Determinants of Capital Structure Choices for Listed Manufacturing Companies in Bangladesh

The study attempts to investigatethe firm specific determinants to explore capital structure choices by using paneldata model for 63 DSE listed manufacturing companies during 2008 to 2012. The FGLSpanel data analysis reveals that determinants assumed under pecking order theoryhave dominating influence on leverage in Bangladesh and short term debt is preferredto long term debt as a source of financing. The implication of this study under transitional economic and infrastructuraloutset profitable firm should finance its project through internally generated fundswithout changing present situation rather availing greater debt capacity as wellas without changing its control scenario. If there is lack of available internalfunds (retained earnings), firm’s manager should be prudent enough to decide rightchoices for financing at that time without inclining to any specific one (only debtor only new stock).Keywords: Capital Structure, Pecking order theory, FGLS

Impact of Inflation on Growth of Net Assets of Listed Companies in Bangladesh: A Study on DS30 Companies

This study compared between nominal growth and inflation adjusted growth of net assets of DS30 companies of Bangladesh and it is found that inflation has significant impact on the growth of net assets. When inflation is adjusted to the growth rate of net assets, it is easy to decide whether net assets grow because of performance or inflation. Measuring net asset is important because it is one of the important elements to decide for doing investments. Investors should consider inflation adjusted net asset growth to consider of their investments. To do the study, 30 companies which are considered as DS30 companies in Dhaka Stock Exchange are taken as sample. Keywords: Inflation, Net asset growth, Bangladesh, Dhaka Stock Exchange.

Study and Implementation of Wideband Bow-Tie Antennas

Demand for multifunctional electronic devices is increasing in modern wireless communication systems. As the antenna plays a vital role in wireless communication, the need to design antennas which will provide better performance and more reliable communication is growing. In this thesis, innovative designs for antennas with wideband characteristic have been proposed to meet the demands of current multi-functional wireless communication systems. First, this thesis explores the design of a wideband pattern reconfigurable antenna with steady realized gain over the operating bandwidth. Another novel design of this thesis work is a highly directive wideband Yagi antenna. Finally, a two-planar structured CPLPDA antenna is designed to overcome the currently existing three-planar structured CPLPDA antenna’s complex design and fabrication process

