Seminar Report on Wireless Charging Of Mobile Phones Using Microwaves

Introduction to Wireless Charging Of Mobile Phones Using Microwaves Seminar Topic:

The fact that mobile phones work on batteries makes them vulnerable to charging once the batteries get drained. The rate at which batteries drain depends on the manufacturers of both batteries and mobiles. Batteries get drained as we talk, how it would be if we reverse this process. Using microwaves mobile phone can be recharged.

The mobile towers transmit microwave with the message/data signal. These microwaves can be used to charge the mobiles by using a guided antenna at a frequency of 2.45 Mhz, sensor, Rectenna and a filter circuit which can be fitted to the regular handset. With this setup the phone automatically gets charged as you talk.

Microwaves are most suitable for data transmission because they can penetrate any obstacles and in all weathers. Microwaves are used in remote sensing, data transmission from space, communication industry and cooking. These radiations are sometimes considered as harmful. We use S band spectrum which is license free and reserved especially for industrial, scientific and medical purposes and also for Bluetooth and wireless LAN.

Magnetron is a device which uses both magnetic field and electric field is produced perpendicular to each other thereby magnetic field is constantly applied along the axis of the device.

A rectenna is a special type of rectifying antenna which converts microwave energy into DC current. We can achieve 90% efficiency using this antenna. This antenna can be constructed by placing a schottky diode between dipoles of antenna. A sensor simply senses when a mobile is used for talking.

The advantages are elimination of different types of chargers; one microwave transmitter serves all service providers in that area and recharging of mobiles without any wires. Few disadvantages are ill effects on human body and not much advancement in practical implementation commercially. 

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Seminar Topic on Wi-Max with Seminar Report

Introduction to Wi-Max Seminar Topic:

The need of the hour is high speed internet provided by both broadband and wireless. Wi-Max technology paves way for wireless access to broadband internet. Because of broadband being expensive and limited to certain areas and Wi-Fi’s small hot spots technocrats are moving to Wi-Max in a big way.

Wi-Max stands for Worldwide Interoperability for Microwave Access and IEEE standardized it by 801.16. Unlike Wi-Fi the Wi-Max covers an area up to 50 Km. It can provide data rate of 70 Mbps which can service few hundred homes with broadband internet. Just like migrated from landlines to mobiles it’s a matter of time people migrates from cable, DSL internet to Wi-Max. Wi-Max provides universal internet access because its architecture of point to multipoint way can reach to places where wired internet cannot. It can be bridged with wireless LAN.

Wi-Max system is made of two important parts Wi-Max Tower and Wi-Max Receiver. Wi-Max tower is just like a mobile tower which covers around 80 Sq.Km. Wi-Max receiver is just like a Wi-Fi access point.

Working: – A Wi-Max tower can be directly connected to high bandwidth internet using T3 line and two Wi-Max towers can be connected using line of sight microwave link. Each tower can cover an area of 30 Sq.miles and provide broadband connectivity. A receiver can be connected to the tower using non-line of sight similar to Wi-Fi sort of service that can be operated at lower frequencies or through a line of sight where higher speeds can be achieved because its operated at higher frequencies.

IEEE 802.16 specifications are range of 30 miles from tower, speed of 70 Mbps, non line of sight connectivity, dual frequency bands of w-11 Ghz and 10 to 66 Ghz, both MAC and PHY layers are defined and permits multiple PHY layer specifications. 

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Project Report on Web Mining

Introduction to Web Mining Project:

World Wide Web (WWW) is a made of millions of websites of multiple genre incorporated with trillions of tetra bytes of data on their web pages. There is will be common data within few web pages and when we want to search for required information on web at times it will be difficult. The process of extracting required information from web is called as data mining. Similarly finding required web sites or web pages from WWW is called as web mining. Web mining helps in finding text documents, multimedia files, images and other types of resources from web pages.

The areas of web mining applications are E-Commerce, Information filtering, Fraud detection and Education and research.

The algorithms and techniques used for data mining can be applied in web mining because it is just an extension of data mining. The tasks involved in web mining are retrieving of documents then extraction of information from the documents using Web miner software and finding similar patterns in web sites and finally validating the extracted information.

Some of the issues that come into play with web mining are saving of a page in a web site or saving entire web site by downloading it in to desktop. This helps in analyzing the visitors to a web site.

Some of the important web mining software’s are Sinope Summarizer, Teleport Pro, and Click Tracks.

In order to analyze online web content web mining software should navige between many web sites and during this process two important categories of web mining comes up, they are web content mining and web usage mining.

There are few disadvantages in web mining such as long time to explore large volume of data, loss of communication link, and dynamic nature of web data, interconnection of web pages by hyperlinks may lead to infinite loop and hidden web data cannot be located.

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Seminar Report on Wearable Computers

Introduction ot Wearable Computers Seminar Topic:

Cellonics Technology Modems

All communications such as LAN, WAN, wired Internet, wireless internet, telecommunications work using modems. Modem is basically a device which modulates and demodulates carrier signal with digital information. The speed of data transfer depends on the speed of modem capacity. Hence there is a need to develop high speed modems to cater for present days technologies where millions of terra bytes of data will be transferred every second. Cellonics is the technology which will solve our problems.

Cellonics technology modems are based on biological cell communication system which is 1000 times faster than traditional modems. This technology is simple, robust and user friendly.

