CO-RELATIONAL STUDY ON HOUSE FRONT SIT-OUT (THINNAI), ITS IDENTITY, AND EMOTIONAL EXPERIENCE AMONG THE OCCUPANTS OF ERODE – TAMILNADU

Architecture persistently evolved from the need for shelter and living spaces. Subsequently, this is prominent and reinforced through the knowledge over some time through oral traditions and practices that are time tested and improvised to develop a unique expression termed Vernacular architecture. It is evident from the existing literature that the built environment as a subsystem has the potential to influence and direct behavior among the occupants of the settings. This paper attempts to determine the spatial feature of the vernacular setting in the selected context which has a certain identity and has the potential in inducing specific positive Affect like enthusiasm among the occupants. In pursuit of this, the physical setting feature of the vernacular house Thinnai is studied for its identity of the house in context and its impact in inducing the experience of positive affect enthusiastic effect through a set of data collected by questionnaire survey and semi-structured interview method. The result of the analysis indicates that Physical setting features induce affect when people habituate to the vernacular settings that stand for their identity daily and it confirms the hypothesis that a physical setting has a significant role in inducing Positive affective among the occupants of the vernacular settings

Voice Feature Extraction for Gender and Emotion Recognition

Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. This led to the development of a model for “Voice feature extraction for Emotion and Gender Recognition”. The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have different voice characteristics due to their acoustical and perceptual differences along with a variety of emotions which convey their own unique perceptions. In order to explore this area, feature extraction requires pre- processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre- processing using Voice Activity Detector (VAD), feature extraction using Mel-Frequency Cepstral Coefficient (MFCC), feature reduction by Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in the healthcare sector, virtual assistants, security purposes and other fields related to the Human Machine Interaction domain.&nbsp

CO-RELATIONAL STUDY ON COURTYARD AND SURROUNDING SPACES ITS ACTIVENESS, EXCITEMENT, AND EMOTIONAL EXPERIENCE AMONG THE OCCUPANTS OF SIVAGANGA – TAMILNADU

Housing evolution has been driven by cultural, economic, and technological advancements. Architecture has a significant role in shaping this evolution, as architects continuously respond to changing needs by using innovative designs and materials. This results in functional, creative, optimizable, and customizable housing solutions. Research and this manuscript are focused on adapting and incorporating elements of vernacular architecture into modern housing designs. This aims to create sustainable and culturally-sensitive homes that balance tradition and modernity, while still retaining emotional and visual connections. The courtyard and surrounding spaces induce a strong positive effect on the occupants. Studies demonstrate that activeness and excitement are positive feelings that are induced by a courtyard and surrounding spaces respectively. By analyzing the data collected from diverse houses and people strongly judge that certain people are triggered to induce these spaces in housing. It confirms the hypothesis that a physical setting has a significant role in inducing Positive affective among the occupants of the vernacular settings

A road map for the generation of a near-infrared guide star catalog for thirty meter telescope observations

The near-infrared instruments in the upcoming Thirty Meter Telescope (TMT) will be assisted by a multi conjugate Adaptive Optics (AO) system. For the efficient operation of the AO system, during observations, a near-infrared guide star catalog which goes as faint as 22 mag in JVega band is essential and such a catalog does not exist. A methodology, based on stellar atmospheric models, to compute the expected near-infrared magnitudes of stellar sources from their optical magnitudes is developed. The method is applied and validated in JHKs bands for a magnitude range of JVega 16–22 mag. The methodology is also applied and validated using the reference catalog of PAN STARRS. We verified that the properties of the final PAN STARRS optical catalog will satisfy the requirements of TMT IRGSC and will be one of the potential sources for the generation of the final catalog. In a broader context, this methodology is applicable for the generation of a guide star catalog for any existing/upcoming near-infrared telescopes

Generation of a near infra-red guide star catalog for thirty-meter telescope observations

The requirements for the production of a near Infra-Red Guide Star Catalog (IRGSC) for Thirty Meter Telescope (TMT) observations are identified and presented. A methodology to compute the expected J band magnitude of stellar sources from their optical (g, r, i) magnitudes is developed. The computed and observed J magnitudes of sources in three test fields are compared and the methodology developed is found to be satisfactory for the magnitude range, JVega = 16–22 mag. From this analysis, we found that for the production of final TMT IRGSC (with a limiting magnitude of JVega = 22 mag), we need g, r, i bands optical data which go up to iAB ~ 23 mag. Fine tuning of the methodology developed, such as using Spectral Energy Distribution (SED) template fitting for optimal classification of stars in the fainter end, incorporating spectral libraries in the model, to reduce the scatter, and modification of the existing colour–temperature relation to increase the source density are planned for the subsequent phase of this work

Voice Feature Extraction for Gender and Emotion Recognition

Voice recognition plays a key function in spoken communication that facilitates identifying the emotions of a person that reflects within the voice. Gender classification through speech is a popular Human Computer Interaction (HCI) method on account that determining gender through computer is hard. This led to the development of a model for "Voice feature extraction for Emotion and Gender Recognition". The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have specific vocal traits because of their acoustical and perceptual variations along with a variety of emotions which bring their own specific perceptions. In order to explore this area, feature extraction requires pre-processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre-processing using Voice Activity Detector(VAD), feature extraction using Mel-Frequency Cepstral Coefficient(MFCC), feature reduction by Principal Component Analysis(PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in healthcare sector, virtual assistants, security purposes and other fields related to Human Machine Interaction domain