DR2S : Deep Regression with Region Selection for Camera Quality Evaluation

In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition. Importantly, our texture preservation measurement should coincide with human perception. Consequently, we formulate our problem as a regression one and we introduce a deep convolutional network to estimate texture quality score. At training time, we use ground-truth quality scores provided by expert human annotators in order to obtain a subjective quality measure. In addition, we propose a region selection method to identify the image regions that are better suited at measuring perceptual quality. Finally, our experimental evaluation shows that our learning-based approach outperforms existing methods and that our region selection algorithm consistently improves the quality estimation

Remote Screening And Self-Monitoring For Vision Loss Diseases Based On Smartphone Applications

Remote Healthcare Monitoring System (RHMS) represents remote observing of patient’s well-being and providing therapeutic services. Sensors play an essential part in RHMs. They measure the physical parameters and give continuous information to health organizations, doctors. The presence of Smartphones and other portable devices have allowed us to utilize remote healthcare monitoring system for an assortment of structures. Also, Wireless Sensor Network (WSN) advances considered as one of the critical research factor healthcare application for enhancing the standard of living. In this dissertation, I have presented three tiers operating in the remote healthcare monitoring system; the Body Area Network (BAN), the PAN Coordinator and the Back- Medical End System (BMEsys). The three tiers focused on several patients PAN coordinators include the Wireless Sensor Network. The Wireless Sensor Network can be used at the fixed tale-monitor location and periodic measurements. The Personal Digital Assistant (PDA) can be used in patients own home or community setting with continuous measurements and smartphones can be utilized anywhere with full range parameters, and I have provided a meaningful utilization comparison between Wireless Sensor Network, PDA and smartphone in Remote Healthcare Monitoring System (HRMs) architecture design. Evaluate the approaches of the healthcare monitoring system architecture and investigate the use of advanced technologies enabling the patient vital signs and diagnostic medical team in real-time. This dissertation demonstrates that how a Smartphone can be used for medical treatment in the field of Ophthalmology and discussed how a Smartphone and its technology could be used to diagnose loss of eye vision. Most recent smartphones have been equipped with a featured camera with high megapixels and advanced sensors which can be used to record fundus photographs through a slit lamp or record videos from an operating microscope and display images from optical coherence tomography systems and other high-tech devices. The ophthalmologists can share these images and analyze with their colleagues utilizing media sharing applications and make the optimal diagnostic and therapeutic results to diagnose the low vision of patients. At present, three widely used pocket-sized adapters can improve the magnification and lighting of the camera, which enables the smartphones to capture high-quality images of the eye. These are Portable Eye Examination Kit (PEEK), EyeGo, and D-Eye. Peek Adapter consists of a smartphone application and retina adapter which can be clipped onto the device and synchronized with the peek application for sharing and analyzing the images. This adapter can be used by anyone and anywhere in the world to examine eyes. EyeGo is an adapter intended to allow ophthalmologists and healthcare specialists to capture high-quality images of the eye using an ophthalmic lens. D-Eye Adapter is one of the extensively used adapters which yield excellent results. It consists of a portable eye and retinal system that fits onto a smartphone creating a retinal camera for evaluation and screening of the eye. It uses LED lights as a light source and requires no extra power, making it an ideal solution for portable diagnostics. The medical field has widely accepted these adaptors with the smartphones for diagnosing low vision and eye-related infections. In this dissertation, I also provide a meaningful utilization comparison between the smartphone adapters: D-Eye, EyeGo and Portable Eye Examination Kit (PEEK). In this dissertation, I have developed a new App (Remote Healthcare-Monitoring Mobile App) to help patients who have low vision and who are suffering from the diseases which may cause a vision loss. This app is capable of a process, evaluate, interact and store health data which is continuously measured by (Personal Health Monitors). This App can exchange the information directly to the Smartphone users (patients) and the doctor who allows more security and privacy. The idea of the App consists of the following: A Smartphone Application, a Data Collection Center, and Professionals in Ophthalmology. The patient should be registered in the system, for example, (Retina Michigan Center or Glaucoma Michigan Center). After registration, the patient is instructed on how to take photos of his/her eyes correctly, and then use the Smartphone application. The patient takes photos of his/her eyes and sends them to the data collection center, the specialists get access to these data and help in the treatment according to the analysis. Finally, I completed the development of the Mobile app (including the Skype and Viber links), which can help in exchanging the information between the patient and the doctor

Integrated Framework for Data Quality and Security Evaluation on Mobile Devices

Data quality (DQ) is an important concept that is used in the design and employment of information, data management, decision making, and engineering systems with multiple applications already available for solving specific problems. Unfortunately, conventional approaches to DQ evaluation commonly do not pay enough attention or even ignore the security and privacy of the evaluated data. In this research, we develop a framework for the DQ evaluation of the sensor originated data acquired from smartphones, that incorporates security and privacy aspects into the DQ evaluation pipeline. The framework provides support for selecting the DQ metrics and implementing their calculus by integrating diverse sensor data quality and security metrics. The framework employs a knowledge graph to facilitate its adaptation in new applications development and enables knowledge accumulation. Privacy aspects evaluation is demonstrated by the detection of novel and sophisticated attacks on data privacy on the example of colluded applications attack recognition. We develop multiple calculi for DQ and security evaluation, such as a hierarchical fuzzy rules expert system, neural networks, and an algebraic function. Case studies that demonstrate the framework\u27s performance in solving real-life tasks are presented, and the achieved results are analyzed. These case studies confirm the framework\u27s capability of performing comprehensive DQ evaluations. The framework development resulted in producing multiple products, and tools such as datasets and Android OS applications. The datasets include the knowledge base of sensors embedded in modern mobile devices and their quality analysis, technological signals recordings of smartphones during the normal usage, and attacks on users\u27 privacy. These datasets are made available for public use and can be used for future research in the field of data quality and security. We also released under an open-source license a set of Android OS tools that can be used for data quality and security evaluation

Application of Low-cost Color Sensor Technology in Soil Data Collection and Soil Science Education

Sensor technologies provide opportunities to increase the quality and quantity of soils data while introducing new techniques and tools for classrooms. Linear regression models were developed for organic carbon prediction using color data gathered with the Nix Pro™ for dry (R2 = 0.7978, MSPE = 0.0819), and moist soils (R2 = 0.7254, MSPE = 0.1536). A mobile application, the Soil Scanner app, was created to allow for a more soil science dedicated interface that would allow users to create their own database consisting of GPS location and soil color data gathered using the Nix Pro™. The final application produced results in multiple color systems, including Munsell, recorded GPS location, sample depth, moisture conditions, in-field or laboratory settings, and a photograph of the soil sample. All data could then be uploaded to an online database. The GPS location allows for easy integration of data into GIS mapping software for the spatial manipulation of soils data. The application was tested by generating GIS maps showing the gradient of soil color across two field surfaces. The Nix Pro™ color sensor functions as a successful teaching tool and, coupled with the Soil Scanner app, offers a new means of gathering and storing reliable soils data. There is added benefit to having a soil science application that can be updated to include further analysis methods, resulting in an ever growing soils database. A laboratory exercise was developed that introduced students in an entry level soils course to the importance of soil color and the methods used to determine soil color. Students were then asked to determine the color of three soil samples using the Nix Pro™ and the standard Munsell Color Chart before conducting simple statistical analysis and responding to a questionnaire. Responses indicate that the Nix Pro™ was the preferred method of color analysis and students felt the sensor to be a more reliable method than traditional color books

Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.publishedVersio

Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities