Engineering of impurity doped regions in semiconducting BaSi[2] by MBE for thin film solar cells application

筑波大学University of Tsukuba博士（工学）Doctor of Philosophy in Engineering2012【要旨】thesi

A novel framework for collaborative intrusion detection for M2M networks

The proliferation of sensor devices has introduced exciting possibilities such as the Internet of Things (IoT). Machine to Machine (M2M) communication underpins efficient interactions within such infrastructures. The resource constraints and ad-hoc nature of these networks have significant implications for security in general and with respect to intrusion detection in particular. Consequently, contemporary solutions mandating a stable infrastructure are inadequate to fulfill these defining characteristics of M2M networks. In this paper, we present COLIDE (COLlaborative Intrusion Detection Engine) a novel framework for effective intrusion detection in the M2M networks without incurring high energy and communication cost on the participating host and edge nodes. The framework is envisioned to address challenges such as flexibility, resource constraints, and the collaborative nature of the M2M networks. The paper presents a detailed system description along with its formal and empirical evaluation using Contiki OS. Our evaluation for different communication scenarios demonstrates that the proposed approach has limited overhead in terms of energy utilization and memory consumption

Magnetic resonance imaging (MRI) findings in white matter disease of brain

Demyelinating and dysmyelinating white matter diseases are important components of neurological problems. Recently, Magnetic Resonance Imaging (MRI) has played a key role in diagnoses of white matter diseases. Therefore, the purpose of the current study is to evaluate the usefulness of MRI in determining the type and frequency of white matter disease. We studied 35 patients who visited the Radiology Department of the Aga Khan University Hospital (AKUH) for MRI with suspected demyelinating/dysmyelinating disorder from January 2003 to December 2005. Multiple Sclerosis (MS) (17; 48%) and leukodystrophies (10; 29%) were the most common diseases. The MRI helped identify the sites and types of the lesion precisely and thereby helped made clearer. distinction between various types of white matter diseases. The current study demonstrated the effective use of the imaging and clinical presentation for arriving at the correct diagnosis

Gaussian mixture model based probabilistic modeling of images for medical image segmentation

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin

Clustering VoIP caller for SPIT identification

The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non-spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non-spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set-up phase

Authentic-caller : self-enforcing authentication in a next generation network

The Internet of Things (IoT) or the Cyber-Physical System (CPS) is the network of connected devices, things and people which collect and exchange information using the emerging telecommunication networks (4G, 5G IP-based LTE). These emerging telecommunication networks can also be used to transfer critical information between the source and destination, informing the control system about the outage in the electrical grid, or providing information about the emergency at the national express highway. This sensitive information requires authorization and authentication of source and destination involved in the communication. To protect the network from unauthorized access and to provide authentication, the telecommunication operators have to adopt the mechanism for seamless verification and authorization of parties involved in the communication. Currently, the next-generation telecommunication networks use a digest-based authentication mechanism, where the call-processing engine of the telecommunication operator initiates the challenge to the request-initiating client or caller, which is being solved by the client to prove his credentials. However, the digest-based authentication mechanisms are vulnerable to many forms of known attacks e.g., the Man-In-The-Middle (MITM) attack and the password guessing attack. Furthermore, the digest-based systems require extensive processing overheads. Several Public-Key Infrastructure (PKI) based and identity-based schemes have been proposed for the authentication and key agreements. However, these schemes generally require smart-card to hold long-term private keys and authentication credentials. In this paper, we propose a novel self-enforcing authentication protocol for the SIPbased next-generation network based on a low-entropy shared password without relying on any PKI or trusted third party system. The proposed system shows effective resistance against various attacks e.g., MITM, replay attack, password guessing attack, etc. We a..