Retrieval of unintelligible speech is a basic need for speech impaired and is under research for several decades. But retrieval of random words from thoughts needs a substantial and consistent approach. This work focuses on the preliminary steps of retrieving vowels from Electroencephalography (EEG) signals acquired while speaking and imagining of speaking a consonant-vowel-consonant (CVC) word. The process, referred to as Speech imagery is imagining of speaking to oneself silently in the mind. Speech imagery is a form of mental imagery. Brain connectivity estimators such as EEG coherence, Partial Directed Coherence, Directed Transfer Function and Transfer Entropy have been used to estimate the concurrency and causal dependence (direction and strength) between different brain regions. From brain connectivity results it has been observed that the left frontal and left temporal electrodes were activated for speech and speech imagery processes. These brain connectivity estimators have been used for training Recurrent Neural Networks (RNN) and Deep Belief Networks (DBN) for identifying the vowel from the subject's thought. Though the accuracy level was found to be varying for each vowel while speaking and imagining of speaking the CVC word, the overall classification accuracy was found to be 72% while using RNN whereas a classification accuracy of 80% was observed while using DBN. DBN was found to outperform RNN in both the speech and speech imagery processes. Thus, the combination of brain connectivity estimators and deep learning techniques appear to be effective in identifying the vowel from EEG signals of subjects' thought.

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The Kudremukh National Park in the central Western Ghats (India) is a mega-biodiversity hotspot. However, the dependence on forests of tribal and non-tribal settlements in the core area of the park has resulted in forest fragmentation, posing a threat to the endemic flora. The study focuses on the disturbed forest ecosystem in the park. Using the belt transect method, we studied the vegetation structure and floristic composition of forests in tribal and non-tribal settlement areas. We compared species diversity, richness, dominance and stand quality of the park with an undisturbed nearby forest. Due to harvesting practices of the rural communities, the percentage of light-demanding species was higher in tribal and non-tribal forest compared those in with the undisturbed forest. Differences in species composition were largely due to extensive establishment of light-demanding or pioneer forest species. The pioneer forest species pose a threat to endemic species of the region by suppressing seedling growth and establishment. Our observations will help forest functionaries to prepare site-specific restoration plans. Primary forest species such as Myristica dactyloides, Palaquium ellipticum, Garcinia gummi-gutta and Poeciloneuron indicum can be used in the restoration process. These species help to maintain forest ecosystem stability and improve provision of forest ecosystem services.

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