Tourist Attractions Visitor Density Monitoring Platform

Tourism is one of the sectors that have been hit hard during the current Covid-19 pandemic. However, in 2021 the tourism sector is slowly rising marked by the number of tourist attractions that are starting to open to the public with the implementation of strict health protocols. However, in practice there are still many cases of violations of health protocols in various tourist attractions, ranging from the undisciplined use of masks, not keeping a distance (social distancing), and the absence of setting the ideal number of visitors during a pandemic like the current one. Managers of tourist attractions tend to ignore restrictions on the number of visitors due to the absence of an management system to run. There needs to be an application that can inform the condition of visitors to tourist attractions in real-time so that people have self-awareness of their personal health so that people can make early plans before traveling. In good governance, this application contributes to controlling the risk of Covid-19 transmission originating from crowded centers (tourist spots). The government can easily monitor the distribution of tourist visitor density in the area so that it can then be used as a basis for carrying out policies and handling in the field. This study proposes the development of an application platform (software and hardware) monitoring the density of visitors to tourist attractions using the concept of object detection based on image processing and deep learning. The integration of hardware with web-based software will provide information on the density of visitors to tourist attractions that are easily accessible to the public so that they are expected to contribute to the handling of the current Covid-19 pandemic.

A Composable Monitoring System for Heterogeneous Embedded Platforms

Advanced computations on embedded devices are nowadays a must in any application field. Often, to cope with such a need, embedded systems designers leverage on complex heterogeneous reconfigurable platforms that offer high performance, thanks to the possibility of specializing/customizing some computing elements on board, and are usually flexible enough to be optimized at runtime. In this context, monitoring the system has gained increasing interest. Ideally, monitoring systems should be non-intrusive, serve several purposes, and provide aggregated information about the behavior of the different system components. However, current literature is not close to such ideality: For example, existing monitoring systems lack in being applicable to modern heterogeneous platforms. This work presents a hardware monitoring system that is intended to be minimally invasive on system performance and resources, composable, and capable of providing to the user homogeneous observability and transparent access to the different components of a heterogeneous computing platform, so system metrics can be easily computed from the aggregation of the collected information. Building on a previous work, this article is primarily focused on the extension of an existing hardware monitoring system to cover also specialized coprocessing units, and the assessment is done on a Xilinx FPGA-based System on Programmable Chip. Different explorations are presented to explain the level of customizability of the proposed hardware monitoring system, the tradeoffs available to the user, and the benefits with respect to standard de facto monitoring support made available by the targeted FPGA vendor.

Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log

Nowadays, the need for wireless sensing applications is increasing. Along with the increased illegal cutting of logs in the forest, however, it requires the integration application to tackle the illegal logging and forest preservation. The wireless sensor network is a suitable network architecture for remotely monitoring or tracking applications in the environment. This paper proposed an integrated system that can identify and track the position of a moving cutting log. An Arduino Uno, Raspberry Pi 3 B+, sound sensor, accelerometer sensor, LoRa GPS HAT Shield, and Outdoor LoRa Gateway OLG01 performed the hardware monitoring and tracking of the proposed system. The network of STAR topology configuration between master and slaves is represented by the LoRa Network embedded with the sensors, as an architecture of the wireless sensor network. The system was examined the performance of the network and the tracking process. The result determined that the LoRa can detect and identify the occurrence of the illegal cutting of logs in real-time. Meanwhile, in terms of the tracking performance, a duration of 5–46 s was required to track the new position of the moving cutting log.

New Monitoring Program of Transformer Substation Foundation Settlement

In order to solve the problem of foundation settlement monitoring, this paper presents a monitoring program of substation foundation settlement, which is based on the distributed optical sensing technology. The composition of the hardware monitoring system and the wiring scheme of stress optical cables were described. The results of simulation experiment showed that the Brillouin optical time domain analysis could well meet the precision requirements of substation settlement monitoring. Moreover, the temperature reference optical fibers should be laid for temperature compensation. This paper can provide the basis for further prevention of substation foundation settlement.

MONITORING TARGET HEART RATE (THR) UNTUK OPTIMALISASI LATIHAN LARI BERBASIS INTERNET OF THINGS

Olahraga merupakan salah satu aktifitas rutinyang dilakukan oleh masyrakat, seperti olahraga yang melatih kardio atau denyut jantung. Kategori olahraga yang mudah dilakukan oleh masyarakat adalah olahraga lari, yang tidak membutuhkan teknik yang rumit dan tidak butuh biaya mahal untuk melakukannya. Aktifitas lari ini sangat mempengaruhi kondisi jantung, khususnya bagi pelari pemula dan atlit lari. Jika Olahraga ini, tidak dipantau dengan baik oleh Pelatih atau Personal Trainer, maka mengakibatkan kematian. Sistem Monitoring yang mengintegrasikan hardware dengan software diterapkan untuk memudahkan proses monitoring. Pantauan yang dilakukan untik nilai THR (Target Heart Rate) dengan menerapkan metode Karvonen. Integrasi hardware yang meliputi sensor Grove Finger ClipHeart Rate, Modul Transmisi Wireless (Modul ESP 8266) , dan Arduino. Performansi 0,6% untuk tingkat kesalahan sensor dan kemampuan transmisi data dengan paket loss < 1% untuk modul ESP . Hardware Monitoring tersebut terintegrasi baik dengan aplikasi monitoring lewat jaringan lokal dan akses web lewat jaringan internet.