Joint access-backhaul mechanisms in 5G cell-less architectures

Older generations of wireless networks, such as 1G and 2G were deployed using leased line, copper or fibre line as backhaul. Later, in 3G and 4G, microwave wireless links have also worked as backhaul links while the backbone of the network was still wireline-based. However, due to multiple different use cases and deployment scenarios of 5G, solo wireline based backhaul network is not a cost-efficient option for the operators anymore. For cost-efficient and fast deployment, wireless backhaul options are very attractive. As drawbacks, wireless backhaul links have capacity and distance limitations. To take the advantages of both the solutions, i.e., wired and wireless, 5G transport networks are anticipated to be a heterogeneous, complex, and with stringent performance requirements. To address the aforementioned challenges, wireless backhaul options are providing more attractive solutions, and hence, technologies using the same resources (e.g., frequency channels) may be used by both access and backhaul networks. In this scenario, blurring the separation line between access and backhaul networks allows resource sharing and cooperation between both the networks and minimizes the network deployment and maintenance cost significantly. Therefore, in 5G, the access and backhaul networks cannot be seen as separate entities; rather, we seek to integrate them together to ensure the best use of resources. In this thesis, firstly, we investigate the challenges and potential technologies of 5G transport network. Later, to address these challenges, we identify and present different approaches to perform joint access-backhaul mechanism. An initial performance evaluation of access-aware backhaul optimization is presented, where backhaul network is dynamically assigned with the required resources to serve the dynamic requirements of a 5G access network. The evaluation results and discussions manifest the resource efficiency of joint access-backhaul mechanisms. Functional splits in different layers of the access network comes as an intelligent solution to reduce the enormous capacity requirements of the transport network in a centralized radio access network approach, which tends to centralize almost all the functionalities into a central unit, leaving only radio frequency functions at the access points. From the joint access-backhaul mechanism perspective, we propose a novel technique, which takes the benefit of functional splits at physical layer, to design a heterogeneous transport network in an economical budget-limited and capacity-limited scenario. Till today, the limited capacity of the wireless backhaul links remains a challenge, and hence, frequency spectrum becomes scarce, and requires efficient utilization. To address this challenge, a joint spectrum sharing technique to implement joint accessbackhaul mechanism is presented. Evaluation results show that our proposed joint spectrum sharing technique, where spectrum allocation in the backhaul network follows the access network's traffic load, is fair and efficient in terms of spectrum utilization. We also propose a machine learning technique, which analyses data from a real network and estimates access network's traffic pattern, and subsequently, assigns bandwidth in the access network according to the traffic estimations. Presented evaluation results show that a well-trained machine learning model can be very efficient to obtain an efficient utilization of frequency spectrum.Las primeras generaciones de redes móviles, se implementaron utilizando líneas de cobre o fibra para la conexión entre la red de acceso y el núcleo de la red (conexión backhaul). Más tarde, los enlaces inalámbricos también han funcionado como backhaul mientras que la columna vertebral de la red seguía basada en cable. Sin embargo, debido a los múltiples escenarios de implementación de 5G, una red de backhaul basada solamente en cable ya no es una opción rentable para los operadores. Para una implementación rentable y rápida, las opciones de backhaul inalámbrico son muy atractivas. Como inconvenientes, los enlaces backhaul inalámbricos tienen limitaciones de capacidad y distancia. Para aprovechar las ventajas de ambas soluciones, es decir, cableadas e inalámbricas, se prevé que las redes de transporte 5G sean heterogéneas, complejas y con estrictos requisitos de rendimiento. Para abordar los desafíos antes mencionados, las opciones de backhaul inalámbrico brindan soluciones más atractivas y, por lo tanto, las tecnologías que usan los mismos recursos (por ejemplo, canales de frecuencia) pueden usarse tanto en las redes de acceso como en las de backhaul. En este escenario, desdibujar la línea de separación entre las redes de acceso y backhaul permite el intercambio de recursos y la cooperación entre ambas redes, y minimiza significativamente los costes de implementación y mantenimiento de la red. Por lo tanto, en 5G las redes de acceso y backhaul no pueden verse como entidades separadas; más bien consideraremos su integración para asegurar el mejor uso de los recursos. En esta tesis, en primer lugar, investigamos los desafíos y las tecnologías potenciales para la implementación de la red de backhaul 5G. Más tarde, para abordar dichos desafíos, identificamos diferentes enfoques para un mecanismo conjunto de gestión de la red de acceso y backhaul. Se presenta una evaluación de rendimiento inicial para la optimización de backhaul que tiene en cuenta el estado de la red de acceso, donde la red de backhaul se equipa dinámicamente con los recursos necesarios para cumplir con los requisitos de la red de acceso 5G. Los resultados de la evaluación manifiestan la mayor eficiencia de los mecanismos de gestión de recursos que consideran redes de acceso y backhaul conjuntamente. Las divisiones funcionales en diferentes capas de la red de acceso (functional splits) se presentan como una solución inteligente para reducir los enormes requisitos de capacidad de la red de transporte en un enfoque de red de acceso, que tiende a centralizar casi todas las funcionalidades en una unidad central, dejando solo las funciones más relacionadas con la transmisión/recepción de señales en los puntos de acceso. Desde la perspectiva del mecanismo conjunto de red de acceso y backhaul, proponemos una técnica novedosa, que aprovecha las divisiones funcionales en la capa física para diseñar una red de transporte heterogénea con un presupuesto económico y un escenario de capacidad limitada. Hasta el día de hoy, la capacidad limitada de los enlaces inalámbricos sigue siendo un desafío, dado que el espectro de frecuencias es escaso y requiere una utilización eficiente. Para hacer frente a este desafío, se presenta una técnica de gestión de recursos espectrales compartidos entre red de acceso y backhaul. Los resultados de la evaluación muestran que nuestra propuesta, donde la asignación de espectro en la red de backhaul se hace de acuerdo a la carga de tráfico de la red de acceso, es justa y eficiente. También proponemos una técnica de aprendizaje automático, que analiza datos de una red real y estima el patrón de tráfico de la red de acceso para, posteriormente, asignar ancho de banda en la red de acceso de acuerdo con dichas estimaciones. Los resultados de la evaluación presentados muestran que un modelo de aprendizaje automático bien entrenado puede ser una herramienta muy útil a la hora de obtener una utilización eficiente del espectro de frecuencias.Postprint (published version

