a record function that lets the users replay sound as much as they need for the analysis. This digital stethoscope has function tests, performance tests and user satisfaction tests. Seven health professionals who are well-experienced in handling Covid-19 patients auscultate and provide an assessment of the three objectives design. The test results showed that this stethoscope satisfies the health professional’s requirements to examine the patient, grade 4.69 of 5. Health professionals also give a perfect rating for the objective of being easy to use. This stethoscope is the right choice for examining patients according to health service guidance in the Covid-19 era

Estimation of the Shoulder Joint Angle using Brainwaves

This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles   0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation