This paper presents a three-dimensional computer vision-based object recognition on FLoW-Vision in RoISC (formerly ER2C) has entered its second phase. Previously, the robot had a basic vision that was used to replicate ‘human-like’ visual skills using 2D computer vision. As a result of the above discussion, we proposed the design and implementation of an object recognition and pose estimation system based on three-dimensional computer vision to handle object recognition and pose estimation tasks in real-world environments simultaneously. In the object recognition process, a point-cloud segmentation method is used to obtain possible object clusters before starting the calculation of feature description. Then, a keypoints-based two-stage matching process is performed to speed up the computation of finding correspondences between the object clusters of the current scene and a colored point cloud model of an object. Next, a Hough voting algorithm is employed to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Last process process the pose estimation from clustered object using RANSAC to search the largest surface as Z surface. Experimental validate the object recognition can work correctly with percentage 100% and pose estimation accuracy of the proposed system can work correctly with percentage 60% in a complex real-world scene.

A robot that can move independently is an essential aspect towards replacing humans in hazardous work conditions. This paper shows the development of a mobile robot called ROISC-v1.0 (Robotic Disinfectant), which executes the sterilizing procedure in the room using UV light with a wavelength of 222nm. The goal of this study is to create a wall-following navigation system with obstacle avoidance capabilities. The modeling of the behavior-based control method is used in the application of a navigation system, including wall following and obstacle avoidance so that the mobile robot can modify linear and angular speeds based on the course of motion. Behavior-based control is used to eliminate the robot's reliance on its work area conditioning. To identify the distance between the robot and the wall, as well as the existence of obstacles in the robot's work area, the ROISC-v1.0 robot uses array of 12 Lidar sensors type VL53L0X. As a result, the robot navigates successfully to follow the contours of the wall and avoiding static and dynamic obstacles. When there are no obstacles in the way, the ROISC-v1.0 robot can perform optimally and efficiently in a 4.5m x 2.8m work area with an average robot travel time of 73.4 seconds and an average robot distance of 880.4 cm. With an average travel time of 109 seconds and a distance of 1052.4 cm, the robot can perform optimally and efficiently in regions where there are obstacles. The VL53L0X ToF sensor, which uses light waves in the transmission process and has an average inaccuracy of 0.7cm, allows the robot to read bright objects more accurately. The ROISC-v1.0 robot is hoped to aid medical professionals as the result of this research, minimizing the impact of virus dissemination caused by the sterilization process.

Prevention of the spread of disease is the most critical effort before the domino effect occurs. One way to prevent its spread is to practice social distancing. Prevention of the spread of covid-19 can be easier by doing Contact Tracing. Contact tracing is done to find out who is infected. This paper proposes an accurate and energy-efficient tracing system based on an android application by utilizing Bluetooth Low Energy (BLE) signals from smartphones to automatically detect possible contacts between users. Using BLE level signals, each app estimates its distance to its opponent. However, calculating the approximate distance with a BLE level signal cannot yield a sufficiently accurate number. Therefore, the prototype of the contact tracing system implements zoning based on the signal level. The results show that the Received Signal Strength Indicator (RSSI) threshold at one meter is accurate for tracing, provided that the devices share the same chipset. Moreover, it is pretty energy-efficient to be installed on a user’s Android smartphone because it only requires 14.17 mAh for 6 hours of operation or the equivalent of 2.14 mAh per hour.

Nowadays, wireless sensor networks (wsn) have many implementations in agriculture, animal husbandry, education, and many more. To support the lifetime of wsn nodes to achieve battery-saving protocol, we propose the head cluster method. This protocol will reduce the power consumption in data transmission by using the concept of head cluster and cluster member. In this method, communication using the cluster head is only carried out by the group head to save energy consumed. Based on the simulation results, the network lifetime with the cluster head method can be increased compared to a single hop.

Robots are a tool that is widely developed today, such as Humanoid, Animal, and others. In this study we discussed about animal robots. One such type of robot is Quadruped Robot. The problem that often arises in quadruped robots is that when performing stunts to be able to walk up or climb a ladder, the robot will not be able to walk with the posture adjusting the slope in the arena. This is due to the effect of earth's gravitational force that results in robots always being attracted to earth. This results in the robot's body losing balance and can accelerate damage to the servo motor due to the robot's unbalanced load. With this problem, this paper makes the control system with Fuzzy Logic place the load position in the middle of the COG (center of gravity) to balance the robot on the trajectory. The balance of the robot uses the IMU (Inertia Measurement Unit) position sensor reference with the reference derived from the angle slope (Yaw, Pitch and Roll) which is processed to adjust changes in the x, y and z axes, so that the robot can adapt to the trajectory of the stairs.

