Campus Monitoring Research Articles

Self-consuming solar-powered campus monitoring drone

Drones are utilised for a variety of purposes and are a regular sight these days. Selfies, insecticide application, and military monitoring. The issue with surveillance and monitoring is that a lot of applications call for continuous surveillance. Although they have a major disadvantage, drones do offer a good view for surveillance and monitoring. This is the battery life of the drone. The main worry a drone operator has when operating a surveillance drone is that the battery might die and the drone might crash into a building, tree, or other unreachable place from which it cannot be recovered and hence cannot be charged. This is also true for drones used in military surveillance; the risk of a drone's battery dying and becoming unusable places restrictions on drone operators while they are conducting surveillance or monitoring. Here, we've developed a drone that solves these issues by using solar power to continuously charge the device, extending its flying time. It can also land anywhere and remotely recharge its battery so it can take off later. The development and deployment of a solar-powered, self-charging campus surveillance drone are suggested in this research. By offering continual monitoring capabilities without requiring manual charge, the drone seeks to answer the demand for increased security measures on campus. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. This maximizes security coverage while minimizing downtime and guarantees continuous operation. Drone capabilities are further enhanced by the incorporation of cutting-edge surveillance technology, which enable detection of threats and constant surveillance. With possible applications in educational institutions, corporate campuses, and large-scale facilities, the suggested method provides an effective and long-lasting answer to campus security concerns.

A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring

The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework’s superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments.

IoT-based Smart Campus Monitoring Based on an Improved Chimp Optimization-Based Deep Belief Neural Network

IoT-based Smart Campus Monitoring Based on an Improved Chimp Optimization-Based Deep Belief Neural Network

1874. Comprehensive, longitudinal wastewater surveillance for SARS-CoV-2 across a university campus, demonstrates low levels of SARS-CoV-2 activity relative to the surrounding community

Abstract Background Universities are interactive communities where frequent contacts between individuals occur, increasing the risk of outbreaks of COVID-19. We embarked upon a real-time wastewater (WW) monitoring program across the University of Calgary (UofC) campus measuring WW SARS-CoV-2 burden relative to levels of disease in the broader surrounding community. Figure 1 The colour scheme shows 6 sewer sub-catchments at the University of Calgary. Autosamplers were deployed at 4 sampling nodes within sub-catchments CR and YA (both residence halls), and UCE and UCS (catchments that include several campus buildings). Figure 2 Log10-transformed abundance (i.e., copies per mL) of nucleocapsid gene (i.e., N1) for SARS-CoV-2 for each sampling location during October 2021 – April 2022. Locations denoted by the same letters (A, B, or C) show no statistical difference (p > 0.05) according to the Wilcoxon rank-sum test. The WWTP sample corresponds to a catchment area covering most of Calgary including the university campus, for which sampling locations CR, UCE, UCS, and UCW are defined in Fig. 1. Methods From October 2021 – April 2022, WW was collected thrice weekly across UofC campus through 4 individual sewer sampling nodes (Fig. 1) using autosamplers (C.E.C. Analytics, CA). Results from these 4 nodes were compared with community monitoring at Calgary’s largest WW treatment plant (WWTP), which received WW from surrounding neighborhoods, and also from UofC. Nucleic acid was extracted from WW for RTqPCR quantification of the N1 nucleocapside gene from SARS-CoV-2 genomic RNA. Qualitative (positive samples defined if cycle threshold < 40) and quantitative statistical analyses were performed using R. Results Levels of SARS-CoV-2 in WW were significantly lower at all campus monitoring sites relative to the WWTP (Wilcoxon rank-sum test p < 0.05; Fig. 2). The proportion of WW samples that were positive for SARS-CoV-2 was significantly higher for WWTP than at least two campus locations (p < 0.05 for Crowsnest Hall and UCE - University way and campus drive) according to Fischer’s exact 2-sided test. The proportion of WW samples with positive WW signals were still higher for WWTP than the other two locations, but statistically not significant (p = 0.216). Among campus locations, the buildings in UCE catchment showed much lower N1 signals than other catchments, likely owing to buildings in this catchment primarily being administration and classroom environments, with lower human-to-human contact and less defecation compared to the other 3 catchments, which include residence hall, a dining area, and/or laboratory spaces. Conclusion Our results show that SARS-CoV-2 RNA shedding in WW at the UofC is significantly lower than the city-wide signal associated with surrounding neighborhoods. Furthermore, we demonstrate that WW testing at well-defined nodes is a sampling strategy for potentially locating specific places where high transmission of infectious disease occurs. Disclosures All Authors: No reported disclosures.

