Design and Implementation of a Mobile Robot for Carbon Monoxide Monitoring

The gas detection problem is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. The mobile robot used for gas detection has several advantages and can reduce danger for humans. In this study, we proposed an integration system for a mobile robot that can be used for carbon monoxide (CO) monitoring with different operating temperatures. The design and implementation of a mobile robot system that proposed consists of the onboard and ground stations. The proposed system can read CO gas concentration and temperature then send it wirelessly using an XBee module to the ground station. This system was also able to receive the command from the ground station to move the robot. The system provided real-time acquisition data that believed can be a useful tool for monitoring and can be applied for various purposes. The experimental results show that a combination of a mobile robot and environmental sensors can be used for environmental monitoring

Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

The detection of an object such as a human is very important for image understanding in the field of computer vision. Human detection in images can provide essential information for a wide variety of applications in intelligent systems. In this paper, human detection is carried out using deep learning that has developed rapidly and achieved extraordinary success in various object detection implementations. Recently, several embedded systems have emerged as powerful computing boards to provide high processing capabilities using the graphics processing unit (GPU). This paper aims to provide a comprehensive survey of the latest achievements in this field brought about by deep learning techniques in the embedded platforms. NVIDIA Jetson was chosen as a low power system designed to accelerate deep learning applications. This review highlights the performance of human detection models such as PedNet, multiped, SSD MobileNet V1, SSD MobileNet V2, and SSD inception V2 on edge computing. This survey aims to provide an overview of these methods and compare their performance in accuracy and computation time for real-time applications. The experimental results show that the SSD MobileNet V2 model provides the highest accuracy with the fastest computation time compared to other models in our video datasets with several scenarios

Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms

Path planning is an essential algorithm to help robots complete their task in the field quickly. However, some path planning algorithms are computationally expensive and cannot adapt to new environments with a distinctly different set of obstacles. This paper presents optimal path planning based on a genetic algorithm (GA) that is proposed to be carried out in a dynamic environment with various obstacles. First, the points of the feasible path are found by performing a local search procedure. Then, the points are optimized to find the shortest path. When the optimal path is calculated, the position of the points on the path is smoothed to avoid obstacles in the environment. Thus, the average fitness values and the GA generation are better than the traditional method. The simulation results show that the proposed algorithm successfully finds the optimal path in an environment with multiple obstacles. Compared to a traditional GA-based method, our proposed algorithm has a smoother route due to path optimization. Therefore, this makes the proposed method advantageous in a dynamic environment

Online Digital Image Stabilization for an Unmanned Aerial Vehicle (UAV)

The Unmanned Aerial Vehicle (UAV) video system uses a portable camera mounted on the robot to monitor scene activities. In general, UAVs have very little stabilization equipment, so getting good and stable images of UAVs in real-time is still a challenge. This paper presents a novel framework for digital image stabilization for online applications using a UAV. This idea aims to solve the problem of unwanted vibration and motion when recording video using a UAV. The proposed method is based on dense optical flow to select features representing the displacement of two consecutive frames. K-means clustering is used to find the cluster of the motion vector field that has the largest members. The centroid of the largest cluster was chosen to estimate the rigid transform motion that handles rotation and translation. Then, the trajectory is compensated using the Kalman filter. The experimental results show that the proposed method is suitable for online video stabilization and achieves an average computation time performance of 47.5 frames per second (fps)

Perbedaan kadar protein, kadar lemak dan nilai pH susu sapi pada daerah dataran tinggi dan dataran rendah di Kabupaten Jombang

ABSTRACT  The purpose of this study was to determine the differences in protein content, fat content and pH value of cow's milk in the highlands and lowlands in Jombang Regency. A total of nine samples of cow's milk were taken from two cow milk shelters in different plains to be examined at the Veterinary Public Health Laboratory of the UWKS FKH. The protein content was tested by the formol method, the fat content was tested by the garber method and the pH value of milk was measured by a pH meter. The results showed that the average protein content in the highlands was 3.00% ± 0.012 and the lowlands was 3.20% ± 0.063, and the mean fat content in the highlands was 4.004% ± 0.063 and the lowlands 3.125% ± 0.109, the average pH value in the highlands was 6.7 ± 0.005 and the lowlands was 6.6 ± 0.043, so that the results of the comparison of cow's milk from the highlands and lowlands were analyzed using T test can be seen that there was no significant difference in the pH value and protein content, but in fat content there was a significant difference. Keywords: Cow's Milk, Protein Content, Fat Content, pH Value, Jomban

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve

Blockchain Technology

Blockchain came because of the occurrence of incredulity to single authorities by introducing the concept of network decentralization and data distribution saved in a ledger. Decentralization is used to validate discrepancies in the majority of data. The consensus mechanism collectively maintains the consistency of the ledger. A blockchain is a set of blocks containing transaction data interconnected to each other using the concept of cryptography. A mining process is an effort to add new blocks to the blockchain. The mining computer carries out the process after passing several complex mathematical problems. The fastest miner is rewarded with crypto coins. Some consensus mechanisms commonly used in blockchain are proof of work, proof of stake, practical byzantine fault tolerance, and proof of elapsed time. Blockchain network is designed and implemented in such a way that it can guarantee the security of its data, is easy to be audited, is robust to denial of service and majority attacks, and is private and confidential. The application of blockchain is not limited to finance systems; it can also be applied in health, education, supply chain, and state democracy systems

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

Artificial potential field (APF) is the effective real-time guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve one-obstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON), and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems