# Networks

[tabs bg=”” border=”” color=””] [tab title=”Syllabus”]

UNIT – I Introduction to Electrical Circuits:
Circuit Concept – R-L-C parameters – Voltage and Current sources – Independent and dependent sources-Source transformation – Voltage – Current relationship for passive elements – Kirchoff’s laws – network reduction techniques – series, parallel, series parallel, star-to-delta or delta-to-star transformation.

UNI-II   A.C Circuits – I:
R.M.S and Average values and form factor for different periodic wave forms, Steady state analysis of R, Land C (in series, parallel and series parallel combinations) with sinusoidal excitation – Concept of self and mutual inductances – co-efficient of coupling series circuit analysis with mutual inductance.

UNIT – III A.C Circuits – II:
Resonance – series, parallel circuits, concept of band width and Q factor. Three phase circuits: Phase sequence – Star and delta connection – Relation between line and phase voltages and currents in balanced systems – Calculations of active and reactive power.

UNIT – IV Network topology:
Definitions – Graph – Tree, Basic cutset and Basic Tieset matrices for planar networks – Loop and Nodal methods of analysis of Networks with independent and dependent voltage and current sources – Duality &Dual networks.

UNIT – V Network Theorems:
Tellegens, Superposition, Reciprocity, Thevinin’s, Norton’s, Max Power Transfer theorem. Milliman’s Theorem – Statement and proofs problem solving using dependent and independent sources for d.c and a.c excitation.

UNIT – VI Two-port networks:
Z,Y, ABCD, h-parameters – Conversion of one parameter to another parameter – condition for reciprocity and symmetry – 2 port network connections in series, parallel and cascaded – problem solving.

UNIT – VII Transient Analysis:
Transient response of R-L, R-C, R-L-C circuits (Series combinations only) for d.c. and sinusoidal excitations – Initial conditions – Solution using differential equation approach and Laplace transform methods of solutions.

UNIT – VIII Filters:
L.P, H.P, B.P, B.E, Prototype filters design – M-derived filters of L.P. and H.P.- Composite filter design of L.P. and H.P design of various symmetrical attenuators.

[/tab] [tab title=”Videos”]

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to “dial” by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

[/tab] [tab title=”Class Notes”]

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to “dial” by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

[/tab] [tab title=”Reference Text Books”]

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to “dial” by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

[/tab]

[/tabs]