Smart Campus Evaluation Monitoring Model Using Rainbow Framework Evaluation and Higher Education Quality Assurance Approach

Education in the industrial era 4.0 experienced very significant changes. The role of information technology is a significant trend in education. Since the pandemic occurred at the beginning of 2020, almost all schools and even universities have indirectly demanded rapid changes in adapting to digitalization methods. It was also found that there were many obstacles during the pandemic in the implementation of information technology. The Indonesian state itself in education is still adapting and starting to develop with the existence of models of information technology approaches that can be implemented in academic activities, especially on campuses. Since 2010 it has been following the smart campus concept and model development. However, it was found to be varied due to the absence of a benchmark index in the campus's preparation, monitoring, and evaluation. Through the Rainbow Framework, this will be a solution in measuring the feasibility level through monitoring and evaluating the use of technology with a quality assurance approach. The results can be seen from the components that have been built using the calculation model for the quality of technology education in Indonesia.

Green Smart Campus Monitoring and Detection Using LoRa.

Along with the rapid development of sensing systems and wireless transmission technology, the scope of application of the IoT has substantially increased, and research and innovation that integrate artificial intelligence. This study integrated civil engineering and electrical engineering to establish a universal and modularized long-term sensing system. Aiming at positive construction in civil engineering, the campus of National Taipei University of Technology was used as the experimental site as a green campus. This paper focused on the cooling effect of the green roof and the temperature difference of the solar panel to effectively isolate the direct sunlight on the roof of the building. To achieve long-term monitoring, energy consumption must be minimized. Considering that the distance between sensor nodes in the experimental site was over dozens of feet, LoRa transmission technology was selected for data transmission. LoRa only consumes a small amount of energy during data transmission, and it can freely switch between work modes, achieving optimal power utilization efficiency. The greening-related research results indicated that the shade from solar panels on the rooftop could effectively reduce the temperature increase caused by direct sunlight on concrete surfaces. The temperature reduction effect was positively correlated with whether the solar panels provided shade. After 1 week of monitoring, we observed that having plants on the rooftop for greening negatively correlated with temperature reduction efficiency. Permeable pavement on the ground was positively correlated with temperature reduction efficiency. However, its temperature reduction efficiency was inferior to that of solar panel shading. The temperature difference between high-rise buildings and the ground was approximately 1–2 °C. At the same elevation, the temperature difference between buildings with and without greening was approximately 0.8 °C. Regarding the sensing system designed for this site, both hardware and software could be flexibly set according to the research purposes, precision requirements of the sites, and the measurement scope, thereby enabling their application in more fields.

Implementing building-level SARS-CoV-2 wastewater surveillance on a university campus

The COVID-19 pandemic has been a source of ongoing challenges and presents an increased risk of illness in group environments, including jails, long-term care facilities, schools, and residential college campuses. Early reports that the SARS-CoV-2 virus was detectable in wastewater in advance of confirmed cases sparked widespread interest in wastewater-based epidemiology (WBE) as a tool for mitigation of COVID-19 outbreaks. One hypothesis was that wastewater surveillance might provide a cost-effective alternative to other more expensive approaches such as pooled and random testing of groups. In this paper, we report the outcomes of a wastewater surveillance pilot program at the University of North Carolina at Charlotte, a large urban university with a substantial population of students living in on-campus dormitories. Surveillance was conducted at the building level on a thrice-weekly schedule throughout the university's fall residential semester. In multiple cases, wastewater surveillance enabled the identification of asymptomatic COVID-19 cases that were not detected by other components of the campus monitoring program, which also included in-house contact tracing, symptomatic testing, scheduled testing of student athletes, and daily symptom reporting. In the context of all cluster events reported to the University community during the fall semester, wastewater-based testing events resulted in the identification of smaller clusters than were reported in other types of cluster events. Wastewater surveillance was able to detect single asymptomatic individuals in dorms with resident populations of 150–200. While the strategy described was developed for COVID-19, it is likely to be applicable to mitigation of future pandemics in universities and other group-living environments.

