There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

The term photonic crystals appears because of the analogy between electron waves in crystals and the light waves in artificial periodic dielectric structures. During the recent years the investigation of one-, two-and three-dimensional periodic structures has attracted a widespread attention of the world optics community because of great potentiality of such structures in advanced applied optical fields. The interest in periodic structures has been stimulated by the fast development of semiconductor technology that now allows the fabrication of artificial structures, whose period is comparable with the wavelength of light in the visible and infrared ranges.

http://ieeexplore.ieee.org/iel5/6354/16969/00781867.pdf

Photonic crystals

Mobile devices have penetrated the healthcare sector due to their increased functionality, low cost, high reliability and easy-to-use nature. However, in healthcare applications the privacy and security of the transmitted information must be preserved. Therefore applications require a concrete security framework based on long-term security keys, such as the security key that can be found in a mobile Subscriber Identity Module (SIM). The wireless nature of communication links in mobile networks presents a major challenge in this respect. This paper presents a novel protocol that will send the information securely while including the access privileges to the authorised recipient.

/pdf/securing-electronic-health-records-with-novel-mobile-2o1qphkuus.pdf

Securing electronic health records with novel mobile encryption schemes.

Activities performed in the same location in a smart home share common features and thus become difficult to classify. We propose an activity recognition approach that identifies key features from the information obtained using the sensors deployed in multiple locations and objects. Key features increase the separability between the classes, making the approach suitable for overlapping activities. For fewer number of activity instances in a class, we apply an oversampling approach for data balancing. The classification is performed using a learning method Evidence Theoretic K-Nearest Neighbors (ET-KNN), which performs better in uncertain conditions. Evaluation of the proposed approach using three publicly available smart home datasets demonstrates better recognition performance compared to the existing methods.

Feature selection and data balancing for activity recognition in smart homes

Use of a semivectorial finite element-based beam propagation method is benchmarked with the results available by using alternative numerical methods to analyse compact optical bends in photonic integrated circuits. It is also shown that the incorporation of the perfectly matched layers boundary condition is a superior approach for the treatment of high radiation loss from compact bends to the more common use of the absorbing boundary conditions. The least squares boundary residual method is employed to calculate the lateral offset necessary to maximise the transmission and simultaneously to minimise the reflection coefficients at the straight-to-bend waveguide interfaces. Using these efficient numerical techniques, the radiation loss, transition loss and the required waveguide offset to maximise the transmission and reduce the reflection are calculated.

Characterisation of low-loss waveguide bends with offset-optimisation for compact photonic integrated circuits

Anomalies are the instances of an activity class that deviate from the normal or expected sequence of events in their performance. In this paper we propose an anomaly detection approach for activities performed in a smart home. In order to detect anomalies, we exploit the information of number of events and the time duration involved in performing an activity instance. The information is obtained through a network of wireless sensors deployed at multiple objects and locations within a smart home. We apply a density based clustering algorithm on the recognized activity instances to separate the normal from the anomalous. Evaluation of the proposed approach on two publicly available smart home datasets demonstrates its effectiveness in identifying the anomalous activity instances.

Muttukrishnan Rajarajan

Papers

Securing electronic health records with novel mobile encryption schemes.

Feature selection and data balancing for activity recognition in smart homes

Characterisation of low-loss waveguide bends with offset-optimisation for compact photonic integrated circuits

Anomalies Detection in Smart-Home Activities

DDoS victim service containment to minimize the internal collateral damages in cloud computing