System Modeling Concepts - Term Paper Example

Systems Modelling Your name Name of Assignment 22nd December , 2012 Introduction Systems modeling describe a set of system development processes, methods and tools used for designing, re-designing and deployment of solutions to system challenges. The process involves considerations in a number of levels and domains. It emphasizes the building of systems in the right way as well as focusing on selecting the appropriate systems and their interactions with an aim of satisfying design requirements. In addition, it addresses operations and development of monumental products, as well as the development and functionality of evolving programs. Whereas systems modeling have been an ongoing trend, efforts are likely to continue in the definition and development of systems modeling and accomplishments. The process of modeling remains fundamentally a design process and so differs from system analysis. This makes it relevant in the future for the invention of new configurations. Moreover, a strong fascination remains about the creative process of modeling. The process involves expanding systems engineering to purposely accommodate the principles and practices of ecological sustainability. It also promotes working amongst engineers to adopt existing technologies while developing others that are not only sustainable but also meet societal needs. The sustainable development paradigm will definitely affect software deployment trends in systems engineering all to ensure that the sustainability of technologies and processes are acceptable to all users and particularly the environmentally conscious. Modeling a system The system to be modeled is Gaussian Noise detection system which works by detecting pulse signals. We begin by assuming that simple threshold detection scheme where a target has been declared to exist and during sampling time, the threshold (T) is exceeded. Under the conventional assumptions of a pulsed system then, and with an additional independent Gaussian noise, there can be two scenarios. Either, the presence of a target causes the threshold detector to recognize a signal-plus-noise Gaussian distribution. To model this system in matlab, we use an equation The equation shows the mean value of zero and a noise variance is equal to probability of detection. We begin by solving the equation and creating functions in matlab that we will make work easier. We will use a function to generate binary data for the equation with a length of 1,000. This has been done and its code is appendix. This are converted into symbols that can used to carry out the function this will produce binary figure shown below; Here the binary are converted into integer values and symbols that are used to reshape the function relating to noise detection. This will modulate the function to have a 16 QAM modulator that is intended to help in detecting noise. The vectors will have integers 0:15 to allow modulation. Matlab dmodce function will be used to modulate these to baseband representation. The following is derived by the functions and they are in appendix This has been done white Gaussian noise added to be modulated and produce a signal. Here energy to noise power spectral density ratio is presented by Eb/N0 which is assumed to 10 dB. Then k is used to convert this to signal-to-noise ratio which is in form of a number of bits per a chosen which are further converted by sampling factor as shown in the appendix. It can be used further to convert Eb/N0 to Es/N0 which is ‘ratio of symbol energy to noise power spectral density’. The function of r nsamp in matlab converts Es/N0 to signal-to-noise ratio in the sampling bandwidth. However we need to insert Sum block and a Gain block to assist in further modelling. This is done in a new window as shown below; The Sum block represents adding together the noise and the Gain block shows dividing by voltage. The Sum block will add with a mean value of zero and a noise (power) variance, equal to N and subtract the false alarm probability to have the probability of detection, P (d). the lines drawn to connect terminals of blocks as well as label them. Then we will add Integrator block as shown above. It is further simulated to produce a modulator that will take following appearance. The following modulated signal are obtained from the proposed system above This is further smoothened to produce Essentials of systems modelling Needs/goals/objectives- Confirmation of needs/goals or objectives is to ensure that statement of needs objectives and goals as provided by user of the system, or acquisition agents, are correct. The developer must confirm that these needs are appropriate and valid. Mission engineering- Mission engineering involves a meticulous articulation of anticipated mission of a system. It also involves analysis of the system. The step is essential for verifying that the missions of the system are justifiable, that they are executable, and that they are not undertaken by other systems already. In mission engineering, projects that qualify to be just minor upgrades of the system may have to b dropped. Mission engineering also undertaken to provide the technological basis upon which full description of system requirements are made. This is in the understanding that poor system requirements often lead to cost, schedule and performance problems further along the process. Requirements analysis/allocation- Requirements analysis entails tasks used in the determination of conditions or needs that should be met for a new or transformed product. The process takes into account the possibility of stakeholder requirements conflicting and eventually affecting the project’s development. These requirements must be documented, measurable, testable, and actionable. They should also be defined to such a detail that is sufficient for designing a system. Functional analysis/decomposition - Functional analysis is the process of identifying the set of inputs, behaviour, and outputs of software. Analysis may involve a description of technical details, calculations, manipulation of date or other specific classification of functionalities that a system is to accomplish. Use cases are used to describe behavioural requirements for system functionality. Generally, functions are expressed in the form of a task that the system must undertake. For instance, “system must do , to produce . So