Research on “Six in One” Campus Security System Based on Multi-node Data Feedback

In terms of the current incompatibility of various campus monitoring networks and the difficulty of timely sharing as well as the unified storage and management of heterogeneous data in heterogeneous networks, a security monitoring system for multi-node heterogeneous data is designed in the paper, in which the data communication format, sensor encapsulation protocol, gateway design and implementation, database design and monitoring platform construction are studied, realizing the collection and forwarding of heterogeneous network data. Moreover, the data exchange format uses a lightweight format to facilitate the transmission and storage of data, and the sensor protocol is encapsulated to solve the heterogeneous problem caused by the difference in data conversion formulas and meanings between different sensor networks. Furthermore, simulation experiments have proved that the system can efficiently and reliably collect, store and process heterogeneous data in a heterogeneous network in real time, and the built monitoring platform can centrally display and manage campus monitoring information in real time.

The Application of Visual Sensors in the Artistic Design of College Campus Landscape

The research purpose is to solve the problems of low efficiency, low accuracy, and high cost of traditional environmental landscape mapping and landscape volume measurement methods in the artistic design of college campus landscape and make up the defects that the traditional campus monitoring is vulnerable to adverse weather, which results in low monitoring accuracy. Primarily, a binocular stereo vision measurement based on Scale Invariant Feature Transform (SIFT) matching algorithm is proposed, which can realize accurate collection of environmental spatial information and measurement of landscape volume without contact in the process of campus landscape design. Then, the visual monitoring system of college landscape based on the Internet of Things (IoT) is constructed to realize real‐time monitoring and early warning of human damage to campus landscape. The proposed method is verified by actual measurement of different objects and simulation experiments using simulation software. Ultimately, the application of visual sensors in artistic design of college campus landscape is analysed by literature analysis. The results show that (1) the error of the improved binocular stereo vision measurement designed here is 52.32% and 59.69% lower than that of the traditional measurement method when measuring the same object with different volumes and the volumes of different objects, respectively, which indicates that the measurement accuracy of the new method is higher. (2) The proposed landscape visual monitoring method based on IoT improves the image recognition accuracy by 21% compared with the traditional digital image monitoring method. The average recognition time is shortened by 12 ms, which ensures the accuracy and improves the recognition efficiency. (3) Through the analysis of existing literature, it is found that the binocular stereo vision sensor can be used to monitor the whole process of landscape construction in real time. The sensor can be combined with social networks, mobile terminals, and physiological monitoring equipment to comprehensively analyse and evaluate people’s preference for campus landscape. The proposed method has broad application prospects in campus landscape design, construction, and maintenance. The research purpose is to provide important technical support for the improvement of the overall image of the college campus and even the city for the design of landscape environment and the technical upgrading of maintenance work in the college campus.

UAV Remote Sensing for Campus Monitoring: A Comparative Evaluation of Nearest Neighbor and Rule-Based Classification

UAV technology when aided with the unique data acquisition strategies, preprocessing techniques and analytical abilities of an established domain of remote sensing provide more affordable, customized and user-friendly option of “UAV-Remote Sensing”. This extended branch of remote sensing flourishes in both the mapping and measurement, if implemented in the ordered fashion to ensure remote sensing grade data. The current study integrates the potential of UAV technology to the high-resolution data classification approach of object-based image analysis. Department of Civil Engineering, Indian Institute of Technology-Roorkee, India, is selected as study area. In the first part of the study, a detailed UAV survey followed by UAV data processing was carried out to capture the VHR orthorectified image of the selected study area. In the second step, a comparative assessment of nearest neighbor (NN) and rule-based classifications were performed. Orthorectified image was segmented using a multi-resolution segmentation. The overall accuracy for NN and rule-based classifier were 95.13% and 93.87%, respectively. Detailed assessment of user accuracy and producer accuracy described that tree, road, solar panel and waterbody were more accurately classified with NN classifier, whereas building, grass land, open land and vehicle were more accurately classified with rule-based classifier.

Stable Flight Mode for Quadcopter Drone for Campus Monitoring and Surveillance

Stable Flight Mode for Quadcopter Drone for Campus Monitoring and Surveillance

Campus Intelligence Mental Health Searching System Based on Face Recognition Technology

The phenomenon of teenage campus suicide has become the focus of attention of parents, schools and the society. The causes behind it are extremely complicated, and the root cause is psychological and spiritual problems. However, one's negative psychology is often hidden, and it is difficult to detect and effectively intervene before the tragedy. How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government, academia and the public. Through investigation and research, it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction, and put into use in schools as a mental health auxiliary diagnosis and treatment system, thus effectively reducing the suicide rate on campus. The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions. Based on this, this paper proposes an effective solution for campus suicide prediction, and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis. Through preliminary experimental analysis and verification, the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate, emotion recognition success rate and behavior prediction success rate. With the input of more experimental data and the increase of self-training time, the prediction system will perform better.

