This study presents a novel multiple objects tracking (MOT) approach that models object's appearance based on K‐means, while introducing a new statistical measure for association of objects after occlusion. The proposed method is tested on several standard datasets dealing complex situations in both indoor and outdoor environments. The experimental results show that the proposed model successfully tracks multiple objects in the presence of occlusion with high accuracy. Moreover, the presented work has the capability to deal long term and complete occlusion without any prior training of the shape and motion model of the objects. Accuracy of the proposed method is comparable with that of the existing state‐of‐the‐art techniques as it successfully deals with all MOT cases in the standard datasets. Most importantly, the proposed method is cost effective in terms of memory and/or computation as compared with that of the existing state‐of‐the‐art techniques. These traits make the proposed system very useful for real‐time embedded video surveillance platforms especially those that have low memory/compute resources.

Recent studies have shown that existing elliptic curve-based cryptographic standards provide backdoors for manipulation and hence compromise the security. In this regard, two new elliptic curves known as Curve448 and Curve25519 are recently recommended by IETF for transport layer security future generations. Hence, cryptosystems built over these elliptic curves are expected to play a vital role in the near future for secure communications. A high-speed elliptic curve cryptographic processor (ECCP) for the Curve448 is proposed in this study. The area of the ECCP is optimised by performing different modular operations required for the elliptic curve Diffie-Hellman protocol through a unified architecture. The critical path delay of the proposed ECCP is optimised by adopting the redundant-signed-digit technique for arithmetic operations. The segmentation approach is introduced to reduce the required number of clock cycles for the ECCP. The proposed ECCP is developed using look-up-tables (LUTs) only, and hence it can be ported to any field-programmable gate array family or standard ASIC libraries. The authors' ECCP design offers higher speed without any significant area overhead to recent designs reported in the literature.

To observe spirometry and its correlation with postoperative pulmonary complications in cardiac surgery patients.

Face recognition is one of the most important technologies, which has been well-developed over the last two decades. Face recognition technology has reached a level of utmost importance as the security issues increase worldwide. Most of the previously proposed systems, based on half face images are computationally slow and require more storage. In the proposed model, an average half face image is used for recognition to reduce computational time and storage requirements. The Viola Jones method is used in conjunction with intensity-based registration for real time face detection and registration, before splitting the full face. Finally, Principal Component Analysis (PCA) is used to compress the multi-dimensional data space and recognition. Experimental results clearly elaborate that half face recognition produces much better results as compared to the full face recognition and other previously proposed half face recognition models.