Road is a transportation infrastructure that has a very important role in supporting the economic, social and culture as well as various other aspects of community life. Various community activities are affected by road conditions. Roads that are in good condition will provide comfort and facilitate economic activity in an area. However, in reality there are still many roads that are not in good condition, such as potholes. Various attempts have been made to detect potholes automatically, especially with the two-dimensional imaging method. In this paper, we propose real-time potholes detection using the Convolutional Neural Network (CNN) method based on the Edge Tensor Processing Unit (TPU) with the MobileNet SSD v2. Our system was implemented on Jetson Nano with several additional sensors such as a camera and GPS. The accuracy of the system is verified through experiments using the testbed.

This paper presents a low-cost and portable water quality monitoring system for a researcher, consumer and also drinking water distribution systems. This system consists of main controller unit based on Commercial off-the-shelf (COTS) single board computer and equipped with LCD touch screen to display data information, the central measurement unit is interfaced to the multi-parameter sensor array to collect data from environment and communication unit that bring connection from the sensor device to The Internet. The dimension of portable water monitoring system are 38cm × 12.8cm × 8cm, and the weight is 658gr. The device also supports for modularity. From experiment we measured that the reliability delay for processing data is 1 second and delay for communication is only 2 seconds with 3.1Mbps Internet connection.

The Internet of Things (IoT) has been extensively deployed for Smart Cities due to its ability to process many different and heterogeneous end systems. The IoT innovation encourages artificial intelligence applications to process data. In Smart City infrastructure, the road is a critical component of transportation infrastructure that supports the economic, social, and cultural things of community life and various aspects of community life. Road conditions affect a variety of community activities. Good roads enhance comfort and support local businesses. However, many roads remain in bad condition, such as potholes. Various methods have been attempted to identify potholes, especially the two-dimensional imaging method. This paper proposes the real-time Artificial Intelligence detection of potholes using the Convolutional Neural Network (CNN), which leverages the Edge Tensor Processing Unit (TPU) with the MobileNet SSD v2. The system was set up on a Jetson Nano with a few extras, including a camera and GPS, to support the IoT infrastructure. Evaluation for the model consists of device implementation, model evaluation, GPS position deviation, and on-road implementation. The effectiveness is confirmed through experiments using a system test-bed that generates ideal mAP off 0.22 and recall values.

Battery management systems are essential in electric vehicles and renewable energy applications, especially in terms of ensuring optimal battery health and performance and regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of the battery depend on the accuracy of the SOC parameter estimation. Moreover, systems that apply active balancing technology are able to move cells with high SOC data to cells with low SOC. Many methods have been developed, but their long execution time makes them less optimal when applied. High-speed SOC estimation is required in active balancing technology, in addition to high accuracy. Therefore, this study proposes the estimation of SOC parameters using a statistical and metaheuristic approach from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R² values compared with the polynomial and linear regression or even the machine learning-based method (recurrent neural network) for training data.

In smart city infrastructure, road management is a critical component of transportation infrastructure inspection to ensure a safe driving experience. The current method relies on a visual inspection or survey to monitor road conditions. Several studies have been widely developed to change visual surveys, such as ultrasonic detection systems, but the accuracy of the devices is limited due to environmental factors. LiDAR, on the other hand, provides a more accurate alternative by measuring distance. In this paper, we propose a low-cost infrared Time of Flight (ToF) sensor with a programmable Region of Interest (RoI) feature to adjust the pointing of the sensor. The RoI identifies road characteristics by setting a LiDAR array, including depth/height, width, and length information. The success of this system was tested through an experiment with an error rate of less than 20% for the width and depth measurement but 35% for the length measurement.