As this technology is based on human cell behavior it is studied that human cells generates a waveform in response to actions these wave forms are serial pulses separated by periodic silence. Cellonics technology duplicates the same process and applies to communications.

Working of Cellonics: – this element takes slow analog signal as input and in gives predictable, fast pulse as output thereby encoding digital information and transferring it over communication channels. In building Cellonics Nonlinear Dynamical Systems (NDS) employs mathematical formulations which are needed to simulate the cell responses. Being nonlinear system the performance exceeds the normal limits and simultaneously simplifies implementation process. Cellonics technology is mostly used in service industry of telecommunications.

There are many advantages in using Cellonics in communication systems. It saves chip/PCB real estate there by simplifies receiver implementation by 4 times. Savings on power by 3 times because of using few components that are passive and consumes less power. Savings on implementation time is possible because of elimination of many sub components such as amplifier, mixer, PLL, oscillator, filter, crystal quartz, etc. in order to build your product which is small, powerful, long on power and low cost employing or using Cellonics technology is ideal.

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Incorporating Energy Maps to Measure and Compare the Coherence Time and Spreading Period across Mobile Wireless Networks

MSc Project Proposal Form

Project Title: Incorporating Energy Maps to Measure and Compare the Coherence Time and Spreading Period across Mobile Wireless Networks 

Aims:  To incorporate the energy maps across the mobile wireless networks for optimal energy consumption and to compare the QoS metrics with the help of coherence time and spreading period. 

Objectives:

Following are the research objectives:- 

  • To understand the concept of energy consumption across the nodes of mobile wireless networks and how to optimize the energy consumption.
  •  To document the critical analysis of the energy optimization techniques and QoS metric across the mobile wireless nodes.
  • To design and propose a path integration algorithm that can integrate the energy maps across the mobile wireless networks.
  • To calculate coherence time and spreading period of the QoS metrics considered and compare them with respective to the energy maps across the nodes.
  • To develop a dot net based code to demonstrate the proposed algorithm practically.
  • To evaluate the system developed and document the observations. 

Project Deliverables: I will make use of both qualitative and quantitative methods of research to proceed with this project. I will study the concepts of energy maps using qualitative methods and develop the proposed algorithm using quantitative methods of research. And Literature review on mobile wireless networks and the importance of coherence time and spreading period across them with the limitations of existing systems. 

 Keywords:

  • Energy maps
  • Spreading period
  • Mobile wireless networks
  • Coherence time. 

Name of supervisor : 
Supervisor’s comments :
Supervisor’s signature :    
Suggested 2nd Supervisor :  
Student Name : 
Student ID
Student Signature :
Student e-mail :                                                        
Month and Year of start : 

Of your programme.

Customer Satisfaction and Quality Perceptions of both Private and Public Sector Banks

Conclusion evaluation of hypothesis

 

T- In order to evaluate the mean of two populations, test is appropriate.

Through the level of significance and three degrees of freedom as 0.5, the tabulated value is 0.878.

By means of similar conditions, the value calculated is 0.706591.

Since the value calculated is less when compared to the tabulated value i.e. 0.726591 < 0.878, this indicates that there is major differences among the customer satisfaction and quality perceptions of both private and public sector banks and hence the alternative hypothesis is satisfactory.

Conclusion and Policy Implications

InIndia, due to the initiation of technology and competition, the banking sector is undergoing significant changes. A superior quality services has been observed by the customer, which improves their satisfaction. From different findings of research, the basis of this study is derived and also by concerning customer satisfaction through other researcher’s it is in line with empirical findings. To summarize, the following results directs to the policy implication and conclusion.    

  • The satisfaction of customer in conditions of quality of service is defined as a relational marketing model. From the viewpoint of services produced by the firm, the relationships are examined mostly. For enhancing the satisfaction of customer, it is significant to build a strong connection for service firm like banks using quality of service.
  • Over every service quality dimensions, public sector banks such as SBI drop much below their customer’s perceptions. On service quality’s reliability dimensions and tangibility, private banks like ICICI bank are going beyond their customer’s perceptions.
  • With regard to the customers’ expectations, banks such as ICICI are seemed to be closer. As much as service quality’s other dimensions are concerned, the banks are not so far away from their customer’s perceptions. Certainly, a miserable reality has been revealed which states that SBI do not meet up to their customers’ expectations. In banks, individualized attention, accuracy, reliability, speed, promptness, etc matters lot in the delivery of service quality. With appropriate use of relevant banking technology, enhanced results can be gained. These are the most important areas where Indian banks in still are lagging behind. 
  • Besides communication material and physical facilities, on tangibles mostly tele-banking, computer-based banking, ‘anytime and any-where banking’, intranet and internet services, etc, the significance and need of important investments have been suggested by the above findings. This will help in decreasing the frontline staff’s workload and delivering accurate and quick services to the customers and thus providing employees the ways to respond towards the requests of a customer. Suitable banking hours will also be made certain by this investment on which the bank’s services are supposed to be much low through the customers.  

Analysis and Findings of SBI an Evaluation of Performance Compare To Other Indian Banks

Critical Appraisal of Literature

       The service quality as well as its dimensions perceptions are concerned by the major analysis and questioning areas such as assurance, reliability, empathy, tangibility and responsiveness. On the seven point scale of strongly agree to strongly disagree, the perceptions are measured. For ICICI and SBI banks, among the banks and its respective customer’s service quality perceptions, mean differences are calculated individually. In below tables, the results are represented which are acquired from this computation.  

