Polyamines (PAs) are aliphatic polycations that are widespread in living organisms. In this review, we are focusing the correlation between endogenous PA titers and physiological perturbations and on the protective role of various analogues of PAs against abiotic stresses. PAs are involved in many physiological processes, such as cell growth and development and also respond to diverse abiotic stresses. Polyamines level shifts in different ways depending on several factors, such as plant species, tolerance or sensitivity to stress, and duration of stress. Exogenously supplied PAs protected plants from abiotic stress, whereas transgenic plants overexpressing PA biosynthetic genes exhibited stress tolerance. On the other hand, loss-of-function mutant of PA biosynthetic genes, or decrease of PA titers, resulted to decrease stress tolerance.
It is concluded that the elevated level of antioxidative enzymes and level of proline might be responsible for minimizing the Ni and/or salinity-induced toxicity in Indian mustard which is manifested in terms of improved growth and photosynthesis.
This paper presents a study about the low dimension visual (LDV) space features and investigates the improvement in audio visual automatic speech recognition using different set of visual features. The experiment is divided into three subsections; in first phase the recognition is performed on 12 static DCT features; in second phase the recognition is performed for combination of 6 static and 6 dynamic features and in third phase the recognition is performed on 12 low dimension DCT feature. For this research work Hindi AMUAV (Aligarh Muslim University Audio-Visual) database was developed in which audio sample at 44.1 kHz and video sample at 25 frames per second was opted. Hidden Markov Model (HMM) tool kit with left-right HMMs modeled was used for recognition and an overall improvement of 26.04% in word recognition is achieved with LDV space features.
In building speech recognition based applications, robustness to different noisy background condition is an important challenge. In this paper bimodal approach is proposed to improve the robustness of Hindi speech recognition system. Also an importance of different types of visual features is studied for audio visual automatic speech recognition (AVASR) system under diverse noisy audio conditions. Four sets of visual feature based on Two-Dimensional Discrete Cosine Transform feature (2D-DCT), Principal Component Analysis (PCA), Two-Dimensional Discrete Wavelet Transform followed by DCT (2D-DWT-DCT) and Two-Dimensional Discrete Wavelet Transform followed by PCA (2D-DWT-PCA) are reported. The audio features are extracted using Mel Frequency Cepstral coefficients (MFCC) followed by static and dynamic feature. Overall, 48 features, i.e. 39 audio features and 9 visual features are used for measuring the performance of the AVASR system. Also, the performance of the AVASR using noisy speech signal generated by using NOISEX database is evaluated for different Signal to Noise ratio (SNR: 30 dB to −10 dB) using Aligarh Muslim University Audio Visual (AMUAV) Hindi corpus. AMUAV corpus is Hindi continuous speech high quality audio visual databases of Hindi sentences spoken by different subjects.
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