Mutual fund performance: an analysis of mutual funds’ return compare to the market return (DSEX).

This internship report is submitted in a partial fulfillment of the requirements for the degree of Master of Business Administration,2015.Cataloged from PDF version of Internship report.Includes bibliographical references (page 20).Mutual Fund is a Capital Market Investment Vehicle. In Bangladesh there are 48 Mutual Funds under 17 Asset Management Companies. This paper focused on evaluating the performance of 48 growth oriented mutual funds on the basis of weekly returns compared to market returns. Risk adjusted performance measures suggested by Jenson, Treynor, Sharpe and statistical models are employed. It is found that, most of the mutual funds have performed better according to Jenson and Treynor measures but not up to the benchmark on the basis of Sharpe ratio. However, most of the mutual funds are diversified and have reduced its unique risk. The growth oriented funds have performed better in terms of total risk and the funds are offering advantages of diversification and professionalism to the investors. So, mutual funds perform better with their expertise.Rakibul IslamM. Business Administratio

Cost-Effective Sensor Systems for Measuring Extracted Chlorophyll-a Concentration

Chlorophyll-a concentration is one of the most measured metrics in both water quality and plant health monitoring. It is an indicator of algal biomass and provides insight into stressors such as eutrophication and bloom risk. It is also a widely used metric in terrestrial ecosystems as an indicator of photosynthetic activity and nutrient limitation. Most currently used laboratory-based methods for measuring chlorophyll-a exploit spectroscopic techniques and require expensive instrumentation, like spectrophotometer or fluorometer. In addition, the readings are taken inside a black box to avoid optical noise. The purpose of this thesis is to propose a smart, low-cost, and portable sensor system to measure the concentration of chlorophyll-a in an extracted solution. The goals were achieved using two distinct spectral method. The first approach involves two consumer-grade spectral sensors that read the optical reflectance at 12 discrete wavelengths in visible and near-infrared spectra. The system was tuned for an optimal distance from the sensors to the solution and an enclosure was printed to maintain the distance, as well as to avoid natural light interference. Extracted chlorophyll-a solutions of 52 different concentrations were prepared, and at least 5 readings per sample were taken using the proposed smart sensor system. The ground truth values of the samples were measured in the laboratory using Thermo Nano 2000C. After cleaning the anomalous data, different machine learning models were trained to determine the significant wavelengths that contribute most towards chlorophyll-a measurement. Finally, a decision tree model with 5 important features was chosen based on the lowest Root Mean Square and Mean Absolute Error when it was tested on the validation set. The final model resulted in a mean error of ±0.9 μg/L when applied on the test set. The total cost for the device was around CAD 135. For the next approach, a rapid system has been proposed using electric impedance spectroscopy (EIS) to measure the concentration of chlorophyll-a, extracted into 95%(v/v) ethanol. Two electrodes accompanied with a high precision impedance converter from Analog Device was used for the development of the sensor. The system was tuned for a fixed electrode orientation, effective area, electrode to electrode distance and excitation voltage by studying different relevant experiments. The proposed sensor was calibrated using the impedance of 95%(v/v) ethanol. Extracted chlorophyll solutions of 60 different concentrations were prepared. At least 5 readings per sample were taken using the proposed system from 1.5 kHz to 7.5 kHz. Samples were then analyzed using standard methods by a spectrophotometer (Genesys20) from Thermo Scientific. Study of Pearson coefficient, principal component analysis, variance inflation factor and backward elimination were used to identify the significant features for chlorophyll-a measurement using EIS. Finally, a simple linear regression model with 11 important features in the range 2.3kHz to 4.7kHz was chosen based on the lowest Root Mean Square (RMS) and Mean Absolute (MA) Error. The coefficient of determination, R2 of the fitted model was 0.93. MAE for the final proposed model is ±0.904 μgL-1 when applied on the test set

