Design of Distance Education System for Ideological and Political Courses of Electronic Engineering Specialty Based on Big Data

According to the existing literature, the existing engineering ideological and political curriculum distance education system has the problems of long response time and few concurrent users, which cannot meet the development needs of the professional talents of electronic engineering. Therefore, the design and research of the long-distance education system of the ideological and political course of electronic engineering specialty dependent on big data is proposed. Based on the analysis of the demand of distance education system, the overall structure of the distance education system is designed. The design of the system business functions mainly includes login verification module design, instant communication module design, online classroom module design, video answer module design and video back-view module design, and design the server cluster load balancing strategy according to big data technology, so as to realize the operation of the remote education system of the ideological and political courses of electronic engineering. The design system is shorter than response time while compared with existing system. and the number of users is more. It is fully proved that the design system has better application performance.

Smart Aero-Spider: An adaptive and selective smart switching aerator system

The recovery from the COVID-19 pandemic hit shows the emergence of increase in quality of life across various parts of the world. With this lifestyle change, people are looking towards high quality food. Fish being a major source of protein, the industry producing fish from aquaculture is booming. The proposed smart aerator system provides an integrated array of underwater systems for selective aeration of the water body. The smart system ensures targeted aeration to guarantee optimal levels of dissolved oxygen at all times. This is beneficial for perfect survival, growth, and reproduction of fishes. The strategically placed spider aerators are turned on when readings from the dissolved oxygen level at the location is below the optimum range of values. The air blower system consists of an intelligent switching system to activate the right aerator based on the requirement. The sensor data is relayed to the cloud with a wireless communication module. This data can be used for useful insights and all-round monitoring of the water body. The respective aerators have IR sensor to detect movement alongside on-board LEDs to indicate functioning status. Overall, this ensures maximum accelerated growth of healthy fishes. Thus, the solution aims at efficiently boosting the ability of the aquaculture industry to meet the ever-growing demand of consumers.

IoT Based Surveillance System Using DNN Model

Localization, Visibility, Proximity, Detection, Recognition has always been a challenge for surveillance system. These challenges can be felt in the industries where surveillance systems are used like armed forces, technical-agriculture and other such fields. Most of the Smart system available are just for the surveillance of Human intervention but there is a need for a system which can be used for animals as well because with the outburst of human population and symbiotic relationship with wild animals results in life loss and damage to agriculture. In this paper we are designing to overcome these above-mentioned challenges for human and animal-based surveillance system in real time application. The system setup is done on a Raspberry pi integrated with deep-learning models which performs the classification of objects on the frames, then the classified objects is given to a face detection model for further processing. The detected face is relayed to the back-end for feature mapping with the saved log files with containing features of familiar face IDs. Four models were tested for face detection out of which the DNN model performed the best giving an accuracy of 94.88%.The system is also able to send alerts to the admin if any threat is detected with the help of a communication module. Keywords: Deep learning, Raspberry Pi, OpenCV, Image Processing, YOLO, Face Recognition

Identifikasi kebutuhan sarana-prasarana pemantauan radiasi nirawak dalam pengawasan radiasi reaktor riset di Indonesia

The facility’s licensee conducts environmental radiation monitoring in nuclear facilities to monitor radiation exposure in the facility’s vicinity. This activity is carried out also to monitor radiation release as a result of nuclear reactor operation. Aside from that, monitoring also works as a device to monitor radioactive release in a nuclear emergency. Therefore, the radiation monitoring system is crucial in nuclear utilization facilities to determine the number of radiation exposure to the surrounding environment. However, the existing stationary monitoring system has a risk of being unable to work if the system is down in case of natural disaster occurs. One way to mitigate this risk is to deploy an unmanned radiation monitoring system to monitor radiation exposure without putting personnel at risk. To define a suitable unmanned radiation monitoring system, identification of facilities and infrastructure required to design an unmanned radiation monitoring system for a research reactor in Indonesia is carried out. Facilities and infrastructure needed for unmanned radiation monitoring systems are unmanned aerial vehicles, radiation detector, control and communication module, navigation system, and software for the control system. These required facilities and infrastructure are then specified to determine the necessary specification for monitoring research reactor in Indonesia. The facilities’ required specifications are unmanned aerial vehicles with rotary-wing type, CdZnTe Detector, and GPS/GLONASS based navigation system. For infrastructure specification, control and communication module and software for the control system is not specified in how the system could meet the expected required performance rather than in detail. However, the system must provide and process measurement data in real-time to be presented in a radiation heatmap. Keywords: Identification, Radiation Monitoring, Unmanned

HOTSPOT: An Ad Hoc Teamwork Platform for Mixed Human-Robot Teams

Ad hoc teamwork is a research topic in multi-agent systems whereby an agent (the "ad hoc agent") must successfully collaborate with a set of unknown agents (the "teammates") without any prior coordination or communication protocol. However, research in ad hoc teamwork is predominantly focused on agent-only teams, but not in agent-human teams, which we believe is an exciting research avenue and has enormous application potential in human-robot teams. This paper will tap into this potential by proposing HOTSPOT, the first framework for ad hoc teamwork in human-robot teams. Our framework comprises two main modules, addressing the two key challenges in the interaction between a robot acting as the ad hoc agent and human teammates. First, a <i>decision-theoretic module</i> that is responsible for all task-related decision making (task identification, teammate identification, and planning). Second, a <i>communication module</i> that uses natural language processing in order to parse all communication between the robot and the human. To evaluate our framework, we use a task where a mobile robot and a human cooperatively collect objects in an open space, illustrating the main features of our framework in a real-world task.