Campus Intelligence Mental Health Searching System Based on Face Recognition Technology

The phenomenon of teenage campus suicide has become the focus of attention of parents, schools and the society. The causes behind it are extremely complicated, and the root cause is psychological and spiritual problems. However, one's negative psychology is often hidden, and it is difficult to detect and effectively intervene before the tragedy. How to effectively identify students with suicidal tendencies in order to prevent tragedies has aroused extensive research and discussion among the government, academia and the public. Through investigation and research, it is found that the current popular computer cutting-edge technologies such as artificial intelligence and computer vision can be well used for human emotion recognition and behavior prediction, and put into use in schools as a mental health auxiliary diagnosis and treatment system, thus effectively reducing the suicide rate on campus. The scenario assumes that machine learning can be used to deduce the risk of psychological problems in human samples by analyzing the frequency of negative emotions in facial expressions. Based on this, this paper proposes an effective solution for campus suicide prediction, and designs a set of auxiliary diagnosis and treatment system based on campus monitoring network system for suicide behavior prediction and student mental health analysis. Through preliminary experimental analysis and verification, the suicide psychological auxiliary diagnosis and treatment system has achieved good results in face recognition success rate, emotion recognition success rate and behavior prediction success rate. With the input of more experimental data and the increase of self-training time, the prediction system will perform better.

Analisa Pembayaran Perkuliahan Dengan Virtual Account Pada Universitas Raharja

In the academic world, every educational institution must have a tuition payment system, and with the rapid technological advances nowadays, educational institutions are required to create systems that are effective and efficient and relevant to current technology which will make it superior in competition within the scope of relevant agencies. At Raharja University, lecture payments still use a conventional system where transfer students and campus staff check which is considered less efficient. In this study, the writer uses a SWOT analysis between the virtual account payment system and the running system. The purpose of this study is to recommend the use of a Virtual Account in the payment of lectures at Raharja University. This research is expected to be a problem solving alternative that makes it easier for students to make financial payments, as well as facilitate campus monitoring of campus payments.

Research on Intelligent Campus Monitoring Management System Based on Deep Neural Network Algorithm

With the development of the economy and the advancement of science and technology, the informationization of education management has become an inevitable trend, and the construction of a intelligent campus cannot be delayed. In order to comply with the current social development, major universities have invested heavily in IoT equipment to improve the management level of the school. Based on the large number of video cameras installed in colleges and universities, this paper uses the monitoring image big data and the CNN algorithm to train a set of graphical features that are easy to manage, without adding additional hardware purchases. In addition, combined with the camera position and other information, an intelligent campus monitoring system is built to help the campus administrator to understand the people flow, traffic flow and physical condition in real time, which is convenient for enhancing the effective monitoring and management of campus crowds, environment and security.

Reduced Methane Emissions from Santa Barbara Marine Seeps

Airborne in situ and remote sensing measurements of methane were performed over the marine seeps in the Santa Barbara Channel close to the Coal Oil Point in California on two days in June and August 2014 with the aim to re-assess their methane emissions. During this period, methane column averaged dry air mole fractions derived from airborne remote sensing measurements in the short-wave infrared and airborne in situ measurements of methane indicate that emissions are 2–6 kt CH 4 y − 1 , significantly lower than expected from previous publications. This is also confirmed by the on ground in situ measurement time series recorded at the onshore West Campus Monitoring Station in Santa Barbara. Using a time series of methane data, a decline in methane concentrations between 2008 and 2015 of more than a factor of two was derived for air masses originating from the seep field direction.