Analysis of Data

          Generally along with their respective customer’s, service quality presents an extensive perceptual difference between Indian (public sector) banks but in private banks an assumed perceptual difference is constricted this whole analysis is shown in the below table.

Table: Comparative perceptions of banks and their Respective Customers about Overall service Quality in Banks.

SERVQUAL Dimension State Bank ofIndia ICICI Bank
Group Mean Group Mean
Overall Service BO 133.52 BO 142.62
Quality BC 113.86 BC 137.36

Through the quality of service which seems by their respective customers, SBI distribute the most important differences in quality of service this is explained by SBI’s (20.68) high mean difference.

         Banks distribute the service quality, for example, particular customer expectations are not coordinated by SBI.  For customers to distribute the quality of service ICICI bank is also below its assessment, however comparing ICICI with SBI the perceptual difference of ICICI is narrow (4.91).

 Findings from Analysis:

   Tangibility: In the table below, in the perceptions of ICICI and SBI Banks a large difference was brought to light by the data on tangibles through their relevant customers. While SBI by means of a large mean difference of 5.28 falls much below the customer’s perceptions on the dimension of quality of service, the date informs that banks like ICICI are exceeding their relevant customer’s perceptions. Between the banks such as SBI as perceived by their relevant customers a serious loss of perceptions were shown by the study of tangibility’s element wise on physical facilities and up to data equipments available in the bank. 

Table: Comparative Perceptions of Banks and their Respective Customers about Tangibility

S. No.         SERVQUAL                                 Dimension            State Bank ofIndia     ICICI Bank
     Group        Mean Group Mean
 

1

 

Up to date equipment

BO 5.63 BO 6.24
BC 4.10 BC 6.53
 

2

 

Physical facilities

BO 5.35 BO 6.28
BO 3.47 BC 6.58
 

3

 

Neatness of employees

BO 6.16 BO 6.97
BC 5.18 BC 6.95
 

4

 

Communication material

BO 5.99 BO 6.31
BC 5.11 BC 6.89
  Tangibility (1  2   3  4) BO 23.13 BO 25.79
BC 17.84 BC 26.93

Note: BO and BC denotes Bank officials and Bank customers respectively.

Bank   SBI ICICI
Numbers BC 75 75
BO 30 30

 Reliability: In the SBI perceptions, an important difference was shown by the study of quality of service of reliability dimension through their relevant customers. In distributing the services of a quality, SBI illustrates that they fall below the customer’s expectations, while ICICI bank in this dimension is going beyond their customer’s perceptions. It is being shown by the element wise study of reliability that as far as being sincere and keeping promise in solving the problems are concerned, SBI is found to be far below their relevant customer’s perceptions.   

Responsiveness: Upon the service quality responsiveness dimension, there are important perceptual differences with their customers where the data is brought into consideration in the below table. The perceptions of bank customers are far below on supposed dimension which is shown through high mean difference of SBI (3.65). Among its customers and ICICI bank, the perceptual difference is narrow (0.66) on supposed dimension. Below the perceptions of their customers, SBI is falling on employees by offering prompt services in case of element wise analysis of this dimension .

 Table: Comparative Perceptions of Banks and their Respective Customers about Responsiveness

S. No.        SERVQUAL Dimension State Bank ofIndia ICICI Bank
Group Mean Group Mean
1 Telling customers exactly when the services will be performed BO 6.11 BO 6.21
BC 5.39 BC 6.33
2 Employees providing prompt service to customers BO 5.86 BO 6.21
BC 4.65 BC 6.11
3 Employees who are always willing to help

 

BO 6.10 BO 6.79
BC 5.18 BC 6.33
4 Employees who are never too busy to respond to customers requests BO 5.31 BO 6.28
BC 4.52 BC 6.04
5 Responsiveness (10 11 12 13) BO 23.38 BO 25.48
BC 19.73 BC 24.81

 Note: BO and BC denotes Bank officials and Bank customers respectively

Bank   SBI ICICI
Numbers BC 75 75
BO 30 30

  Assurance: Apparently from the mean difference, among its customers and ICICI the perceptual difference is high. Regarding the assurance dimension to the bank, the persons of SBI specified a low rating. The perceptions of SBI customers are exceeded by ICICI in case of feeling safe and trust worthiness in transacting with the concern bank.

Empathy:  Regarding the quality services delivery, the SBI banks move away from their customers which are revealed in the table of data analysis. Apparently from the high mean difference (5.50), a large gap is existed among the perceptions of banks like SBI and their customers. Below the perceptions of their customers (4.09), the ICICI is declining on this dimension. 

 Table 5: Comparative perceptions of Banks and their Respective Customers about Empathy

S. No. SERVQUAL Dimension State Bank ofIndia ICICI Bank
Group Mean Group Mean
1 Bank  that gives individual attention BO 6.02 BO 6.28
BC 5.28 BC 6.29
2 Convenient operating hours BO 6.23 BO 7.00
BC 4.85 BC 6.88
3 Employees who give personal attention BO 6.16 BO 6.21
BC 5.17 BC 6.06
4 Bank which has your best interests at least BO 6.24 BO 6.62
BC 4.99 BC 5.15
5 Employees who understand specific needs of the customer BO 5.98 BO 6.62
BC 4.84 BC 5.15
6 Empathy (18  19  20  21 22) BO 30.63 BO 33.10
BC 25.13 BC 29.02

 Note: BO and BC denotes Bank officials and Bank customers

Bank   SBI ICICI
Numbers BC 75 75
BO 30 30

 Interpretation: 

  1. What is your occupation? 
House wife 9
Student 0
Business man 15
Government Employee 22
Other 0

 Interpretation: – The pupils here are Housewives, students, Businessman, Government employees and other. The total number of people is 46. In that housewives are of 9, Students are 0, Businessman is of 15 and Government employees are of 22. And to the category of others, no one of the people is belonged. Finally four of the persons do not answer. 

