mobile platform
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Background: With the increasing demand of mobile robots in warehousing, logistics and service fields, simple planar motion is difficult to meet the task requirements of complex environment. The combination of mobile robot and cooperative robot is helpful to improve the dexterity of robot movement and expand the application of robots. Methods: Aiming at the application requirements of dual-arm robots and mobile robots in practical applications, this paper designed the hardware of a platform, built a simulation platform based on ROS (Robot Operating System), and designed the actual software control framework. Finally, the feasibility of the platform design was verified by the coupling motion experiment of the two robots. Results:  We have established a simulation of the dual-arm mobile platform in ROS, designed the actual software control framework, and verified the feasibility of the platform design through experiments. Conclusions:  The mobile platform can meet a variety of application requirements and lay the foundation for subsequent development.

Abstract The paper discusses the generalized simultaneous localization and mapping problem statement from the standpoint of the Bayesian approach and its relationship with algorithms for different map representations. The two-dimensional example describes the linearized simultaneous localization and mapping algorithm for the mobile platform in two-dimensional space.

Because the traditional way of sign in is not to call the roll, or sign in on the paper list, or sign in through fingerprint recognition and face recognition, on the one hand, it is cumbersome and time-consuming, on the other hand, it is non-human. The update of roll call sign in is a hot topic at present, and many new schemes rush out. This paper introduces the automatic sign in system based on Bluetooth technology, which does not rely on the mobile phone operating system, does not need the mobile phone installation software, as long as you bring a mobile phone, you can realize automatic sign in. This paper designs and implements the automatic check-in system, and compares it with other ways, proving that it is an innovative check-in method.

As a result of the analytical review, it was established that the family of Yolo models is a promising area of search and recognition of objects. However, existing implementations do not support the ability to run the model on the iOS platform. To achieve these goals, a comprehensive scalable conversion system has been developed to improve the recognition accuracy of arbitrary models based on the Docker system. The method of improvement is to add a layer with the Mish activation function to the original model. The method of conversion is to quickly convert any Yolo model to CoreML format. As part of the study of these techniques, a model of the neural network Yolov4_TCAR was created. Additionally, a method of accelerating the load on the CPU using an additional layer of neural network with the function of activating Mish in Swift for the iOS mobile platform was added. As a result, the effectiveness of the Mish activation function, the CPU load of the mobile device, the amount of RAM used, and the frame rate when using the improved original Yolov4-TCAR model were studied. The results of the research confirmed the functioning of the algorithm for conversion and accuracy increase of the neural network model in real-time.

Peat fires cause major environmental problems in Central Kalimantan Province, Indonesia and threaten human health and effect the social-economic sector. The lack of peat fire detection systems is one factor that causing these reoccurring fires. Therefore, in this study, we develop an Android mobile platform application and a web-based application to support the citizen-volunteers who want to contribute wildfires reports, and the decision-makers who wish to collect, visualize, and evaluate these wildfires reports. In this paper, the global navigation satellite system (GNSS) and a global position system (GPS) sensor from a smartphone’s camera, is a useful tool to show the potential fire and smoke’s close-range location. The exchangeable image (EXIF) file image and GPS metadata captured by a mobile phone can store and supply raw observation to our devices and sent it to the data center through global internet communication. This work’s results are the proposed application easy-to-use to monitoring potential peat fire by location and data activity. This paper focuses on developing an application for the mobile platform for peat fire reporting and a web-based application to collect peat fire location for decision-makers. Our main objective is to detect the potential and spread of fire in peatlands as early as possible by utilizing community reports using smartphones.

Proses diagnosa penyakit pada tanaman daun jeruk umunya dilakukan melalui pemeriksaan laboratorium oleh seorang ahli patologi tumbuhan dengan melihat gejala visual yang timbul pada tanaman untuk membantu petani daun jeruk dalam memberikan pola penanganan yang tepat berdasarkan gejala yang nampak pada kondisi daun jeruk. Klasifikasi penyakit daun jeruk menggunakan metode pengolahan citra mampu memberikan referensi diagnosis yang cepat dan akurat. Penelitian ini mengusulkan pendekatan deep learning menggunakan arsitektur MobineNet CNN untuk melakukan klasifikasi. Metode pada penelitian ini dievaluasi pada citra penyakit daun jeruk dalam tiga kategori yaitu normal, HLB dan CTV dengan ukuran citra 150x150. Pengujian dilakukan dengan menggunakan algortima RMSprof optimize dengan learning rate 0.001. Proses pelatihan  menggunakan arsitektur Binary Cross Entropy fungsi aktivasi sigmoid. Hasi klasifikasi penyakit pada citra daun jeruk pada proses training mencapai tingkat akurasi 98% pada epoch 15.