Humanoid robots are robots that resemble humans. ERISA robot is a robot made by EEPIS participating in the KRI robot competition in the humanoid dancing robot division. This robot has a mission to walk and dancing in predetermined game field zones. While dance, the walking-trajectory may drift outside the desired zone since there is no feedback for the robot to correct its trajectory. In this research, a VL53L0X rangefinder sensor and an IMU MPU6050 sensor are deployed in the system to measure the robot position against the fence installed in the game field. The sensor is mounted on the side sole in one of the robot legs. The sensor will give the relative distance from the robot to the fence and its absolute heading angle. The footstep distance will be calculated using odometry by using the robot steps and its angle heading. By using walking-trajectory control the robot can move in the desired trajectory relative to the fence, which is used as the guardrail, while the robot walks to the target zone in the game field.

Drive technology for mobile robots is currently developing very quickly, especially for the type of wheeled driven platform. The driving models such as Ackermann steering, DDMR, and Omni-wheel robots have been widely implemented as mobile robot platforms. However, to answer the challenges in the robot contest where the competition is getting tougher, research is needed on a mobile platform that is more efficient, faster, and more precise. In this research, the design and fabrication of a mobile robot platform with a swerve drive model will be carried out. Swerve drive has independent driving and steering at each wheel. This model can provide a higher speed and freedom of maneuver for the robot compared to the DDMR, Ackermann steering, and Omni wheel drive models. However, swerve drive requires a higher number of motors as well as a more complex control algorithm in regulating the speed of the wheel drive motor and the steering angle on each independent wheel to be further controlled simultaneously in moving the robot to the target position and orientation. In terms of the control system, a multi-level control will be made where the low-level control will regulate the speed of the wheel drive and its relative direction to the robot’s body. Meanwhile, high-level control will be used to coordinate the movement of each wheel so that the results can make the robot to move according to the given trajectory.

The robot contest is one of the fascinating events that drive innovation in robotics research and development. ERISA is a humanoid robot dancing developed by EEPIS students to participate in such a contest. ERISA robot is designed to be able to dance traditional Indonesian dances with agile and attractive movements. ERISA robot has a mission to dance and move from the Start Zone to the Finish Zone. Unfortunately, it is hard to make the ERISA robot stop at the desired zone without rigorous tuning because the walking trajectory in ERISA still uses an open-loop control. Since each field zone in the game field has a different color characteristic, it can be used as the robot’s guidance to assist its walking trajectory. In this research, the Pixy CMUcam5 camera will detect the Finish Zone in the game field and marked it as the target position. Since the robot heading is changing during its movement, an Inertial Measurement Unit (IMU) sensor is used to correct the projection of the target position. Thus, the location is processed in the form of(X, Y) coordinates, which are used as the reference to control the robot walking trajectory. As a result, the robot can walk towards the target accurately.

In the last decade, the era of the Industrial Revolution 4.0 began. Technology played a major role in almost all industrial revolutions. So, computers and manufacturing systems will be able to collaborate. One of the pillars of the fourth Industrial Revolution is crowdsensing. Several experiments in the fields of society, infrastructure, agriculture, and health have used it. Crowdsensing in the health sector can help gather a substantial source of data on general public health conditions. However, most crowdsensing techniques rely on only one communication protocol. This method can cause problems if a system failure occurs. Besides that, another consideration is that the crowdsensing users consist of several communication protocols. As a result, we present a multi-communication protocol gateway architecture for crowdsensing that addresses the aforementioned issues by combining two communication protocols, such as HTTP and MQTT, into a single gateway. This gateway serves to receive data from sensor nodes before transmitting it to an IoT platform on a cloud server. The test results show that the MQTT protocol has better performance than the HTTP protocol in the delay test. The MQTT protocol delay reaches 0.113 seconds and the HTTP protocol delay is 0.717 seconds. In addition, the gateway is also able to receive data properly even though both protocols are run simultaneously.