  1. Since how many years you are related with SBI bank?
1-5 years 24
More than 5 12
Less than 1 year 10

 Interpretation:  In total 46 persons, less than one year 10 persons are connected with bank, from more than five years 12 persons are connected with bank and since one to five years 24 persons are connected with bank.

  1. How do you come to know about the home loan schemes of SBI bank?
Internet 10
Television 14
Newspaper 18
Other resources 4

 Interpretation:  Out of 46 persons, from internet, television, newspaper and other resources nearly 10, 14, 18 and 4 persons respectively came to know about the bank. 

4. Are you aware of these types of home loans? 

Home equity loan 4
Land purchase loan 9
Home construction loan 6
Home purchase loan 9
Home improvement loan 18

 Interpretation:  Regarding the Land purchase loan only 9 persons knew, Home equity loan only 4 persons knew, Home purchase loan only 9 persons knew, Home improvement loans 6 persons knew and finally about Home construction loan nearly 18 persons knew out of 46 persons.

 5. Are you aware all terms and conditions of home loans?

No 6
Yes 40

 Interpretation:  Except 6 persons nearly 40 persons knew about all home loan terms as well as conditions out of 46 persons.

 6. Are you satisfy with the interest rate charges by your bank? 

Agree 30
Disagree 4
Strongly agree 12
Strongly disagree 0

 Interpretation:  The total number of persons is 46. Out of these persons, the bank interest rate is agreed by 30 consumers, disagreed by 4 consumers, strongly agreed by 12 consumers and none of the consumers did not disagreed with it strongly. 

7. Which type of services is offered by your bank?

Net banking 15
Forex banking 7
Mobile banking 24

 Interpretation:   From 46 persons, 15 persons say that the bank presents net banking services, 24 person’s state that it presents mobile banking services but only 7 persons assumed that it presents forex banking services.

8. Your bank loan processing is fast, do you agree that? 

Agree 26
Disagree 9
Strongly agree 8
Strongly disagree 3

 Interpretation: Out of 46 persons, the bank processing is fast is agreed by 26 persons, disagreed by 9 persons, strongly agreed by 8 persons and strongly disagreed by 3 persons.

9. When compared to other banks, do you satisfy with the after home loan services offered by your bank are best?

Agree 30
Disagree 4
Strongly agree 12
Strongly disagree 0

 Interpretation:  The total number of persons is 46. In those persons, after sale the bank services are agreed by 30 consumers, disagreed by 4 consumers, strongly agreed by 12 consumers and strongly disagreed by none of the consumers. 

10. According to your demand does the home loan cost is appropriate? 

No 13
Yes 33

 Interpretation:  Out of 46 persons, according to their demand that the home is loan appropriate is agreed by 33 persons and remaining 13 persons not agreed it. 

11. The bank employee behavior is satisfactory or not? 

Agree 23
Disagree 4
Strongly agree 19
Strongly disagree 0

 Interpretation: In total 46 persons, the bank employee behavior is satisfied by 23 persons, not satisfied by 4 persons. And very satisfied by 19 persons and finally no one disagreed with the bank employee behavior.

12. Upon loan services did bank gave any discount? 

No 6
Yes 40

 Interpretation:  The total number of persons is 46. In those persons, nearly 40 persons said that discount is given upon loan services by the bank and remaining 6 persons said that the bank did not offer any discount.

13. Are you satisfied by the time consumed in loan sanctioning? 

No 12
Yes 34

 Interpretation:  Out of 46 persons, the time taken while sanctioning a loan is satisfied by the 34 persons and not satisfied by 12 persons.

14. While taking the loan did you face any difficulty? 

No 7
Yes 39

 Interpretation:  The total number of persons is 46. In those persons, except 7 persons remaining 39 persons faced difficulty while taking the loan. 

15. Which grade you want to give off to the bank home loan schemes?

Average 4
Good 18
Excellent 24
Below average 0

 Interpretation:  The total number of persons is 46. In those, only 4 persons gave average grade to bank, 18 persons gave good grade to bank, 24 persons gave excellent grade to bank and none of them gave below average grade to bank.

16. Do you have all the documents which are required to open an account?

TABLE

Sr. No. Category No. of people Percentage
1 Yes 120 60%
2 No 80 40%
  Total 200 100%

                                                                                                         Base 200 people                       

Interpretation

From the table and graph above it can be seen that

  • In order to open an account in the SBI bank, 60% of people have all the documents which are essential.
  • In order to open an account in the SBI bank, 25% of people don’t have all the documents which are necessary.    

17. What is your annual household income?

TABLE

Sr. No. Category No. of People Percentage
1 Less than 2 lacs 98 49%
2 Between 2 to 5 lacs 62 31%
3 Between 5 to 8 lacs 30 15%
4 More than 8 lacs 10 5%
  Total 200 100%

                    Base 200 People

Interpretation

  • An annual household income of 49% people is less than 2 lacs.
  • Annual household income of 31% people is between 2 to 5 lacs.
  • Annual household income is between 5 to 8 lacs of 15% people.
  • An annual household income of 5% people is more than 8 lacs. 

