The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance/Quality Control (QA/QC) is necessary to ensure that the data collected is fit for purpose. Current automated QA/QC approaches provide assessments based upon hard classifications of the gathered data; often as a binary decision of good or bad data that fails to quantify our confidence in the data for use in different applications. We propose a novel framework for automated data quality assessments that uses Fuzzy Logic to provide a continuous scale of data quality. This continuous quality scale is then used to compute error bars upon the data, which quantify the data uncertainty and provide a more meaningful measure of the data’s fitness for purpose in a particular application compared with hard quality classifications. The design principles of the framework are presented and enable both data statistics and expert knowledge to be incorporated into the uncertainty assessment. We have implemented and tested the framework upon a real time platform of temperature and conductivity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. Results indicate that the error bars generated from the Fuzzy QA/QC implementation are in good agreement with the error bars manually encoded by a domain expert.

In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.

Recent research indicates a lot of attempts to create an intrusion detection system that is capable of learning and recognizing attacks it faces for the first time. Benchmark datasets were created by the MIT Lincoln Lab and by the International Knowledge Discovery and Data Mining group (KDD). A number of competitions were held and many systems developed as a result. The overall preference was given to expert systems that were based on decision making tree algorithms. This paper explores neural networks as means of intrusion detection. After multiple techniques and methodologies are investigated, we show that properly trained neural networks are capable of fast recognition and classification of different attacks at the level superior to previous approaches.

Multiple sensors are incorporated in modern mobile devices. The ubiquity of these mobile devices with a wide range of embedded sensors enables developing new types of sensor networks and novel applications. However, more often than not, users are unaware of the sensor quality in their devices. With the goal of facilitating closer collaboration between sensor developers and users' communities, we developed a knowledge base that contains various characteristics of sensors embedded into mobile devices and their quality analysis. In our novel analysis, we fused both sensor and mobile device characteristics to produce integral sensor quality indicators. Machine learning and clustering techniques were employed. To validate our approach, we collected comprehensive information about sensors currently embedded in more than 9000 mobile devices and analyzed them. We made this knowledge base and evaluation results available to the community. Also, an Android OS-based application that evaluates the quality of sensors in a particular device and provides recommendations on their use was developed and presented for the community use. Examples of possible knowledge base applications are given.