A security suite for wireless body area networks

Wireless Body Area Networks (WBANs) have gained a lot of research attention in recent years since they offer tremendous benefits for remote health monitoring and continuous, real-time patient care. However, as with any wireless communication, data security in WBANs is a challenging design issue. Since such networks consist of small sensors placed on the human body, they impose resource and computational restrictions, thereby making the use of sophisticated and advanced encryption algorithms infeasible. This calls for the design of algorithms with a robust key generation / management scheme, which are reasonably resource optimal. This paper presents a security suite for WBANs, comprised of IAMKeys, an independent and adaptive key management scheme for improving the security of WBANs, and KEMESIS, a key management scheme for security in inter-sensor communication. The novelty of these schemes lies in the use of a randomly generated key for encrypting each data frame that is generated independently at both the sender and the receiver, eliminating the need for any key exchange. The simplicity of the encryption scheme, combined with the adaptability in key management makes the schemes simple, yet secure. The proposed algorithms are validated by performance analysis.Comment: 20 pages, 10 figures, 3 tables, International Journal of Network Security & its Applications (IJNSA

On the Lipschitz continuity of the solutions map in semidefinite linear complementarity problems

In this paper, we investigate the Lipschitz continuity of the solution map in semidefinite linear complementarity problems. For a monotone linear transformation defined on the space of real symmetric n × n matrices, we show that the Lipschitz continuity of the solution map implies the globally uniquely solvable (GUS)-property. For Lyapunov transformations with the Q-property, we prove that the Lipschitz continuity of the solution map is equivalent to the strong monotonicity property. For the double-sided multiplicative transformations, we show that the Lipschitz continuity of the solution map implies the GUS-property

SD-OCT to differentiate traumatic submacular hemorrhage types using automatic three-dimensional segmentation analysis

Traumatic submacular hemorrhage may present with significant decrease in vision and may have varying outcomes. Following injury, the hemorrhage can collect either between the neurosensory retina and retinal pigment epithelium (RPE) or below the RPE. This differentiation may be important to prognosticate and to guide treatment. In two patients with post-traumatic submacular hemorrhage, Cirrius spectral domain high-definition optical coherence tomography (OCT) (Carl Zeiss Meditec, Dublin, CA) was used to differentiate traumatic submacular hemorrhage types using automation three-dimensional segmentation analysis. Based on the OCT findings, the patient with sub-RPE bleed was subjected to pneumatic displacement. En face C-scan imaging just below the RPE allowed for the diagnosis of the exact location of choroidal rupture that was masked due to hemorrhage

Stacked neural nets for increased accuracy on classification on lung cancer

Lung cancer is regarded as one of the most lethal diseases endangering human survival. It is difficult to detect lung cancer in its early stages, because of the ambiguity in the lung regions in the medical images. Healthcare business is automating itself with the use of image recognition and data analytics, much as the computing sector has completely automated. This article proposes a novel architecture, the Stacked Neural Network (SNN), for the detection and classification of lung cancer using CT scan data. The goal of the proposed technique is to investigate the accuracy levels of different Neural Networks (NN) and determine the early stage of lung cancer. The most effective technique for processing medical images, classifying and detecting lung nodules, extracting features, and predicting the stage of lung cancer is deep learning. First, lung areas are extracted using image processing techniques. SNN is used for the segmentation process. Various neural network techniques are utilised for the classification process once the features are retrieved from the segmented pictures. The suggested methods' performances are assessed using F1-Measure, accuracy, precision, and recall metrics. 96% classification accuracy is shown in the testing findings, which is comparatively greater than other methods currently in use. Proposed algorithm is clearly supported for real-world clinical practice