18. Regarding various services or products provided by SBI bank, what is your opinion?

TABLE

Sr. No. Category No. of People Percentage
1 Lucrative 50 25%
2 Not Lucrative 120 60%
3 No idea 30 15%
  Total 200 100%

Base 200 People

Interpretation

From the above table and graph it can be seen that

  • Regarding different products 25% of people’s opinion is lucrative.
  • In relation to products, 60% of people’s opinion is not lucrative.
  • 15% people have no idea. 

19. To continue an account at SBI Bank, are you aware of various conditions and terms which are very much necessary? 

TABLE

Sr. No. Category No. of People Percentage
1 Yes                   25 12%
2 No 175 88%
  Total 200 100%

                                                                                                          Base 200 respondents

Interpretation

It can be seen from the above graph and table that

  • In order to continue an account with the bank, only 12% people were familiar through the conditions and terms that are very much necessary.
  • While, 88% people have no idea regarding it.

20. In relation to sales executives, do you know regarding SBI Bank’s recruitment policies?

TABLE

Sr. No. Category No. of People Percentage
1 Yes 82 41%
2 No 118 59%
  Total 200 100%

                                                                                                          Base 200 respondents

Interpretation

It can be seen from the above graph and table that

  • In relation with sales executives, only 41% people are well-known regarding recruitment policies of SBI Bank.
  • Whereas, in relation to sales executives 59% of the people are not well-known regarding the recruitment policies of SBI Bank.

Empirical Specification of banking sector in india

EMPIRICAL SPECIFICATION: 

From Specification,   reduction of the following standard is approximately calculated by an empirical strategy.

 IR= a+b ln (PF)+c ln (PL)+d ln(PK)+e ln (SIZE)+f ln (CRAR)+g ln (LNASST)+h ln (BR)+ i (D5) ………3

Where

IR is interest revenue (net of income on CRR) scaled by total assets

PL is personnel expense to the total number of employees (proxy for unit price of labor)

SIZE is natural logarithm of the total assets,

LINASST is the ratio of loans to total assets,

D5 is dummy variable for total assets (one for the five largest banks, zero elsewhere),

­ denotes the natural logarithmic operator,

PF is average funding rate calculated as the ratio of aggregate interest expenses to total deposits plus borrowings (proxy for unit price of labor).

PK is other operating costs to fixed assets (proxy for unit price of capital),

CRAR is the ratio of capital to risk-weighted assets, and

BR is the number of branches to the total number of branches.

              By following an equation (3) H statistics is equals to b, c and d i.e.; H=b + c + d. When there is rise in costs mutually with an equal amount, total revenues and marginal costs are increasing while there is an increase in the input price, under perfect competition. Reducing of total revenues, marginal costs and reducing of equilibrium output are augmented by input prices, when under monopoly there is an increase in input prices. 

                                 Table: Interpretation of the Panzar-Rosse H Statistic 

H-value Interpretation
H ≤0 Monopoly or perfectly collusive oligopoly
H<1 Monopolistic competition
H=1 Perfect competition, natural monopoly in a perfectly contestable market, or sales-maximizing firm subject to a break-even constraint

 Particularly it is very interest to understand that is in cost figures how total revenues react towards variations this is the main opinion of H statistic in environment.  Banks central business is financial intermediation, first of all as a dependent variable, interest revenue is considered. The relation is a total income in the share of non-interest income is increasing, through total revenue (TR), interest revenue (IR) is restored then the revenue function’s alternative specification is evaluated where it is defined as aggregate of interest revenues and non-interest revenues. 

                To consider the bank-specific features, control variables are initiated. For aggregate demand and bank size, proxy is measured by the natural algorithm of total asset (SIZE). For the purpose of risk-return profile, the size is also controlled in comparison with the cost differences; therefore the coefficients sign that is ex ante is not definite.

                 To consider the firm-level risk, there is an integration of loan-to-total-asset (LNASST) ratios and capital to risk-weighted assets ratios (CRAR). The coefficient which is based on former differs as positive or negative which depends on high range of risk that may lead to low revenue or high revenue of bank. When the revenue of the bank becomes high due to the reason of increased loans based on the assets, the coefficient which is on later becomes positive.

              On revenues, to calculate an outcome of bank size one more proxy which is useful is signified towards total number of branches (BR) by the ratio of many bank branches and therefore possible scale economies and discovered. The above mentioned ratio becomes negative or positive based on the branch networks and the differences between those particular banks which in turn affect the bank revenue.  In banks, particularly state-owned banks are having a large branch network that is the reason in Indian context variable is very useful.

                  To decide five largest banks, an additional dummy variable D5 is used: D5 must be significant, at present oligopoly power is having link with large size 

EMPIRICAL RESULTS

              In the long-run equilibrium phases, banks are operated in which income should be correlated statistically with input prices. In order to test the H=0 hypothesis, ratio of net income to total assets are specified in eq (3). PR (Panzar Rosse) test and long-run equilibrium are meaningfully interpreted and the results are not discarded at the 5 percent level of the market equilibrium condition. Descriptive summary of variables as specified by the eq (3) are presented in the below table (Descriptive summary of variables (1996-2004).

