In unmanned aerial operations, the ground control station duties as a monitoring and command station so that operators on land can send mission orders, monitor the mission's course and monitor the condition of the UAV during the mission. It is necessary to have a GCS system capable of connecting with UAV that not limited with control transmitter range.This research develops GCS system using internet network and web server based. the system consists of two units, namely flying units and GCS units. The flying unit consists of Raspberry pi, modem, webcam, ADAHRS module and quadrotor with MultiWii controller. on the GCS unit consists of Raspberry pi connected on the internet network with 10Mbps download speed and 1.5Mbps upload.The GCS system can display aircraft conditions, stream video and perform command controls. Configure streaming video for delay time of no more than one second with 240x144 pixel resolution, 256kbps maximum bitrate and 5 fps framerate. This configuration runs at a 1.1 Mbps upload speed with a percentage of 93.83% bitrate compression. Aircraft condition data sent to GCS is optimal if internet bandwidth exceeds the bitrate of streaming video used on the system
Differential drive wheeled mobile robot (DDWMR) is one example of a robot with a constrained movement, Multiple Input Multiple Output (MIMO), and nonlinear system. Designing a low resource position and heading controller using linear MIMO methods such as LQR became a problem because of the linearization of robot dynamics at zero value. One of the solutions is to design a MIMO controller using a Single Input Single Output (SISO) controller. This work design a controller using PID for DDWMR Jetbot and selects the best feedback gain using different scenarios. The designed controller manipulates both motors by using calculated control signal to achieve a complex task such as path tracking with robot position in x-Axis, y-Axis, and heading angle as the feedback. The priority between position and heading angle can be adjusted by changing three feedback gains. The controller was tested, and the best gain was selected using Integral Absolute Error (IAE) metrics in a path tracking task with four different path shapes. The proposed methods can track square, circle, and two types of infinity shape paths, with the less well-formed shape being the four edges square path.
Single-frame depth prediction is an efficient 3D reconstruction method for one-side artifacts. However, for this purpose, ground truth images, where the pixels are associated with the actual depth, are needed. The small number of publicly accessible datasets is an issue with the restoration of cultural heritage objects. In addition, relief data with irregular characteristics due to nature and human treatment, such as decolorization caused by moss and chemical reaction is still not available. We therefore created a dataset of Borobudur temple reliefs registered with their depth for data availability to solve these problems. This data collection consists of 4608 × 3456 (4K) resolution and profound RGB frames and we call this dataset the Registered Relief Depth (RRD) Borobudur Dataset. The RGB images have been taken using an Olympus EM10 II Camera with a 14 mm f/3.5 lens and the depth images were obtained directly using an ASUS XTION scanner, acquired on the temple's reliefs at 15000–25000 lux day time. The registration process of RGB data and depth information was manually performed via control points and was directly supervised by the archaeologist. Apart of enriching the data availability, this dataset can become an opportunity for International researchers to understand more about Indonesian Cultural Heritages.
An electromyogram is a recording of muscle activity. These signals have been used both for medical diagnosis and engineering such as finger motion detection in healthy people and rehabilitation patients. Many studies have been conducted to map the relationship between electromyogram and finger movements, one of which is the relationship between the number of channels used and the complexity of the system. The number of channels used is directly proportional to the complexity of a system. The more complex the system, the heavier the data processing is so that it requires greater resources. Therefore, this study focuses on the construction of a classification system for human finger movements using fewer channels. The number of channels used in this study is 4. Root Mean Square is applied in a sliding window as feature extraction. The classifier used is the artificial neural network. System validation is done with 10-fold cross-validation. The test results of the average accuracy value for the thumb, index finger, middle finger, ring finger, little finger, grip, and relaxation were 89%, 90%, 93%, 95%, 93%, 94%, and 91% respectively which can be said to be quite good considering the number of channels relatively few compared to previous studies.
Differential Drive Wheeled Mobile Robot (DDWMR) is a nonholonomic robot with constrained movement. Such constraint makes robot position control more difficult. A closed-loop control system such as PID can control robot position. However, DDWMR is a Multiple-Input-Multiple-Output system. There will be many feedback gains to be tuned, and the wrong value will make the system unstable. Therefore this research proposes an offline autotune method to choose optimal feedback gain that minimizes a fitness function. The fitness function uses Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). These works propose to autotune feedback gain for DDWMR Jetbot, which implements a PI control system with six feedback gains. The methods used to tune the feedback gain are Particle Swarm Optimization (PSO) and Bird Swarm Algorithm (BSA). There are four different scenarios to do the autotune. The autotune result performance shows that those two methods can find an optimal gain to make the robot follow four different continuous trajectories without much trajectory deformation. PSO and BSA can do an autotune PI gain with six variables to minimize the Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE)
Badan Nasional Penanggulangan Bencana (BNPB) describes the number of casualties, property and environment resulting from landslides. Wireless sensor network technology can minimize the loss of life, property and environment [1, 2]. Wireless sensor networks are prone to interference, especially in data transmission. Transmission of wireless sensor data can be disrupted if material is blocked. Slides that are easily landslide in Indonesia consist mainly of soil material [3]. Soil is one material that can interfere with wireless sensor data transmission and is influenced by aspects such as temperature, weather, soil composition, soil moisture, and soil homogeneity [4, 5]. This study focuses on analyzing the effect of sensor node placement on data transmission distance on WiFi-based soil material. The results of the analysis of the placement of sensor nodes planted in the ground resulted in an average percentage attenuation of signal strength every 5 cm depth increase in soil material was 4.90%.
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