Sinkholes and a Disappearing Lake: Victory Lake Case Study

Human-induced sinkhole collapse can result in drastic changes to landscape aesthetics and present challenges to land managers seeking to determine the plausibility of restoration, the amount of financial investment needed, and the long-term sustainability of tampering with karstic environments. Alteration of groundwater flow in a karstic environment expedited the formation of large sinkholes in the southern end of man-made Victory Lake, causing it to drain immediately. Soon after the lake emptied in 1986, two unsuccessful attempts were made to restore the 13 ha (32 ac) lake. The sinkholes formed in the southern basin were completely in-filled, eliminating 3 ha (8 ac) of lake basin and significantly altering the original lake morphology. Some twenty-seven years later, Victory Lake is holding some water in the shallow basin at the northern end and would primarily be classified as a marshy wetland. This study was initiated to investigate the current relationship between the groundwater and surface water at the lake's altered basin to determine the potential for it to be restored fully or partially as a recreational focal point of the Berry College campus. Over the course of one year we measured the inputs (stream flow and precipitation) and outputs (evaporation and surface water outflow) of the lake system. We were able to conclude that groundwater is not likely contributing to the lake, based on inorganic and stable isotope ( 18 O and 2 H) water chemistry analysis and the deep position of the groundwater table relative to the lake bottom. From the results of dye-tracer tests conducted in the lake, we concluded that basin water may not be escaping downward at measurable rates because of its clay bottom. Our overall water-budget analysis confirms an adequate water volume entering by rainfall and ephemeral stream inflow; nearly 90% of the water leaves Victory Lake through surface-water outflow. Water loss through evapotranspiration during spring and summer months overcomes the gain accomplished during wetter and cooler months, particularly February. Through an investigation of water level records kept for campus monitoring wells from 1998 through 2012 we confirm that the groundwater table has stabilized and the immediate threat of new sinkhole formation is minimal. Restoration of Victory Lake to its original picturesque meeting spot may be possible through creative engineering strategies and project financing. However, we question the longevity of managing a karstic environment and consider the potential risks to infrastructure, groundwater, and human health should lake bottom failure occur again on campus.

Intelligent Campus Monitoring Platform Design Based on Internet of Things

Based on the Internet of things, this paper brings forwards the general design framework of the system construction and analyzes its functions. We make the construction figure with the media of RFID, sensor and two-dimensional bar code, propose four aspects to construct intelligent campus mobile service applications and make functional planning to mobile educational administration, mobile library, mobile office, mobile admissions and five mobile service centers.

A Novel Approach to Design the Fast Pedestrian Detection for Video Surveillance System

The pedestrian detection is a hot research topic in computer recognition. It involves not only the pedestrian location information but also the intrusion detection function, which has wide prospects in the application of vehicle traffic, campus monitoring, and building guard. However, the identification accuracy and recognition speed play an important role in the pedestrian detection, which calls for a fast pedestrian detection approach. The general pedestrian detection implementation, based on the integral channel features method and soft cascade classifier, is the popular technique in the current business application since its better speed and accuracy. Thus, this method uses the feature approximation technique and multiple classifiers to achieve the feature computing, which speeds up the detection without resizing image. To this end, this paper is motived to propose a multi-scale handling method for the fast pedestrian detection, using the tactics detection from sparse to dense. Our pedestrian detection method consists of four parts functions, mainly pedestrian statistics and intrusion detection, pedestrian tracking and pedestrian flow statistics. All these modules are introduced with its details about design and implementation. In Addition, the proposed multi-scale handling method can be applied into most of object detectors to improve their recognition speed. In conclusion, our proposed approach has a good potential application prospect in the video surveillance system.

Power Management and Charging Method for the Battery-Set of the Intelligent Servant Robot

This paper studies and implements “high efficiency power management system and intelligent battery-set charger system” for “the Intelligent Servant Robot”. This power management system provides high quality and efficiency electric power supply for all subsystems of the servant robot and accurately estimates the residual capacity of battery set system of the servant robot. This servant robot will be charged by the intelligent battery-set charger when the residual capacity of battery sets is insufficient. This servant robot can take the elevator, follow the user, carry articles, and provide the service of campus security patrolling and monitoring. LiFePO4 batteries are chosen as main power supply of the servant robot. The LiFePO4 battery-set system is divided to two subsystems which are the power battery-set (36V/20AH) for motors and the system battery-set (48V/10AH) for PC and MCUs, respectively. High efficiency power management system can measure the basic information (voltage, temperature, and current), provide protection, and give alarm for battery-set system. For the power converter module design of the intelligent battery-set charger, the main structure will use the boost power factor corrector to cascade the full-bridge power converter. The former mainly offers the power input of the high power factor, and the latter adopts the soft-switching and synchronous rectification technology in order to improve its electric conversion efficiency. For the construction of two-stage controllers, the single digital microchip controller is adopted to avoid the interference of high frequency switching of traditional power structure which uses two microchips. The experiment results have demonstrated: 1. the battery-set system can provide over two hour operation of the servant robot continuously. 2. the power management system can accurately estimates the residual capacity of battery set system. 3. the lithium-ion battery protection circuits can cut off the power apply and give the warnings when the battery set is operating under abnormal status. The battery charger allows 110V or 220V input voltage and provides the biggest 20A current to charge battery set. The power battery set can charge up to 90% capacity within one hour.