defined, the functions are used to drive the application architecture of a system. Architecture design/synthesis- Architecture synthesis is the conversion of designs into functional physical systems. The step starts during the formulation of alternative architectures and before selecting of the most preferred alternative. The process involves the consideration of available designs and choices. An example is the basic selection of time-division multiplexed systems against frequency-division systems with the aim of driving the outstanding parts of the system design. Thereafter, the remaining architecture is made compatible with the basic framework. Alternative analysis/evaluation- Alternative analysis is the process of accepting the identified alternatives of architecture element as input. This is followed by a comprehensive analysis of the alternatives to come up with the most preferred system architecture. The process combines input from TPM, LCC, Risk Analysis, concurrent engineering, system engineering management, and secondary input from ILS, RMA, T&E, QA&M, specialty engineering, and P3I. It also involves the definition of evaluation criteria, and evaluation framework for undertaking assessment of alternatives. Technical performance measurement (TPM)- TPM is the primary basis for evaluating architectural alternatives’ performances. It is also the key feature in the selection of more detailed design strictures such as hardware, human components, and software. An example of TPM for the radar system, TPM would include range to target, probability of detection, and false alarm probability evaluations. Life-cycle costing (LCC)- LCC is also a vital step in the selection process of the best alternative among a choice of architectures. It defines the cost selection and the implied systems within the system development process. Consideration of costs in LCC must be on a life-cycle basis. This involves Research, development, test, and evaluation (RDT&E), acquisition or procurement processes, as well as operations and maintenance (O&M). The process may further be broken down into cost elements that form the 3-D of LCCM (cost model). New trends in systems modelling Three trends in system engineering are discussed below. These are the Capability Maturity Model (CMM), Systems Architecting, and Sustainable Development. Capability Maturity Model (CMM)- Software development has been a continuous process since the first computer was invented. However, advancements in existing organization's software development processes has brought about the adoption of effective approaches towards improving their functionality. The CMM model, for instance, was limited to uses within the US DoD. Eventually, it became clearer that the model could be applied to other processes. This gave rise to a more general concept of the model that is applied to business processes and development. Currently, several different approaches exist in the formulation of integrated CMM. The future tendency is towards a better understanding of elements of system engineering and internal processes aimed at improving of system engineering and management. Systems modelling- Whereas systems modelling has been an ongoing trend, efforts are likely to continue in the definition and development of systems modelling and accomplishments. The process of modelling remains fundamentally a design process and so differs from system analysis. This makes it relevant in the future for the invention of new configurations. Moreover, a strong fascination remains about the creative process of modelling. Sustainable Development- Sustainable development is largely derived from concepts of environmental consciousness and sustainability. The process involves expanding systems engineering to purposely accommodate the principles and practices of ecological sustainability. It also promotes working amongst engineers to adopt existing technologies while developing others that are not only sustainable but also meet societal needs. The sustainable development paradigm will definitely affect software deployment trends in systems engineering all to ensure that the sustainability of technologies and processes are acceptable to all users and particularly the environmentally conscious. Trade-off analysis is important to the systems engineer for it helps in selecting approaches during the functional level of software designing process (Eisner 2008). It is also relevant during the selection of specific design choices at the process’ subsystem level and for the determination of the sensitivity of the overall system selection to change in weights and ratings accorded to various architectural alternatives. References Eisner, H. (2008). Essentials of Project and Systems Engineering Management. Hoboken, N.J.: John Wiley & Sons. Appendix – matlab code function bask(b,f) % b is the input binary bit stream % f is the frequency of the carrier n = length(b); % determine the length of bit stream t = 0:0.01:n-0.01; % time axis for i = 1:n bw( ((i-1)*100)+1 : i*100 ) = b(i); % loop end carrier = cos(2*pi*f*t); % carrier signal modulated = bw.*carrier; % modulated signal subplot(3,1,1) plot(t,bw) grid on ; axis([0 n -2 +2]) subplot(3,1,2) plot(t,carrier) grid on ; axis([0 n -2 +2]) subplot(3,1,3) plot(t,modulated) grid on ; axis([0 n -2 +2]) >> Fd=1; Fs=1; >> nsamp=1; >> M = 16; >> k = log2(M); >> n = 8e4; >> x = randint(n,1); >> stem(x(1:20),'filled'); >> xlabel('Bit Index'); ylabel('Binary Value'); >> xsym = bi2de(reshape(x,k,length(x)/k).'); >> figure; >> stem(xsym(1:10)); >> title('Random Symbols'); >> xlabel('Symbol Index'); ylabel('Integer Value'); >> y = dmodce(xsym,Fd,Fs, 'qask',M); >> ytx = y; >> EbNo = 10; >> snr = EbNo + 10*log10(k) - 10*log10(nsamp); >> pinput = std(ytx); >> noise = (randn(1,n/k)+sqrt(-1)*randn(1,n/k))*(1/sqrt(2)); >> Noisestd = (pinput*10^(-snr/20)); >> ynoisy = (ytx + (Noisestd*noise).'); >> yrx = ynoisy; >> figure; >> plot(real(yrx(1:5e3)),imag(yrx(1:5e3)),’b*’); hold on; plot(real(ytx(1:5e3)),imag(ytx(1:5e3)),’g.’); title(‘Signal Constellation’); legend(‘Received Signal’,’Transmitted Signal’); axis([-5 5 -5 5]); % Set axis ranges. hold off; >>plot(real(yrx(1:5e3)),imag(yrx(1:5e3)),’b*’); >> zsym = ddemodce(yrx,Fd,Fs, 'qask', M); >> z = de2bi(zsym); >> z = reshape(z.',prod(size(z)),1); >> [number_of_errors,bit_error_rate] = biterr(x,z) number_of_errors = 133 bit_error_rate = 0.0017 Read More