Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks

A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft. Existing sense-and-avoid methods often require specialized sensors that are too large or power intensive for use on small unmanned vehicles. This paper presents a method to estimate object distances based on visual image sequences, allowing for the use of low-cost, on-board monocular cameras as simple collision avoidance sensors. We present a deep recurrent convolutional neural network and training method to generate depth maps from video sequences. Our network is trained using simulated camera and depth data generated with Microsoft's AirSim simulator. Empirically, we show that our model achieves superior performance compared to models generated using prior methods.We further demonstrate that the method can be used for sense-and-avoid of obstacles in simulation

The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation

We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M. Furthermore, we show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets, which makes it highly suitable for real-world applications where robustness, accuracy as well as low inference time on high-resolution fundus images is required

Intelligent control and security of fog resources in healthcare systems via a cognitive fog model

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this paper, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN and DT) using a standard security dataset (NSL-KDD)

Adapting users' privacy preferences in smart environments

A smart environment is a physical space where devices are connected to provide continuous support to individuals and make their life more comfortable. For this purpose, a smart environment collects, stores, and processes a massive amount of personal data. In general, service providers collect these data according to their privacy policies. To enhance the privacy control, individuals can explicitly express their privacy preferences, stating conditions on how their data have to be used and managed. Typically, privacy checking is handled through the hard matching of users' privacy preferences against service providers' privacy policies, by denying all service requests whose privacy policies do not fully match with individual's privacy preferences. However, this hard matching might be too restrictive in a smart environment because it denies the services that partially satisfy the individual's privacy preferences. To cope with this challenge, in this paper, we propose a soft privacy matching mechanism, able to relax, in a controlled way, some conditions of users' privacy preferences such to match with service providers' privacy policies. At this aim, we exploit machine learning algorithms to build a classifier, which is able to make decisions on future service requests, by learning which privacy preference components a user is prone to relax, as well as the relaxation tolerance. We test our approach on two realistic datasets, obtaining promising results

Helping users managing context-based privacy preferences

Today, users interact with a variety of online services offered by different providers. In order to supply their services, providers collect, store and process users' data according to their privacy policies. To have more control on personal data, user can specify a set of privacy preferences, encoding the conditions according to which his/her data can be used and managed by the provider. Moreover, many services are context dependent, that is, the type of delivered service is based on user contextual information (e.g., time, location, and so on). This makes more complicated the definition of privacy preferences, as, typically, users might have different attitude with respect the privacy management based on the current context (e.g., working hour, free time). To provide a more fine-grained control, a user can set up different privacy preferences for each different possible contexts. However, since user change the context very frequently, this might result in a very complex and time-consuming task. To cope with this issue, in this paper, we propose a context-based privacy management service that helps users to manage their privacy preferences setting under different contexts. At this aim, we exploit machine learning algorithms to build a classifier, able to infer new privacy preferences for the new context. The preliminary experimental results we have conducted are promising, and show the effectiveness of the proposed approach

To clamp or not to clamp? Long-term functional outcomes for elective off-clamp laparoscopic partial nephrectomy

OBJECTIVE: To evaluate whether elective off-clamp laparoscopic partial nephrectomy (LPN) affords long-term renal functional benefit compared with the on-clamp approach. PATIENTS AND METHODS: This is a retrospective review of patients who underwent elective LPN between 2006 and 2011. Patients were followed longitudinally for up to 5 years. In all, 315 patients with radiographic evidence of a solitary renal mass and normal-appearing contralateral kidney underwent elective LPN; 209 were performed on-clamp vs 106 off-clamp. One patient who required conversion from LPN to open PN was excluded from the study. Additionally, four patients in the on-clamp cohort who underwent subsequent radical nephrectomy for local-regional recurrence were excluded from longitudinal functional evaluation after their procedure. The primary objective was to evaluate differences in postoperative estimated glomerular filtration rate (eGFR) between hilar clamping groups. Subgroup analyses were performed for patients with clamp times \u3e30 min and those with baseline renal insufficiency (eGFR/min/1.73m(2) ). Risk of developing worsened or new-onset renal insufficiency was also compared. RESULTS: The mean preoperative eGFR was similar between the on-clamp and off-clamp cohorts (80.7 vs 84.1 mL/min/1.73m(2) , P \u3e 0.05). Univariable and multivariable analyses did not show significant differences in postoperative eGFR between both groups among all-comers, those with clamp times \u3e30 min, and patients with baseline renal insufficiency. Risk of chronic kidney disease was not diminished by the off-clamp approach with up to 5 years of follow-up. CONCLUSIONS: Progressive recovery of renal function after hilar clamping in the elective setting eclipses short-term functional benefit achieved with off-clamp LPN by 6 months; there was no significant difference in eGFR or the percentage incidence of chronic kidney disease between the on-clamp and off-clamp cohorts with up to 5 years follow-up. As such, eliminating transient ischaemia during elective LPN does not confer clinical benefit