Descriptive summary of variables (1996-2004):

Variable Mean Standard Deviation Minimum Maximum
Ln (SIZE) 8.870 1.415 4.054 12.919
Ln (PF) 1.920 0.272 -0.199 2.693
Ln (PL) 0.832 0.625 -0.627 3.841
Ln (PK) 5.152 0.803 2.313 7.026
Ln (CRAR) 2.379 0.658 -6.908 5.088
Ln (BR) -0.940 2.065 -5.599 2.917
Ln (Wages/ total asset) 0.192 0.655 -2.135 1.359
Ln( interest revenue) 6.465 1.361 1.764 10.341
Ln ( total revenue) 6.648 1.365 1.880 10.545

 

                          Descriptive summary of Variables four features are Mean, Standard Deviation, Minimum and maximum are most important. High variability is exhibited initially through both bank networks and bank size, and by the RBI rationalization policy of banks is affected.  Second, by means of interest revenues, total revenues are determined, as is obvious from the close correspondence among standard deviation and mean of two variables. Third, capital prices remain high, on average, over the period.     

                In the public sector banks operating expenses of banks are high, which results with the low variability labor costs are cheap, which is the strength of the banking system.  Total revenues and share of interest revenues are depicted in the below figures as a result of total assets in the form of percentages across bank groups. In 2002, the new private bank groups record the shares of interest revenues which are significant. 

    SoB: State-owned Banks; NPVT –New Private Banks. 

    OPVT- Old Private Banks; Foreign- Foreign Banks. 

    SoB: State-owned Banks; NPVT –New Private Banks. 

    OPVT- Old Private Banks; Foreign- Foreign Banks.

         The achievement of the financial sector reforms in India is broadly divided into two phases. First generation reforms is generally labeled as 1992-99 and it is known as first phase. Potential reforms, diversification and prudential reforms are guided by the first phase. Second generation reforms are perceived during 1999 and this phase is called as second phase and by means of structural reforms implementation of financial sectors are consolidated.  Below figure shows the economic results of Indian Commercial Banks over the period of 1996-2004.  

                        Estimated value of H is always considerable as non-negative. According to the PR classification Indian banking sector is distinguished by monopolistic competition and the suggested results are observed at specific period. At the one percent level, the unit cost of funds (PF) has a positive symbol which has statistically important. And also at the conventional levels PL turned to be significant. In the explanation of interest revenues (and therefore to the H statistic), the price of funds offers highest involvement during the period of 1996-2004.    

                    The entire Control variables have positive signs and which are significant. Therefore revenues are positively related to bank size. Interest revenues are increased which allows significant capital ratios. In the below table interest revenues are showed between the periods of 1996-2004. During the second sub period H statistics are increased marginally compared to the first, while in the both sub periods H statistics are differ from both 0 and 1.      

                 During the second sub period PK coefficient is important whereas the coefficient of PF also uniformly significant across both the sub periods at the rising importance of the price of capital. Due to the two factors like manpower rationalization and increasing technological sophistication, PL turns to be insignificant particularly at the state-owned banks across the second sub period.

Estimation results for the Indian Commercial Banks: Interest Revenue, 1996-2004. 

Dependent Variable = Ln (Interest Revenue/Total Assets)
  1996-2004 Sub-period 1, 1996-99 Sub-period 2, 2000-04
  Model 1 Model 2a Model 2b
Constant -3.181 (0.140)* -3.227 (0.173)* -3.424 (0.189)*
Ln PF 0.438 (0.024)* 0.382 (0.030)* 0.449 (0.029)*
Ln PL 0.052 (0.018)* 0.086 (0.029)* -0.004 (0.024)*
Ln (SIZE) 0.979 (0.012)* 0.959 (0.014)* 0.985 (0.016)*
Ln PK 0.021

(0.012)***

0.0007 (0.013) 0.042 (0.016)*
Ln(LNASST) 0.007 (0.006) 0.872 (0.077)* 0.002 (0.0005)
Ln (CRAR) 0.014

(0.008)***

0.030 (0.014)** 0.021 (0.009)*
Ln (BR) -0.015(0.009) 0.026 (0.012)** 0.005 (0.012)*
D5 0.059

(0.039)***

0.032(0.047) 0.018 (0.043)
H statistic 0.407 0.469 0.487
F-value on Wald test for H=0 149.32a 125.52a 147.96a
F-value on Wald test for H=1 316.11b 160.47b 163.09b
Diagnostics      
Adjusted R-square 0.989 0.994 0.989
Number of banks 64 64 64
Total panel observations 570 252 318

 *, ** and *** indicate statistical significance at 1, 5, and 10%, respectively 

Significantly is different from 0 to 5% level. 

Significantly is different from 0 to 5% level. 

            In current years, the bank’s non-interest revenues have raised drastically which can be distinguished. The banks like old private and state owned banks, in terms of augmenting their fee incomes leaned to insulate after their new foreign and private counterparts historically and considerable progress is made in this regard. In 1992 from a level of 8-10 percentages, the non-interest revenues share like a percentage of total revenues is doubled for the banks of public sector to 18-20 percentages at end of the year 2004.

The alternate revenue function specification can be estimated in order to get the report of non-interest revenues increasing share in total revenues. The TR (total revenue) is defined as the “aggregate of non-interest revenues plus interest revenues net of CRR income” when a dependent variable like interest revenue is replaced. In the above given table the results are displayed. For the relevant related financial services, between the regressors the interest to non-interest income ratio to account is incorporated additionally for dissimilar elasticity of demand.

Table: For the Indian Commercial Banks the estimated results: Total revenue from    1996 to 2004

Dependant Variable = In (Total Revenue/Total Assets)
       1996-2004 Sub-period 1,1996-99 Sub-period 2, 2000-2004
  Model 3 Model 4a Model 4b
Constant -2.820(0.143)* -2.896(0.184)* -3.003(0.189)*
In PF 0.367(0.024)* 0.375(0.031)* 0.367(0.030)*
In PL -0.024(0.019) 0.082(0.029)* 0.024(0.023)
In PK 0.025(0.012)** -0.0009(0.012) 0.053(0.017)*
In(SIZE) 0.982(0.011)* 0.965(0.015)* 0.981(0.016)*
In(LNASST) 0.006(0.005) 0.866(0.079)* 0.0009(0.005)
In (CRAR) 0.013(0.0007)*** 0.031(0.015)** 0.018(0.008)*
In (BR) -0.023(0.009)* 0.018(0.012) -0.004(0.012)
In (interest income/ non-interest income) -0.065(0.017)* -0.111(0.022)* -0.075(0.020)*
D5 0.055(0.036) 0.033(0.049) 0.026(0.042)
H statistic 0.0368 0.456 0.444
F-value on Wald test for H=0 128.04a 113.55a 132.91a
F-value on Wald test for H=1 377.16b 161.19b 207.93b
Diagnostics
Adjusted R-square 0.990 0.994 0.989
Total Panel observations 570 252 318
Number of banks 64 64 64

 At 1, 5 and 10%, the *, ** and *** indicates the statistical significance respectively

Significantly a different from 0 at 5% level

Significantly b different from 0 at 5% level 

              The earlier findings are explained in the above given table. The H statistic value is lower when compared to the earlier but it is positive for the entire period and confirms the monopolistic free entry equilibrium presence. Similar to that of earlier model, during the second sub period the second most contribution is made to the H statistic by the capital price. Throughout the first sub period the contribution of labor price is significant but when moved to the second sub period it is insignificant.  But the second sub period can turn into significant with the help of bank size, capital adequacy and control variables for the whole period that is, similar to that of the earlier, the magnitudes are of same order approximately.

A negative sign is shown on a particular interest in case of interest to non-interest income ratio.  When the fee incomes of a bank are increasing, the bank’s interest income falls in an era of benevolent interest rates economically and this finally makes on government securities higher treasury incomes. For this reason, the interest to non-interest income ratio became lesser probably. By means of higher total revenues, the declining of ratio is linked. In the second sub period, the marginal decline in H statistic is signified by the sub period analysis. Like in earlier specification many variables of control maintain their significance as well as sign. During the second sub period, the raising importance of capital cost is revealed by the analysis that relies on sub periods, in affecting the H statistic. 

Table: For the Indian Commercial Banks the estimated results: Interest revenue by Type of Bank in the period 1996 to 2004. 

  Public Sector Banks New Private & Foreign Banks
Constant -1.298(0.253)* -3.664(0.325)*
In PF 0.161(0.023)* 0.507(0.042)*
In PL 0.059(0.025)** -0.013(0.039)
In PK 0.004(0.015) -0.006(0.029)
In (SIZE) 0.821(0.023)* 0.981(0.030)*
In (LNASST) 0.001(0.003) 0.967(0.124)*
In (CRAR) 0.062(0.016)* 0.012(0.014)
In (BR) 0.168(0.026)* -0.010(0.031)
H statistic 0.224 0.488
F-value on Wald test for H=0 40.60a 61.54a
F-value on Wald test for H=1 487.25b 67.80b
Diagnostics
Adjusted R-square 0.991 0.969
Number of banks 27 20
Total panel observations 243 174

 At 1, 5 and 10%, the statistical significance is indicated as *, ** and *** respectively

Significantly a different from 0 at 5% level

Significantly b different from 0 at 5% level

                 The weighted least squares (WLS) approach can be utilized for re-estimating the baseline specification (3). Here, in a particular year the banks weigh the observations with respective asset shares. The procedure of WLS is well suitable instinctively for capturing the representative banks behavior through offering high weight in case of bigger banks. Many variables contain counter instinctive signs and no extra significant insight is given by the results. Mainly for a cross country framework, the methodology of WLS is most suitable.

For various Latin American countries, the Levy Yeyati and Micco in the year 2003 in their study of bank competition reported, based on that across the countries the number of reporting banks vary. In support of dissimilar types of banks the results will be estimated.  The foreign as well as new private banks related business orientation is specially given and these two banks are grouped as one for the whole sample period in order to estimate the baseline model. The contribution of funds price is highest for the H statistic followed by the labor price in case of public sector banks which is clearly illustrated in the above table.

To H statistic, the funds price forms the most significant contribution in case of other group of banks.  In influencing the H statistic, the resultant high wage bill as well as in public sector banks the huge manpower is considered as most important because this is intuitive.  For public sector banks, the CRAR is concerned as the main which signify that for foreign banks the capital adequacy is not a key concern. When compared the foreign and private bank groups with the public sector group, the H statistic in terms of magnitudes is higher for foreign as well as private banks. The values of H statistic are dissimilar significantly from 0 and 1.

