VII. COMPARISON WITH OTHER SYSTEM MODELING LANGUAGES AND FRAMEWORKS

The Manifolda language and the Teraptor Synthetic IoT Platform allow complex intelligent systems to be modeled and realized in different forms. As mentioned in Section 1, there exist other modeling languages that are used for system modeling. Modelica [12] is a language that is used for modeling physical systems consisting of mechanical, electrical, electronic, thermal, hydraulic and other components. OpenModelica [13] is an open-source modeling and simulation environment based on Modelica. Table 3 compares the features of Manifolda and the Teraptor Synthetic IoT Platform with Modelica/OpenModelica.
        Modelica is designed for modeling physical systems that consist of components from multiple domains (electrical, mechanical etc). Manifolda language also supports modeling of such systems. In addition, Manifolda can be used to model data-intensive, computation-intensive systems and allows planes of data to be modeled as system components.         The primary purpose of modeling with Modelica is to study and verify the system behavior using simulation. The Teraptor Synthetic IoT Platform allows this and in addition provides tools to realize the modeled system as a distributed software system, an electronic chip, FPGA or any other computing system.
        There exist a large collection of Modelica libraries of standard components across different domains [14]. The Teraptor Synthetic IoT Platform also offers a growing list of libraries of processor models, electronic device models, physical device models and system models.

Comparison of Manifolda and Modelica

VIII. CONCLUSION

A platform based approach to complex intelligent system modeling allows systems to be described at high-levels of abstraction and a distributed solution to be automatically synthesized from the models. The communication and synchronization aspects can be handled by the platform. This approach helps in ensuring that intelligent behavior can be realized as per requirements, enables global optimization to obtain the required price/performance ratio, reduces development complexity and scales well when the system complexity increases.
channel car {
transitive CarSteeringPosition { double steering_direction; };
progressive soft CarControl(in CarSteeringPosition ctp) {
east() {
using steering_logic play (ctp.steering_direction);
} }
progressive soft CarSteering(out CarSteeringPosition ctp) {
east() {
using update_steering_position play (ctp.steering_direction);
} }
system connected_car { CarSteering driver; CarControl car;
port MultiFrame () { CarSteeringPosition csp = { steering_direction = form('input.txt'); };
using driver project (csp) [];
using car project (csp) [];
}
};
}
channel MM {
progressive soft MultiplyMC(in int m1[][], in int m2[], out int product[]) {
east() {
// Initialize product to zero
using = play(product[R0],0) [];
// Multiply matrix m1 with vector m2
using += * play(product[R0], m1[R0][R1], m2[R1]) [][];
} }
transitive Mat2D { int m[][]; };
progressive soft MatrixMul( in Mat2D m1, in Mat2D m2, out Mat2D product) {
east() {
MultiplyMC RowMul[MAX_MUL];
// Multiply m1 with each column of m2
using RowMul.east project(m1.m, m2.m[][R0], product.m[][R0]) [];
} }
system MM_manifold { MatrixMul mm1;
port SingleFrame (char *m1s, char *m2s, char *m3s) { Mat2D m1 = { m = form(m1s); };
Mat2D m3 = { m = form(m3s) ; };
using mm1.east project( m1, form(m2s), m3 )[];
}
port MultiFrame () {
Mat2D m1 = { m = form("m1.plane") [100]; }; Mat2D m2 = { m = form("m2.plane") [100]; };
Mat2D m3 = { m = form("m3.plane") [100]; };
using mm1.east project( m1, m2, m3 )[];
}
};
}

References

[1] Mattern, Friedemann; Christian Floerkemeier (2010). "From the Internet of Computers to the Internet of Things". URL: http://www.vs.inf.ethz.ch/publ/papers/Internet -of-things.pdf
[2] Winning with the IoT - A Synthetic Approach, SANKHYA Research, May 13, 2014. SyntheticIoTFull.pdf
[3] IDC Press Release URL: http://www.idc.com/getdoc.jsp?containerId=prUS24542113
[4] Unified Modeling Language [Online] URL: http://www.uml.org
[5] SysML [Online] URL: http://sysml.org
[6] SANKHYA Teraptor User Guide and Reference Manual. (Part No. 10080275-009) [Online].. Available: http://www.hamara.in/khub?cmd=system&if=WVFile&op=read_file&o bject=/home/applications/webvaradhi/DMS/documents/KnowledgeHub/ Sankhya/Product_Documentation/TeraptorUGRM.pdf
[7] Teraptor Synthetic IoT Platform web-page URL: "http://www.sankhya.com/info/CISMP.html
[8] SANKHYA Infiniproc URL: http://www.infiniproc.com/
[9] SANKHYA NextTrade URL: http://www.nexttrade.in/
[10] Smart Grid Demo Video URL: "https://www.youtube.com/watch?v=3YyCP2TYuns
[11] Connected Car Development Platform Demo Video URL: https://www.youtube.com/watch?v=p_9vHDT6oPY
[12] Modelica Language Specification 3.3 URL: https://www.modelica.org/documents/ModelicaSpec33.pdf
[13] OpenModelica URL: https://openmodelica.org/

NOTICES

This document may contain forward-looking statements and product specifications that are subject to change. Sankhya Technologies Private Limited reserves the right to change all or part of the specifications in this document without prior notice.
        SANKHYA, Infiniproc, NextTrade, Manifolda, Synthetic Processor, Teraptor, Teraptor Designer, Teraptor Player, Teraptor Core Channel, Teraptor Channel, Teraptor Verifier, Teraptor Synthesizer, Teraptor Assembler, Teraptor Linker, Teraptor Model Space Explorer, SMDL, SANKHYA Machine Description Language, and SANKHYA TECHNOLOGIES are trademarks or registered trademarks of Sankhya Technologies Private Limited. All other brands and names are the property of their respective owners.
        SANKHYA Software is protected, in whole or in part, by U.S. and/or foreign patents. The following is a partial list of patents that Sankhya Technologies either owns or licenses for its products: US Patent No. 7,376,936, 7,529,658,8,161,376, 8,566,772, 8,849,650 ; Indian Patent No 258624. In addition, other         US and Indian patents are pending on Sankhyas products and technologies.