Histological, Histochemical and Biochemical characterisation of male morphotypes of Macrobrachium rosenbergii (de Man)

This thesis entitled “Histological ,Histochemical and biochemical characterisation of male morphotypes of macrobrachium rosenbergii(De Man). The giant fresh water prawn Macrobrachium rosenbergii (de Man) is emerging as a prime candidate species in fresh water aquaculture as a global basis and therefore, receiving much attention in recent years.the present work was aimed at to study the histological variations, if any, in the reproductive system viz. testes, vas deferens including androgenic gland, hepatopancreas and the neurosecretory system viz. eye stalk, brain and thoracic ganglion among the male morphotypes and their transitional stages of M.rosenbergii from growouts. This study was also aimed at to bring out the histochemical variations, if any, in the reproductive system comprising of testes, vas deferens including androgenic gland and the hepatopancreas among the male morphotypes and their transitional stages collected from growouts. Biochemical characterisation of various male morphotyes and their transitional stages have also been attempted in order to find out biochemical evidence, if any, in the morphotypic transformation.Histological study of the testes of three male morphotypes viz., SM, SOC and SBC and their 'transitional stages viz., WOC, tSOC, WBC and OBC have been carried out with a view to unravel the structural and functional differences of the testes, if any, of these morphotypes. Studies on the lipid components viz., cholesterol, phospholipid and triglyceride In the muscle tissue and hepatopancreas have been carried out.

Speech Recognition of Malayalam Numbers

Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.