INTELLIGENT SIGNAL ACQUISITION SYSTEMS (ISAS)

• Main Author: Ivan Goenawan (NIM : 233 00 005), Stephanus
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/9336
• Institution: Institut Teknologi Bandung

Abstract: <br /> <br /> <br /> <br /> <br /> Intelligent Signal Acquisition Systems (ISAS) is systems that can acquire signal intelligently using Artificial Neural Networks (ANN). The main benefit of these system is can acquire larger information from one signal than conventional digital systems. In this research, we would try to discover the systems that is how to enhance the density of information twice in one signal by ability well return patterns. <br /> <br /> <br /> <br /> <br /> In the SASDIC technique that is discrete signal acquisitions still accuracy for half aktif area width so a maximum random pertubation is 0.45 V, while one aktif area width is 0.95 V. In the conventional technique by using TTL voltage characteristics that is signal acqusitions still accuracy so a maximum random pertubation is 0.40 V. <br /> <br /> <br /> <br /> <br /> The results of attenuation pertubation analysis and tests g have a minimum and maximum boundary value of a prediction pertubation change k that is g = k.(1 (less more) 0.0769), whereas random superpositions of r-max have a maximum boundary value of a prediction amplification k and an aktif area width A that is rmax 0.90.k- A . <br /> <br /> <br /> <br /> <br /> The combined pertubation analysis and tests by sequences attenuation g and random superposition r-max have boundary values r-max 5 0.6.(kl . A) and g = (k2 / kt ).(l (less more) 0.0769), whereas in the reverse order of the above pertubations that is attenution g then random superposition r-max have boundary values g = (k2 / kl ).(l (less more) 0.0769) and r-max 0.63.(ki . A), where k, is a prediction amplification parameter and k2 is a normalisation factor. <br />