        The approximation in the table below also informs that a statistical importance for the group of public sector bank is shown by the price of both capital and labor. Since previous, for the public sector bank the capital adequacy and branch network amongst the control variables were considered significant. On the income ratio of interest to non-interest variable, the negative sign present may involve the rising importance of other incomes as well as non-interest treasury as a part of banks total income. Same as earlier(seen in table above), even though these magnitudes are reasonably lower, the H statistics magnitude practices the same order that is larger for private and foreign banks as compared to the public sector banks.

Overview and History of the Banking Industry in India

Overview of the Industry:

HISTORY:

In India, banking contains a detailed and extended history of about 200 years. After the first bank of the country i.e. Bank of Bengal was launched, the industry’s foundation can be traced back to 1786. However in 1969, by following the nationalization of banks the industry has changed significantly and quickly. Therefore, a vast growth and several positive changes were experienced by the public sector banks. In order to explore the opportunities of new business, the economic reforms and liberalization permitted the banks to do this and from mere lending and borrowing it has not just remain forced to produce revenues. Hence, to get improved with time continuously, the scenario of Indian banking was offered with a significant facelift. But, in the country till today, the nationalized banks are persisted to be the largest lenders, in spite of the foreign banks voyage. This is mainly because of the diffusion of networks and the banks size. The system of Indian banking can be categorized as specialized banking institutions, private banks and nationalized banks.

With banking units of about 30, the industry is divided extremely contributing to approximately 60% of advances and 50% of deposits. In the financial sector of India, one of the leading monitoring bodies is the Reverse Bank of India. It acts as a centralized body in the system, so as to examine the limitations and inconsistencies. Through the estimation of industry it has been specified that, beyond 274 commercial banks that are working in the country, 51 are in the private sector and 223 are in the public sector. 24 foreign banks have been incorporated by these private sector banks, where these foreign banks had started their processes here. From the division of nationalized banks group, the institutions of specialized banking include rural banks, cooperatives, etc.  

Opportunities:

In the present job market, one of the most beneficial options that are considered is the banking sector. An arrangement in Forex or Treasury is being regarded to be accurate on the top of the industry and hence this is pursued as a result of the careers in retail banking, investment banking and private banking. In the industry of banking, one can work in different areas which includes banking officer, loan officer, personal loan officer, home loan agent, mortgage loan underwriter, accountant, customer service executive, recurring deposit account, probationary officer, assessor, home loan officer, loan manager, loan processing officer, and sales executive and product marketing between others.       

Fewer significant jobs in the Financial Services basically comprise of a person called stockbroker who sells and buys securities for some commission on behalf of institutions and individuals. Some brokers work for institutions whereas the others desire to put into practice through individual clients. Those brokers who often work for the investors of an institution are named as security traders.

Most of them wish to work as securities analysts, advisors and dealers. Since the security analysts are estimated to include capital market’s sound knowledge, they provide advice to the companies regarding the shares floatation. For the Financial service sector, the investment analysts are regarded as the backbone. They evaluate different statistical information, compare financial results, profitability projections, examine the whole industry depending on the foundation of information available, study the company’s financial reports, and at last terminate to the result. Same like investment analysts, the equity analysts perform jobs and make calculations and also investigates the equity markets. 

Growth:

In private banks, the boundary for foreign direct investment has been improved to 74% from 49%. Moreover, in private banks for foreign institutional investment the limit is only up to 49%. Within the banking sector and other fragments of financial sector like non banking finance companies, capital markets, mutual funds, venture capitalists, post offices, and etc, a challenging environment has been produced by globalization and liberalization. To their offerings the markets and research has declared the accumulation of “Indian Retail Banking” in 2006. In the Country, the credit growth has been predefined continuously by Indian Retail Banking. To handle Rs. 3,538 billion, the credit growth has been enormously increasing to 44.4% in the year 2005-06.

In spite of, growth in risk weight by means of RBI, there was an additional increase for real estate and housing loans in August, 2005. During the year 2005-06, among the entire retail loans housing that represents more than 52% will develop a strong rate of about 44.35%. To plan future strategies and to recognize the competition and market the Indian banks are being helped, by coming out through an industry approach on Indian Retail Banking. In India the segments of retail banking market has been analyzed, and along with challenges and issues the key trends were also obtained. In the strategies of retail banking space and among itself, 21 major players were being reported. The growth of finance market is decreasing due to the out breakable measures of Reserve Bank of India (RBI).  

State Bank of India Introduction

State Bank of India:

Introduction:

In India, one of the biggest commercial bank is ‘State Bank of India’ (SBI). It commands one-fifth of loans and deposits of each and every programmed commercial bank and also contains an enormous network of about 9000 branches i.e. around 14% of all branches of bank in India.  A network of about, numerous non-banking subsidiaries and eight banking subsidiaries are included by State Bank Group which offers fund management, credit cards, merchant banking services, primary dealership in government securities, insurance and factoring services.

The eight banking subsidiaries include: State Bank of Hyderabad (SBH), State Bank of Travancore (SBT), State Bank of Indore (SBIR), State Bank of Patiala (SBP), State Bank of Bikaner and Jaipur (SBBJ), State Bank of India (SBI), State Bank of Mysore (SBM), and State Bank of Saurashtra (SBS). At present, the State Bank of India across all the time zones included a network of branches and around the world it has widened its arms. By means of its four wings such as the Foreign Offices division, International Services division, the Domestic division and the Foreign Department, the International Banking Group of SBI distributes the cross-border finance solutions in complete range.