Novel CCII-based Field Programmable Analog Array and its Application to a Sixth-Order Butterworth LPF

In this paper, a field programmable analog array (FPAA) is proposed. The proposed FPAA consists of seven configurable analog blocks (CABs) arranged in a hexagonal lattice such that the CABs are directly connected to each other. This structure improves the overall frequency response of the chip by decreasing the parasitic capacitances in the signal path. The CABS of the FPAA is based on a novel fully differential digitally programmable current conveyor (DPCCII). The programmability of the DPCCII is achieved using digitally controlled three-bit MOS ladder current division network. No extra biasing circuit is required to generate specific analog control voltage signals. The DPCCII has constant standby power consumption, offset voltage, bandwidth and harmonic distortions over all its programming range. A sixth-order Butterworth tunable LPF suitable for WLAN/WiMAX receivers is realized on the proposed FPAA. The filter power consumption is 5.4mW from 1V supply; it’s cutoff frequency is tuned from 5.2 MHz to 16.9 MHz. All the circuits are realized using 90nm CMOS technology from TSMC. All simulations are carried out using Cadence

Regulation of the Adrenal Cortex Function During Stress

A proposal to study the function of the adrenal gland in the rat during stress is presented. In the proposed project, three different phases of experimentation will be undertaken. The first phase includes establishment of the circadian rhythm of both brain amines and glucocoticoids, under normal conditions and under chronic and acute stressful conditions. The second phase includes the study of the pharmacokinetics of glucocorticoid binding under normal and stress conditions. The third phase includes brain uptake and binding under different experimental conditions. In the outlined experiments brain biogenic amines will be evaluated, adrenal functions will be measured and stress effect on those parameters will be studied. It is hoped that this investigation can explain some of the complex relationships between the brain neurotransmitter and adrenal function

Enterprise Risk Management and Firm Performance: An Integrated Model for the Banking Sector

This study investigates how the implementation of Enterprise Risk Management program affects the performance of firms using an Enterprise Risk Management model for the banking sector and an integrated model for measuring Enterprise Risk Management index used in the study by Mukhtar and Soliman (2016). Ten listed commercial banks were selected with the Enterprise Risk Management index as the main independent variable, with Return on Average Equity (ROAE), Share Price Return (SPR) and Firm Value (FV) used as three separate dependent variables. The study provides strong evidence of a positive relationship between Enterprise Risk Management implementation and performance in the Nigerian banking sector. The findings and conclusions of this study are consistent with those of other studies that used data from different industries, providing a basis from which to generalize the findings from this study to firms in other industries

Capacity Utilization and Unemployment in Selected West African Countries

The purpose of this research paper is to examine the proposition that capacity utilisation is an important factor in the determination of unemployment and wages. Underlying this proposition is the notion that capacity utilisation helps to determine the future path of the economy and is a significant factor in the response of the economy to different supply and demand shocks. We derived capacity utilisation and unemployment relationships, which were estimated and tested using data covering from 1997 to 2016 for three West Africa countries. The results suggest that long-term unemployment and capacity utilisation have a significant impact on unemployment. The policy implications of our findings are that in view of the strong effect of capacity utilisation on unemployment, programmes that enhance efficiency in production and investment enhancing policies may allow unemployed to regain access to the labour market

Potato Classification Using Deep Learning

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach

Track before mitigate: aspect dependence-based tracking method for multipath mitigation

People tracking is a key building block in many applications such as surveillance, abnormal activity detection and the monitoring of elderly persons or persons with restricted mobility. In this reported work, the problem of multipath signals, which is one of the main challenges in indoor and urban environments, is addressed. The proposed method integrates the aspect dependence feature of multipath signals into the tracking framework which allows making full use of more potentially useful information in the time domain in order to make more accurate decisions and to relax some constraints in the space domain such as the large number of antennas that are placed over a large area. An important feature of the proposed method is that it can suppress/mark the entire multipath track; furthermore, it does not assume any prior knowledge of the environment

STOCK MARKET DEVELOPMENT AND ECONOMIC GROWTH: THE CAUSAL LINKAGE

This paper addresses the question: does stock market development cause growth? It examines the causal linkage between stock market development, financial development and economic growth. The argument is that any inference that financial liberalisation causes savings or investment or growth, or that financial intermediation causes growth, drawn from bivariate causality tests may be invalid, as invalid causality inferences can result from omitting an important variable. The empirical part of this study exploits techniques recently developed by Toda and Yamamoto (1995) to test for causality in VARs, and emphasises the possibility of omitted variable bias. The evidence obtained from a sample of seven countries suggests that a well-developed stock market can foster economic growth in the long run. It also provides support to theories according to which well-functioning stock markets can promote economic development by fuelling the engine of growth through faster capital accumulation, and by tuning it through better resource allocation.Financial Development, Economic Growth, Stock Market, Causality Testing, VARs, Incomplete Systems