Further Investigation on Building and Benchmarking A Low Power Embedded Cluster for Education

Embedded parallel computing become popular, and the future of innovation in the semiconductor industry will be in ubiquitous computing. Many researchers built embedded cluster system with limited number of devices, but we utilize the device from embedded classroom to build more number of parallel computing unit. In this paper we built low power cluster consisting 32 ARM boards with low-cost customized power supply for high performance computing class for education purpose, tested with several benchmarks on embedded cluster system and analyse the raw performance

Indonesian Automatic Speech Recognition For Command Speech Controller Multimedia Player

The purpose of multimedia devices development is controlling through voice. Nowdays voice that can be recognized only in English. To overcome the issue, then recognition using Indonesian language model and accousticc model and dictionary. Automatic Speech Recognizier is build using engine CMU Sphinx with modified english language to Indonesian Language database and XBMC used as the multimedia player. The experiment is using 10 volunteers testing items based on 7 commands. The volunteers is classifiedd by the genders, 5 Male & 5 female. 10 samples is taken in each command, continue with each volunteer perform 10 testing command. Each volunteer also have to try all 7 command that already provided. Based on percentage clarification table, the word “Kanan†had the most recognize with percentage 83% while “pilih†is the lowest one. The word which had the most wrong clarification is “kembali†with percentagee 67%, while the word “kanan†is the lowest one. From the result of Recognition Rate by male there are several command such as “Kembaliâ€, “Utamaâ€, “Atas “ and “Bawah†has the low Recognition Rate. Especially for “kembali†cannot be recognized as the command in the female voices but in male voice that command has 4% of RR this is because the command doesn’t have similar word in english near to “kembali†so the system unrecognize the command. Also for the command “Pilih†using the female voice has 80% of RR but for the male voice has only 4% of RR. This problem is mostly because of the different voice characteristic between adult male and female which male has lower voice frequencies (from 85 to 180 Hz) than woman (165 to 255 Hz).The result of the experiment showed that each man had different number of recognition rate caused by the difference tone, pronunciation, and speed of speech. For further work needs to be done in order to improving the accouracy of the Indonesian Automatic Speech Recognition system.Keywords: Automatic Speech Recognizer, Indonesian Acoustic Model, CMU Sphinx, indonesian Language Model, Recognition Rate, XBMC

Merging of Depth Image Between Stereo Camera and Structure Sensor on Robot “FloW” Vision

Human can recognize an object just by looking at the environment, this capability is very useful for designing the reference of humanoid robot with the ability of adapting it on its environment. By knowing the field conditions that exist in such environments, robot can understand the obstacles or anything that can be passed. To do that, robot vision needs to have a knowledge to understanding an obstacles that exist around it. Because of these problems, this paper shows a method for reducing error rate of vacant space in the data depth by combining a stereo camera and structure sensor. Merging the stereo camera and structure sensor can extract depth information becomes dense. The proposed method has been successfully running the whole algorithm built and has a density of depth with an average error rate of vacant space is 18.10%

Digital Twin and Blockchain Extension in Smart Buildings Platform as Cyber-Physical Systems

Cyber-physical systems is integrated computation with the physical world. CPS increasing in a wide range of applications, from smart homes to smart buildings. Digital twins are promising way to solve challenges with combination of CPS, 3D technology, and IoT. The system provides users with immersive interfaces to control and interact with devices within the smart building environment. Blockchain was chosen to secure user data using cryptographic algorithms and ensure data protection against manipulation, spying, and theft. Average load testing data for digital twin platform implemented in smart buildings range from 1 to 11 floors. The results reveal a gradual increase in average test times as the buildings' size and complexity grow, with the following values: 5.663s for 1 floor until 11 floors 7.294s. The data obtained from of the blockchain test using Hyperledger Besu provide essential insights into the system's performance with several bandwidth that used in the system. Average time for each test trial ranged from 1.066 seconds to 2.006 seconds, showing slight variations based on the bandwidth used. However, transactions per second (TPS) values were relatively fast, ranging from 1.066 tps to 0.499 tps with positive aspect of the retention rate for all trials was 100% success

Implementation of Oxymetry Sensors for Cardiovascular Load Monitoring When Physical Exercise

The performance condition of an athlete must always be maintained, one way to maintain that performance is by training. Each individual has different abilities and physiological responses in receiving the portion of the exercise. Physical exercise that exceeds the body's ability can worsen the condition of the athlete itself which can result in excessive fatigue (overtraining) or can even result in injury. Therefore a system is needed to monitor the condition of the physiological response when given the intensity of the training load so that the portion of the training provided provides positive benefits for the athlete. This system was developed using an oxymetry sensor, microcontroller and wifi module ESP8266.  This system is used to collect heart rate and oxygen saturation data, then with the existing formula the heart rate value is converted to a CVL (Cardiovascular Load) value to determine the level of fatigue in athletes when given the intensity of the training load. By using a web-based application, measurement data is displayed in realtime to make it easier to see the results of monitoring. From the experimental results the system can monitor changes in the physiological condition of the athlete when given the intensity of the training load. Finally, the developed system can collect athlete's physiological data, and can store the data in a database and display it in a web application

Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System

Efforts to prevent the spread of the COVID-19 virus have underscored the critical importance of mask-wearing as a preventive measure. Concurrently, road traffic accidents, often resulting from human error, have emerged as a significant contributor to global mortality rates. This study endeavors to address these pressing issues by employing advanced Deep Learning techniques to detect mask usage and identify drowsy eyes, thus contributing to the prevention of COVID-19 and accidents due to driver fatigue. To achieve this objective, an embedded system was developed, utilizing the integration of hardware and software components. The system effectively utilizes MobileNetV2 for face mask detection and employs HOG and SVM algorithms for drowsy eye detection. By seamlessly integrating these detection systems into a single embedded device, the simultaneous detection of both mask usage and drowsy eyes is made possible. The results demonstrates a commendable accuracy rate of 80% for face mask detection and 75% for drowsy eye detection. Furthermore, the mask detection component exhibits a remarkable training accuracy of 99%, while the drowsy eye detection component demonstrates an 80% training accuracy, affirming the system's efficacy in precisely identifying masks and drowsy eyes. The proposed embedded system offers potential applications in enhancing road safety. Its capability to effectively detect drowsy eyes and mask usage in car drivers contributes significantly to preventing accidents due to driver fatigue. Additionally, it plays a vital role in mitigating COVID-19 transmission by promoting widespread mask-wearing among individuals. This study exemplifies the potential of integrating Deep Learning methodologies with embedded systems, thus paving the way for future research and development in the realm of driver safety and virus prevention

Performance of Implementation IBR-DTN and Batman-Adv Routing Protocol in Wireless Mesh Networks

Wireless mesh networks is a network which has high mobility and flexibility network. In Wireless mesh networks nodes are free to move and able to automatically build a network connection with other nodes. High mobility, heterogeneous condition and intermittent network connectivity cause data packets drop during wireless communication and it becomes a problem in the wireless mesh networks. This condition can happen because wireless mesh networks use connectionless networking type such as IP protocol which it is not tolerant to delay. To solve this condition it is needed a technology to keep data packets when the network is disconnect. Delay tolerant technology is a technology that provides store and forward mechanism and it can prevent packet data dropping during communication. In our research, we proposed a test bed wireless mesh networks implementation by using proactive routing protocol and combining with delay tolerant technology. We used Batman-adv routing protocol and IBR-DTN on our research. We measured some particular performance aspect of networking such as packet loss, delay, and throughput of the network. We identified that delay tolerant could keep packet data from dropping better than current wireless mesh networks in the intermittent network condition. We also proved that IBR-DTN and Batman-adv could run together on the wireless mesh networks. In The experiment throughput test result of IBR-DTN was higher than Current TCP on the LoS (Line of Side) and on environment with obstacle.Keywords: Delay Tolerant, IBR-DTN, Wireless Mesh, Batman-adv, Performanc

Aplikasi DIY Smart Trash berbasis IoT Open Platform

Sampah merupakan hal yang tidak asing lagi bagi manusia saat ini. Bagi orang-orang yang tidak bertanggung jawab akan berfikir bahwa sampah dapat dibuang dimana saja, bahkan ada yang meletakkan sampah dengan mudahnya sambil meninggalkan sampah tersebut secara sembarangan tanpa berpikir bahwa ada tempat sampah yang layak dan sesuai dengan peruntukannya. Ketidaksadaran dalam membuang sampah secara sembarangan inilah yang dapat merusak pemandangan di area sekitar tempat itu. Saat ini, sangat banyak produk-produk sampah yang pintar di pasaran dalam memecahkan permasalahan sampah bahkan memudahkan pemiliknya. Namun, produknya sangat mahal dan biasanya setiap orang sudah menyediakan tempat sampahnya sendiri dirumah. Oleh karena itu, kami mengusulkan sebuah aplikasi DIY (Do It Yourself) Smart Trash berbasis IoT (Internet of Things) Open Platform. Aplikasi DIY Smart Trash dengan IoT Open Platform ini terdiri dari platform terbuka yang menyediakan monitoring web dan visualisasi interaktif yang ditampilkan dalam dashboard, layanan MQTT (MQ Telemetry Transport), Kafka, database untuk menerima data dan push notification untuk dapat memberitahukan kepada pengguna. Kami juga mnggunakan sistem embedded dan menyediakan source code untuk Arduino, Redbear, Raspberry Pi3 dan Raspberry Pi Zero W untuk membangun DIY Smart Trash. Dengan menggunakan aplikasi DIY Smart Trash, secara tidak langsung dapat mengurangi biaya manajemen sampah di setiap rumah. Selain itu dari segi harga, DIY Smart Trash memiliki harga yang lebih terjangkau daripada yang lain