Automatic volume control (AVC) changes volume of TV/Radio based on surroundings noise. A number of hardware and software implementation of AVC is available. Conventional AVC methods concentrates on maintain a constant power-ratio between input of microphone and speaker-output signals. Some new methods are concentrating on preserving intelligibility of speech. They are extracting desired frequency band then comparing their power. This paper proposes Correlation based volume control for radio/Smart Television. Proposed Correlation Method adjusts volume based on linearity of input of microphone and speaker-output signals. A band-pass filter is used to filter-out unwanted frequency bands for comparing sound intelligibility. Conventional system concentrates on maintaining SNR. Proposed method adjusts volume considering delay and wave-shape of input of microphone and speaker-output signals.
Alzheimer's and related dementia are associated with a gradual decline in cognitive abilities of an individual, impairing independent living abilities. Wandering, a purposeless disoriented locomotion tendency or behavior of dementia patients, requires constant caregiver supervision to reduce the risk of physical harm to patients. Integrating technology into care ecology has the potential to alleviate stress and expense. An automatic wandering detection system integrated with an intervention module may provide warnings and assistive suggestions in times of abnormal behavior. In this study, we survey existing research on technology aided methodologies and algorithms used in detection and management of wandering behavior of individuals affected with dementia. Our study provides insights into mechanisms of collecting movement data and finding patterns that distinguish wandering from normal behavior.
Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of word pairs, Hearst Patterns based on regular expressions, and word embeddings created from the UMBC corpus. Our system Babbage participated in Subtask 1A for English and placed 6th of 19 systems when identifying concept hypernyms, and 12th of 18 systems for entity hypernyms.
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