Rompi Penyedia Informasi bagi Penyandang Tunanetra Menggunakan Multisensor HC-SR04

Various electronic travel aids for people having visual impairment have been developed based on ultrasonic object detection employing the HC-SR04 ultrasonic proximity sensor. However, most of them do not consider blind spots where harmful objects cannot be detected. This study discusses the development of a vest that can detect objects in front of the blinds more widely and provide sound alert if an object in front is detected. This detector was developed based on an Arduino Uno equipped with five HC-SR04 ultrasonic sensors, and a mini DFPlayer module. In addition, blind area analysis of sensor detection is carried out to overcome objects that are not detected by similar studies. Horizontally, this travel vest sweeps objects up to 150 cm in distance with a 25o right or left angle deviation from forward direction. Vertically, object detection reaches up to 150 cm in distance with both upward and downward deviation of 30o from the vest.

Intelligent Shopping Trolley (IST) - a social distancing device during pandemic shopping

Intelligent Shopping Trolley (IST) is a device that was built to help in fighting the Covid-19 pandemic. This Intelligent Shopping Trolley is equipped with a RFID and timer system, Indoor Positioning System (IPS) and Microwave Sensor Detection system to determine the distance and to alert customers as they shop in a supermarket. This Intelligent Shopping Trolley will also utilise the Internet of Things (IoT) to manage its functionality. The Intelligent Shopping Trolley will help customers to determine the distance between other customers while implementing social distancing measures that is recommended by the Ministry of Health Malaysia and manage their shopping time as well. Besides that, it helps supermarkets to track their customers after the time limit given to the customers ends. This paper explains the details on this Intelligent Shopping Trolley project. This project helps in making the community aware of the importance of social distancing.

Trigger-Based Dexterous Operation with Multimodal Sensors for Soft Robotic Hand

This paper focuses on how to improve the operation ability of a soft robotic hand (SRH). A trigger-based dexterous operation (TDO) strategy with multimodal sensors is proposed to perform autonomous choice operations. The multimodal sensors include optical-based fiber curvature sensor (OFCS), gas pressure sensor (GPS), capacitive pressure contact sensor (CPCS), and resistance pressure contact sensor (RPCS). The OFCS embedded in the soft finger and the GPS series connected in the gas channel are used to detect the curvature of the finger. The CPCS attached on the fingertip and the RPCS attached on the palm are employed to detect the touch force. The framework of TDO is divided into sensor detection and action operation. Hardware layer, information acquisition layer, and decision layer form the sensor detection module; action selection layer, actuator drive layer, and hardware layer constitute the action operation module. An autonomous choice decision unit is used to connect the sensor detecting module and action operation module. The experiment results reveal that the TDO algorithm is effective and feasible, and the actions of grasping plastic framework, pinching roller ball pen and screwdriver, and handshake are executed exactly.

The accuracy of Infrared sensor detection in a smart toilet

Background: Infrared (IR) sensors are useful tools for detecting distance and proximity. However, these sensors are not good at detecting edges of an area, therefore when used in a smart toilet it has difficulty in detecting the orientation and position of the user’s body. The aim of this study was to design an IR sensor for a smart toilet with a more accurate and consistent detection. Methods: A total of 12(six men and six women) participants with different body types were involved in this study. IR sensor detection was tested in the sitting and squatting toilets. For the best accuracy, the IR sensor's angle was measured. Red, blue, and red-blue plastic covers were used, as these colors improve precision. The microcontroller was set up to calculate the participant’s distance and presence in the cubicle. Results: Toilet positioning varied greatly depending on whether one is sitting or squatting. For sitting toilet, the red cover was close to the accurate distance at a 172˚ angle. IR detected a man but not a woman's body. The blue cover provided the same best angle of 172˚ with a higher sensor distance. When the red and blue cover combination was applied, the reading of 141cm detected both men and women, at 172˚ angle. The actual distance for squatting toilets was 158cm. The optimal angle for both red and blue covers was 176˚, however the sensor distance was greater for the blue cover. Finally, the red and blue cover combination gave a more accurate distance of up to 163cm from the actual reading, when detecting both genders at a normal angle of 76˚. Conclusion: The combination of red and blue cover gave the most accurate detection for the squatting and sitting toilets. The best angle for sitting was 172˚, and for squatting was 176˚.

A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN

Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.

Concept of Short-Range Radar and Counter-Projectile for Detecting and Countering Armour-Piercing Projectiles

During warfare and acts of terrorism an extreme threat to vehicles and other high-value assets comes from armour-piercing projectiles. Under these conditions, defence systems should include devices capable of rapid detection of these threats. Defence assets should also be provided with counter-projectile systems capable of destroying incoming armour-piercing projectiles at a safe distance from the asset to be protected. This paper describes the concept of a system comprising of a lightweight short-range radar and a counter-projectile for countering armour-piercing projectiles. The purpose of the radar is to monitor the environment and search for incoming armour-piercing projectiles. When an armour-piercing projectile is detected in a designated monitoring area, an automatic command is given for the counter-projectile launcher to be fired. The counter-projectile deployed can be equipped with a single or multi-sensor detection head unit and an explosive payload module, both being the primary components of the warhead. When the signal analysis blocks interfaced with the detection head determine that the armour-piercing projectile to be struck down is in the target position in relation to the counter-projectile deployed, they automatically command the explosive payload module to detonate. The components of the system concept were tested in proving ground conditions. The successful results of these tests confirmed the validity of the solutions initially adopted and the execution of the individual systems.

Development of First Aid Kit with Intuitive Touch Screen Display and Voice Instruction

First aid kit has been used to treat minor pain or injuries before one can receive a qualified treatment from the medical doctor. It is found that the contents inside the first aid is only checked periodically and as a result, there is high chance that certain important medicine is not there by the time it is needed. Thus, the development of smart first aid kit that prioritizes on content management via intuitive display and voice instruction is presented in this paper. The developed smart first aid kit offers several unique features. The first is the use of infrared sensor to detect the availability of the first aid content. If the content is taken, the red LED will be lit up, and the user is notified through Blynk apps in smartphone and email by means of NodeMCU. The second feature is that the developed first aid is equipped with the Smart TFT LCD touch screen display and speaker. This smart touch screen can display the list of first aid contents as well as provide a quick button for voice instructions. The voice instruction button will play the recorded guidance and medical instructions in the form of phrases and voices. The programming for the touch screen display, the voice instructions (played by Arduino Mini MP3 module) and speaker are all processed by the Arduino Mega. Simple testing and analysis on sensor detection and notification revealed that both sensor and Blynk app work fine if the medications are placed in the correct position. In conclusion, a smart first aid kit with touch screen medications instructions menu equipped with voice instructions and the ability to alert the end user whenever the content of first aid is taken out has been successfully developed and executed in this study. All sensors and the programming for the touch screen and voice instruction work accordingly. For future work, several improvements are recommended such as better data management that has complete record on date, time, and name of the content that is taken out as well as alerting the user on the expiry date. This can help the person in charge to efficiently monitor the content in the first aid.

Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors

Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.