Analysis of Human Affect and Bug Patterns to Improve Software Quality and Security

The impact of software is ever increasing as more and more systems are being software operated. Despite the usefulness of software, many instances software failures have been causing tremendous losses in lives and dollars. Software failures take place because of bugs (i.e., faults) in the software systems. These bugs cause the program to malfunction or crash and expose security vulnerabilities exploitable by malicious hackers. Studies confirm that software defects and vulnerabilities appear in source code largely due to the human mistakes and errors of the developers. Human performance is impacted by the underlying development process and human affects, such as sentiment and emotion. This thesis examines these human affects of software developers, which have drawn recent interests in the community. For capturing developers’ sentimental and emotional states, we have developed several software tools (i.e., SentiStrength-SE, DEVA, and MarValous). These are novel tools facilitating automatic detection of sentiments and emotions from the software engineering textual artifacts. Using such an automated tool, the developers’ sentimental variations are studied with respect to the underlying development tasks (e.g., bug-fixing, bug-introducing), development periods (i.e., days and times), team sizes and project sizes. We expose opportunities for exploiting developers’ sentiments for higher productivity and improved software quality. While developers’ sentiments and emotions can be leveraged for proactive and active safeguard in identifying and minimizing software bugs, this dissertation also includes in-depth studies of the relationship among various bug patterns, such as software defects, security vulnerabilities, and code smells to find actionable insights in minimizing software bugs and improving software quality and security. Bug patterns are exposed through mining software repositories and bug databases. These bug patterns are crucial in localizing bugs and security vulnerabilities in software codebase for fixing them, predicting portions of software susceptible to failure or exploitation by hackers, devising techniques for automated program repair, and avoiding code constructs and coding idioms that are bug-prone. The software tools produced from this thesis are empirically evaluated using standard measurement metrics (e.g., precision, recall). The findings of all the studies are validated with appropriate tests for statistical significance. Finally, based on our experience and in-depth analysis of the present state of the art, we expose avenues for further research and development towards a holistic approach for developing improved and secure software systems

Day of the Week Effect on Stock Return and Volatility: Evidence from Chittagong Stock Exchange

The study focuses on examining the stochastic process of return distribution in the Chittagong stock exchange (CSE) to deliver persistency of weak form of efficiency and time varying risk -return association for an emerging country like Bangladesh. This study used daily series of market index (CASPI) data over the period from January 1st 2004 to September 30th 2014.The OLS, GARCH (1, 1) regression and GARCH (1, 1) with dummy variable models are employed to identify the existence of the day-of-the-week effect on stock market returns and volatility. The empirical findings attained from the models verified that the day-of-the-week effects on stock returns and volatility are persistent in the stock market. Specifically, a negative effect is observed for Sunday while a positive effect occurs on Thursday. Moreover, the highest volatility occurs on Sunday and lowest volatility found in Thursday. All statistically significant results confirm the absence of weak form of efficiency in Chittagong stock exchange in Bangladesh. Key word: Day-of-the-week effect, stock returns, volatility, GARC