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Since wireless systems allow for easier access to communication paths than wired systems, it is necessary to improve their dependability against cyberterrorists. To make wireless systems more dependable, additional measures at the lower layer are required, in addition to those at the upper layers. Our proposal uses an integrated terminal-like cellular phone which has multiple radio access technologies (RATs) such as cellular, wireless local area network (LAN), Bluetooth, and an ultra-wide band (UWB). We propose to communicate information encoded by shortened code using multiple RATs. Redundancy by RATs and their error correction capability can simultaneously improve wiretap resistance and attack resistance. A codeword of shortened code is obtained by removing a part of a codeword of popular code. A decoder can improve the error correction capability if the removed part is known. By using shortened codes, the dependability can be further enhanced because the error correction capability between the legitimate receiver and the cyberterrorist can make a difference. To do this, it is necessary to securely share the removed part between the sender and receiver. Our proposal is to securely measure the distance between the sender and receiver using UWB and use it as the removed part. It was confirmed that the secrecy capacity is improved.

Image-guided neurosurgery, or neuronavigation, has been used to visualise the location of a surgical probe by mapping the probe location to pre-operative models of a patient's anatomy. One common limitation of this approach is that it requires the surgeon to divert their attention away from the patient and towards the neuronavigation system. In order to improve this type of application, the authors designed a system that sonifies (i.e. provides audible feedback of) distance information between a surgical probe and the location of the anatomy of interest. A user study (n = 15) was completed to determine the utility of sonified distance information within an existing neuronavigation platform (Intraoperative Brain Imaging System (IBIS) Neuronav). The authors’ results were consistent with the idea that combining auditory distance cues with existing visual information from image-guided surgery systems may result in greater accuracy when locating specified points on a pre-operative scan, thereby potentially reducing the extent of the required surgical openings, as well as potentially increasing the precision of individual surgical tasks. Further, the authors’ results were also consistent with the hypothesis that combining auditory and visual information reduces the perceived difficulty in locating a target location within a three-dimensional volume.

Minimally invasive techniques, such as laparoscopy and radiofrequency ablation of tumors, bring important advantages in surgery: by minimizing incisions on the patient's body, they can reduce the hospitalization period and the risk of postoperative complications. Unfortunately, they come with drawbacks for surgeons, who have a restricted vision of the operation area through an indirect access and 2D images provided by a camera inserted in the body. Augmented reality provides an "X-ray vision" of the patient anatomy thanks to the visualization of the internal organs of the patient. In this way, surgeons are free from the task of mentally associating the content from CT images to the operative scene. We present a navigation system that supports surgeons in preoperative and intraoperative phases and an augmented reality system that superimposes virtual organs on the patient's body together with depth and distance information. We implemented a combination of visual and audio cues allowing the surgeon to improve the intervention precision and avoid the risk of damaging anatomical structures. The test scenarios proved the good efficacy and accuracy of the system. Moreover, tests in the operating room suggested some modifications to the tracking system to make it more robust with respect to occlusions. Graphical Abstract Augmented visualization in minimally invasive surgery.

In vehicular ad hoc networks (VANets), a precise localization system is a crucial factor for several critical safety applications. The global positioning system (GPS) is commonly used to determine the vehicles&rsquo, position estimation. However, it has unwanted errors yet that can be worse in some areas, such as urban street canyons and indoor parking lots, making it inaccurate for most critical safety applications. In this work, we present a new position estimation method called cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS positions of nearby vehicles and minimize their errors through an extended Kalman filter to execute Data Fusion using GPS and distance information. Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in distance information given by neighbors closer to the target. We implement and evaluate the performance of CoVaLID using real-world data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that CoVaLID is capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet location improve (VLOCI) algorithm.

Partially supported by TUBITAK grant 106E071. We construct a robust localization framework to handle noisy measurements in wireless sensor networks. Traditionally many approaches employ the distance information gathered from ranging devices of the sensor nodes to achieve localization. However the measurements of these devices may contain noise both as hardware noise and as environmental noise due to the employment conditions of the network. It Is necessary to provide a general framework that handles such a noise in data and yet still be applicable within several localization algorithms. In order to handle noise in distance measurements, our framework utilizes convex constraints and confidence intervals of a random variable. At the end of the localization process nodes are assigned to a set of feasible regions with corresponding probabilities. The accuracy of the localization can be adjusted and the framework can easily be embedded to work within previously suggested localization algorithms. Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) Publisher's Version WOS:000275024200125