HOTSPOT: An Ad Hoc Teamwork Platform for Mixed Human-Robot Teams

Ad hoc teamwork is a research topic in multi-agent systems whereby an agent (the "ad hoc agent") must successfully collaborate with a set of unknown agents (the "teammates") without any prior coordination or communication protocol. However, research in ad hoc teamwork is predominantly focused on agent-only teams, but not in agent-human teams, which we believe is an exciting research avenue and has enormous application potential in human-robot teams. This paper will tap into this potential by proposing HOTSPOT, the first framework for ad hoc teamwork in human-robot teams. Our framework comprises two main modules, addressing the two key challenges in the interaction between a robot acting as the ad hoc agent and human teammates. First, a <i>decision-theoretic module</i> that is responsible for all task-related decision making (task identification, teammate identification, and planning). Second, a <i>communication module</i> that uses natural language processing in order to parse all communication between the robot and the human. To evaluate our framework, we use a task where a mobile robot and a human cooperatively collect objects in an open space, illustrating the main features of our framework in a real-world task.

Development of a Verbal Robot Hand Gesture Recognition System

This article analyzes the most famous sign languages, the correlation of sign languages, and also considers the development of a verbal robot hand gesture recognition system in relation to the Kazakh language. The proposed system contains a touch sensor, in which the contact of the electrical property of the user's skin is measured, which provides more accurate information for simulating and indicating the gestures of the robot hand. Within the framework of the system, the speed and accuracy of recognition of each gesture of the verbal robot are calculated. The average recognition accuracy was over 98%. The detection time was 3ms on a 1.9 GHz Jetson Nano processor, which is enough to create a robot showing natural language gestures. A complete fingerprint of the Kazakh sign language for a verbal robot is also proposed. To improve the quality of gesture recognition, a machine learning method was used. The operability of the developed technique for recognizing gestures by a verbal robot was tested, and on the basis of computational experiments, the effectiveness of algorithms and software for responding to a verbal robot to a voice command was evaluated based on automatic recognition of a multilingual human voice. Thus, we can assume that the authors have proposed an intelligent verbal complex implemented in Python with the CMUSphinx communication module and the PyOpenGL graphical command execution simulator. Robot manipulation module based on 3D modeling from ABB.

Unmanned Driving Infringement Judgment Based on Wireless Sensor Network Data Mining

Based on the wireless sensor network unmanned driving infringement identification system, this paper focuses on the application of data mining technology and state machine technology and designs and implements a set of practical and effective. Self-driving cars can reduce the frequency of traffic accidents, alleviate urban traffic congestion, improve people’s travel efficiency, and lower the threshold of driving and other social values. The data processing program and a number of algorithms are given, and a complete set of data processing procedures and algorithms are proposed, including the collection of raw sensor data, the preprocessing of the collected data, and the feature extraction of the processed data. In the experiment, the unmanned driving infringement monitoring network was first designed to conduct real-time monitoring of unmanned driving infringements during transportation and application. Aiming at the characteristics of unmanned driving infringements, a monitoring network platform was designed for remote control and large-scale monitoring. Secondly, according to the characteristics of the unmanned driving infringement monitoring sensor network, the unmanned driving infringement node monitoring terminal is designed. The monitoring terminal part mainly designs the sensor module, the wireless communication module, the display warning module power module, and the data mining processing module. The sensor modules, respectively, include temperature, humidity, and concentration sensors, and the communication mode in the communication module mainly adopts Wi-Fi. At the same time, the research is based on wireless sensor network, combined with data mining technology, puts forward a sensory data display system model based on data mining technology, and conducts an in-depth analysis of the sensory data display system model, including the logical level of the system, system architecture, and functional modules. Finally, it focuses on the specific application of data mining technology in environmental information analysis and prediction, uses JAVA programming and realizes a data analysis and display system based on wireless sensor network, and verifies the accuracy of the data mining algorithm. The experimental results analyze the application of data mining technology in the driverless infringement determination system and use a large number of unmanned driving infringements to analyze the determination rules, so as to realize the interaction between active people and driverless cars.

Pulse interference identification and suppression for OFDM signal

The new generation smart meter is the core node of information acquisition and flows in the power Internet of Things (IoT). Because it is powered by the low-voltage power line, various short-time pulse interference signals in the line can be easily coupled to the received signal of the communication module embedded in the meter, resulting in the degradation of communication performance. In view of this situation, an identification-suppression algorithm of the short-time pulse-interference signal is proposed. The core idea is to comprehensively judge the existence of the interference signal and its specific position in the time domain based on the energy ratio of adjacent OFDM symbols and the change of mean square error value between frequency domain amplitude signals. Based on this, an interference energy elimination scheme with an adaptive threshold is realized. Theoretical analysis and simulation results show that the scheme has low computational complexity, high accuracy of pulse interference identification and position judgment, and can effectively reduce the energy of pulse interference signal, and thus improving the decoding performance of OFDM while it receives signals.