APIs are critical for digital transformation as well as the establishment and development of new business models. They are the foundation of application economics which allows for quicker, better, and less expensive development. In security perspective, OWASP released its first API security report in 2019 which finally differentiate the security risk categories between API and web application. In recent years, there have been many incidents of cyber attacks related to API, while the implementation of the API itself is growing in popularity among organizations. Therefore, the need to understand APIs from a security perspective should be taken seriously and considered as an integral part of the software development life cycle. In this research, we proposed an API security learning environment called Vulnerable Academic Information System (VAIS) based on the OWASP API Security Risks with containerization deployment plan and gamification technique to provide a fun yet challenging environment for people in understanding the API security in a legal environment.

Nowadays, computer-based tests are commonly used since a pandemic made educational institutions hold exams from home and it made computer-based tests application demand increasing time by time. This situation leads to the need for applications that can run on any device, secure from cheating and compatible with poor internet connection. Our proposed system able to improve security and deception issues in the examination. In this paper we also introduce distributed online computer-based test for English test that made our software can share questions-data to another instance securely.

This paper proposed an algorithm to recognize intersection cylindrical feature, especially counterbore and countersink geometry. The proposed algorithm analyzes 3D solid drawing in term STEP format to find intersection cylindrical feature based on a specific properties of counterbore and countersink geometry. The proposed algorithms was verified experimentally by implementing to holes inspector (HECTOR) software. A 3D product model with five different hole was designed to validate the proposed algorithm. according to the experiment result, counterbore and countersink on the 3D specimen product is properly recognized by the proposed algorithm.

The global data footprint doubles every few years. Organizations struggle to gain meaningful insights from the rapidly growing volumes of data to use this ever-increasing data. Organizations need to manage capacity placement and data governance requirements with speed and flexibility, and minimal costs to reduce capital expenditure. One of the limitations is that traditional storage still has a customer’s need to spend on capital expenditure. The customer needs to pay more every year for maintenance. Many companies consider moving internal data out of their data centers or migrating their data into the cloud. And also, this cloud technology can make the customer no need to worry about the scalability of their resource. Furthermore, the customer only needs to pay for what the customer use on the cloud. Unfortunately, until now, a user does not have many options to deploy object storage solutions across on-premise data centers and the cloud with high–performance capabilities. Storage Gateway is a hybrid cloud storage service that can give users access from their on-premises to virtually cloud storage with unlimited scalability. This Storage Gateway can connect between cloud storage and on-premises site seamlessly. This gateway system can interact with cloud storage services more simply while boosting performance and reducing cost. This paper aims to study the influence of the implementation and optimization of Storage Gateway as a heterogeneous integrator between premise and cloud storage. Also, the Network factor is using optimized by CDN Enabled to reduce upload latency between on-premise to Bucket. Tests are carried out using direct measurement methods to measures Throughput, Latency, and cost-efficiency.

Wireless Body Area Network (WBAN) is a health service that consists of small sensors attached to the patient's body to improve health services. Apart from the importance and necessity of the WBAN, there are some problems in WBAN such as inaccurate measurements, hardware failures, discharged sensors, and sensors running out of energy which can cause false alarms and reduce confidence in using the WBAN system for remote health services. This research aims to create a system or modelling to solve anomalous data problems in WBAN health data. The system will process data to produce an analysis of information in an anomalous condition by applying the statistical method using Mahalanobis Distance and the prediction method using SMOreg. The process of collecting data and pre-processing is carried out before completing the detection of anomalous features. The data normalization process is carried out to make the process faster. This approach was tested on a real health dataset on the WBAN sensor. The results of the implementation show that the method produced a high-performance detection ratio (DR), which is above 80%. In addition, the experiment resulted that the size of the sliding window affected the anomaly detection results.

This paper proposes an adaptive control strategy to improve the performance of the Social Force Model (SFM) based mobile soccer robot navigation using the Fuzzy Inference System (FIS). The combination of FIS dan SFM is then called a Fuzzy Social Force Model (FSFM). In the FSFM strategy, FIS is used to adaptively adjust the parameters of SFM based on the stimulus from the external condition, namely the obstacle's relative distance to the robot, d, and its direction, γ, so that the robot's reactivity and responsiveness can be automatically adjusted. Currently, we adjust only one parameter, namely obstacle's gain value, k. Obstacle's gain value will control the amount of forces produced by the closest obstacle. We tested our proposed method using a realistic 3D simulator, called V-Rep, by utilizing an omnidirectional robot model from Festo, namely Robotino. Our experimental results show that our proposed FSFM can work well by always successfully finishing all of the trials with less collision with obstacles. The comparison results with the fixed-parameter method prove that our proposed method is better and very promising to be implemented for real robot applications.