Unmanned ground vehicle system to collect soil moisture data

With an increased interest in precision agriculture, it is important to identify efficient ways to monitor soil moisture. Soil moisture can be monitored using handheld sensors, but this method is laborious and time consuming. Remote methods, such as radar systems can be used as well, but these methods require ground truth data to verify their accuracy. It becomes clear that to collect this data regularly and reliably, a mobile robotic device is necessary. This thesis proposes to implement mobile robot take soil moisture measurements with less human effort than existing methods while maintaining the same accuracy. This soil moisture data collection system uses an unmanned ground vehicle (UGV) to take measurements with position data. This system uses an actuator inserted soil moisture probe, and a radio frequency identification (RFID) sensing system that uses buried moisture sensing tags. Field testing of both measurement systems showed that the actuator-based system worked reliably

Tablet-Based Mobile GIS Approaches to Archaeological Data Collection

Over the past 15 years, archaeological research in northern Armenia has documented the unique evolution of prehistoric complex societies in the South Caucasus, where complex, fortress-centered institutions emerged during the Late Bronze Age (c.1500-1150 BC) not from settled farming villages—as is more typical of archaic states—but from mobile herding communities. As the costs of archaeological fieldwork continue to rise, resulting in shorter and more intensive field seasons, researchers are leveraging new technologies to improve the efficiency and accuracy of data collection in the field. An increasingly popular solution in archaeology is the use of “paperless” site recording strategies that enhance the quality and effiency of data collection by limiting the potential for human error in transfering data from paper forms into a digital format; allowing project leaders to track the accuracy of data entry in real time and correct potential problems in the field; and tracking the transect lines of crew members to ensure greater scientific accuracy in sampling a survey area. This talk will focus on the cenceptual and technical aspects of a new mobile GIS system, developed in collaboration with Dr. Nicole Kong, that allows crew members to record sites directly into iPads with ESRI’s Collector for ArcGIS app and remotely linked via cellular connection to the project geodatabase hosted on a Purdue Library server. I will highlight the benefits and challenges of the new system encountered during a summer 2014 pilot survey in Armenia in anticipation of a full-scale survey planned for summer 2015

IoT Based Industrial Production Monitoring System Using Wireless Sensor Networks

The objective of the work is to monitoring the production lines in industry using wireless sensor networks. This work presents the benefits of an automated data collection and display system for production lines. It involves wireless sensor networks for monitoring the productions in industry. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate and improves safety and reliability. This work can monitor productions using temperature, voltage and current sensors with support of microcontroller. The relay is acts like a switch to monitor the production lines. In this work, Global System for Mobile communication technique is used to transferring the collected data. The collection of data, it is transferred into computerize spreadsheet in the remote office by authorized personnel for reporting purpose. The system will generate an automated report which stays in place and the management only needs to act base on the results. This work is cost effective automatic data collection is the alternative to manual data collection. It significantly improves the accuracy of the valuable reports for the management. It also reduces the time for identifying the fault using this techniqu

Master of Science

thesisThe 2012 Great Utah Shakeout highlighted the necessity for increased coordination in the collection and sharing of spatial data related to disaster response during an event. Multiple agencies must quickly relay scientific and damage observations between teams in the field and command centers. Spatial Data Infrastructure (SDI) is a framework that directly supports information discovery and access and use of the data in decision making processes. An SDI contains five core components: policies, access networks, data handling facilities, standards, and human resources needed for the effective collection, management, access, delivery, and utilization of spatial data for a specific area. Implementation of an SDI will increase communication between agencies, field-based reconnaissance teams, first responders, and individuals in the event of a disaster. The increasing popularity of location-based mobile social networks has led to spatial data from these sources being used in the context of managing disaster response and recovery. Spatial data acquired from social networks, or Volunteer Geographic Information (VGI), could potentially contribute thousands of low-cost observations to aid in damage assessment and recovery efforts that may otherwise be unreported. The objective of this research is to design and develop an SDI to allow the incorporation of VGI, professional Geographic Information System (GIS) layers, a mobile application, and scientific reports to aid in the disaster management process. A secondary goal is to assess the utility of the resulting SDI. The end result of combining the three systems (e.g., SDE, a mobile application, and VGI), along with the network of relevant users, is an SDI that improves the volume, quality, currency, accuracy, and access to vital spatial and scientific information following a hazard event

Effect of mobile technology on information quality in human-mediated information collection

Acknowledging the paramount importance of information quality in the modern data-driven society and the prevalence of mobile devices, this thesis assesses the effect the use of mobile devices in human-mediated information collection has on information quality. More specifically, the thesis delves into a specific use of mobile devices as information collection devices in environments of high mobility, which do not permit use of less compact and accessory dependent computer technology. The implications of mobile device use in information collection are two-fold; on one hand the devices' radical mobility could enable supported information collection directly at the source, potentially improving information quality, but on the other hand the small screen with cumbersome text input via on-screen keyboard could entail a number of unintended errors as well as narrower range of recorded information. The thesis assesses two sets of patient notes from a Finnish residential elderly care facility, one produced solely with desktop computers and the other with desktop computers and mobile devices in parallel. On the intrinsic information quality dimensions of accuracy, completeness, timeliness, and consistency, the complementary use of mobile device results in enhanced information quality; particularly the completeness and timeliness of information show significant improvement. Additionally, anticipated difficulties in data input with mobile devices do not seem to have any remarkable effects on the produced information

Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal

Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interface. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains (i.e. environments, persons and locations) and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive the signal variation corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework based on the correlation between signal patterns and gesture variations. Furthermore, we propose a dynamic window mechanism to perform gesture segmentation automatically and accurately, thus enable real-time recognition. Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments and locations, respectively. In real-time scenario, the accuracy of DI-Gesutre reaches over 97% with average inference time of 2.87ms, which demonstrates the superior robustness and effectiveness of our system.Comment: The paper is submitted to the journal of IEEE Transactions on Mobile Computing. And it is still under revie