Delay Constrained Throughput Analysis of a Correlated MIMO Wireless Channel

The maximum traffic arrival rate at the network for a given delay guarantee (delay constrained throughput) has been well studied for wired channels. However, few results are available for wireless channels, especially when multiple antennas are employed at the transmitter and receiver. In this work, we analyze the network delay constrained throughput of a multiple input multiple output (MIMO) wireless channel with time-varying spatial correlation. The MIMO channel is modeled via its virtual representation, where the individual spatial paths between the antenna pairs are Gilbert-Elliot channels. The whole system is then described by a K-State Markov chain, where K depends upon the degree of freedom (DOF) of the channel. We prove that the DOF based modeling is indeed accurate. Furthermore, we study the impact of the delay requirements at the network layer, violation probability and the number of antennas on the throughput under different fading speeds and signal strength.Comment: Submitted to ICCCN 2011, 8 pages, 5 figure

Wavelet based segmentation of hyperspectral colon tissue imagery

Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit the spectral characteristics of the hyperspectral imagery data

On The Modeling of OpenFlow-based SDNs: The Single Node Case

OpenFlow is one of the most commonly used protocols for communication between the controller and the forwarding element in a software defined network (SDN). A model based on M/M/1 queues is proposed in [1] to capture the communication between the forwarding element and the controller. Albeit the model provides useful insight, it is accurate only for the case when the probability of expecting a new flow is small. Secondly, it is not straight forward to extend the model in [1] to more than one forwarding element in the data plane. In this work we propose a model which addresses both these challenges. The model is based on Jackson assumption but with corrections tailored to the OpenFlow based SDN network. Performance analysis using the proposed model indicates that the model is accurate even for the case when the probability of new flow is quite large. Further we show by a toy example that the model can be extended to more than one node in the data plane.Comment: Published in Proceedings of CS & IT for NeCOM 201

Sources to Finance Fiscal Deficit and Their Impact on Inflation: A Case Study of Pakistan

Theoretically, fiscal deficit is inflationary but the sources of financing fiscal deficit may differ in terms of their impact on inflation. Question arises that what should be the least inflation cost source of financing? This study attempts to answer this question and explore the long run relationship among the sources to finance fiscal deficit and inflation. In so doing, the estimations have been done in four stages on the basis of categorisation of the deficit financing heads. In the first stage it has been tested that fiscal deficit along with money supply are inflationary. In the second stage fiscal deficit is bifurcated into two components, domestic borrowing and external borrowing for fiscal deficit. In the third stage, domestic borrowing is further divided into two heads, bank and non-bank borrowing. While in the fourth and last stage, bank borrowing is further categorised into two parts, borrowing from scheduled banks and central bank, and non-bank borrowing which comprises borrowing from National Saving Scheme for budgetary support. The Johansen Cointegration Technique is used for the first stage of estimation, while Auto Regressive Distributed Lag Model is employed for the rest of the three stages. The study finds that there is a long run relationship among sources of financing fiscal deficit and inflation. Inflation is positively affected by domestic borrowing, bank borrowing and borrowing from central bank, while central bank borrowing is more inflationary in nature. Consequently, fiscal deficit should be financed through external sources, non-bank and scheduled bank borrowings. JEL Classification: H62, H74, E31 Keywords: Deficit, State and Local Borrowing, Inflatio

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Derivative Usage In Corporate Pakistan: A Qualitative Research Of Listed Companies

The motivation behind this study was to see why Pakistani companies are releuctant to use derivative instruments. The study aims to look into factors that influence the corporate finance managers to use derivatives. A structured questionnaire was used to obtain the response of finance managers of companies. The questionnaire aims at ascertaining the factors that influence the usage or non-usage of derivatives in corporate Pakistan. The questionnaire incorporated factors like trend of derivative usage, risk level, awareness with modern finance, correlation between hedging and firm’s value, firm’s performance and business cycle effect, and correlation between nature of business and financial risk. For this purpose, 67 non-financial firms were selected based on their nature of business, turnover, and risk level. Out of 67, 31 firms responded. We concluded that managerial knowledge of modern finance, development of full fledged derivatives market and measuring the risk level of corporation may enhance the derivative usage thus minimizing the financial risk of companies

A Quantum Key Distribution Network Through Single Mode Optical Fiber

Quantum key distribution (QKD) has been developed within the last decade that is provably secure against arbitrary computing power, and even against quantum computer attacks. Now there is a strong need of research to exploit this technology in the existing communication networks. In this paper we have presented various experimental results pertaining to QKD like Raw key rate and Quantum bit error rate (QBER). We found these results over 25 km single mode optical fiber. The experimental setup implemented the enhanced version of BB84 QKD protocol. Based upon the results obtained, we have presented a network design which can be implemented for the realization of large scale QKD networks. Furthermore, several new ideas are presented and discussed to integrate the QKD technique in the classical communication networks.Comment: This paper has been submitted to the 2006 International Symposium on Collaborative Technologies and Systems (CTS 2006)May 14-17, 2006, Las Vegas, Nevada, US

Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline