The paper presents two blocks that extend Simulink range of applicability to varying matrices, allowing the user to build (without writing a single line of code) dynamical systems that can be both complex and suitable for real time simulations. First of all, the three common techniques to build dynamical system with Simulink will be presented, showing the advantages and disadvantages of each of...

We present a semi-automatic design flow from Simulink models to prototypes of mixed hardware/software implementations of these models. Our work consists of three key contributions: (1) transformation of a functional model given in MATLAB/Simulink to the well-defined synchronous reactive model of computation (SR MoC), (2) an automatic SystemC code generation from Simulink models using the SR MoC...

Simulink is a modeling environment suitable to model embedded systems at system-level. However, there is no standard to prototype Simulink models onto modern multiprocessor system-on-chip (MPSoC). In this demonstration we show how our NoC System Generator tool can be used as part of an automated platform-based design flow to synthesize a Simulink model onto a network-on-chip based MPSoC impleme...

System-level design methodologies have been introduced as a solution to handle the design complexity of mixed Hardware / Software systems. In this paper we describe a system-level design flow starting from Simulink specification, focusing on concurrent hardware and software design and verification at four different abstraction levels: System Simulink model, Transaction Simulink model, Macro arc...

MATLAB is specifically designed for simulating dynamic systems. This paper describes a method of modelling impulse voltage generator using Simulink, an extension of MATLAB. The equations for modelling have been developed and a corresponding Simulink model has been constructed. It shows that Simulink program becomes very useful in studying the effect of parameter changes in the design to obtain ...

However, because the modeling environment of Simulink is a mixed and heterogeneous one as the combinations of discrete semantics, continuous semantics, etc. Usually it is not very easy to understand a Simulink model. A better way to represent a Simulink model may be necessary. One possible method is to separate the model into sub-models which have relatively simpler, well-defined semantics. Wit...

However, because the modeling environment of Simulink is a mixed and heterogeneous one as the combinations of discrete semantics, continuous semantics, etc. Usually it is not very easy to understand a Simulink model. A better way to represent a Simulink model may be necessary. One possible method is to separate the model into sub-models which have relatively simpler, well-defined semantics. Wit...

Although a system designer can use hardware description languages, such as VHDL (Chapter 6) and Verilog to program FPGAs, the algorithm developer typically uses higher-level descriptions to refine an algorithm. As a result, an algorithm described in a language such as Matlab or C is frequently reentered by hand by the system designer, after which the two descriptions must be verified and refine...

Voice communication over internet not be possible without a reliable data network, this was first available when distributed network topologies were used in conjunction with data packets. Early network used single centre node network in which a single workstation (Server) is responsible for the communication. This posed problems as if there was a fault with the centre node, (workstation) nothin...

This paper presents the development of a fuzzy logic library using the dynamic simulation software SIMULINK under MATLAB environment. The developed library includes the main blocks: MIN (minimum operation), MAX (maximum operation), MFG (membership function generator), FUZ (fuzzifier to convert a crisp input into a fuzzy labels), F-RULES (fuzzy rules matrix), F-INF (Min-Max fuzzy inference), DEF...