After analysing the geomertry of the Sri Yantra, the eminent computer scientists Gérard Huet concludes his paper with the following:

We cannot resist quoting again The Tantric Way: “Sri Yantra, in its formal content, is a visual masterpiece of abstraction, and must have been created through revelation rather than by human ingenuity and craft”.
[A. Mookerjee, M. Khanna, The Tantric Way, Thames and Hudson, London, 1977]


Sri Yantra references

Krishna Kanth Varma, P., Krishna, C. M., & Santhi Ratna Priyanka, G.. (2018). Performance improvement of sri yantra shaped multiband antenna with defected ground structure. International Journal of Engineering and Technology(UAE)

Plain numerical DOI: 10.14419/ijet.v7i3.31.18197
DOI URL
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Huet, G.. (2002). Śrī Yantra geometry. Theoretical Computer Science

Plain numerical DOI: 10.1016/S0304-3975(02)00028-2
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Barsotti, T. J., Jain, S., Guarneri, M., King, R. P., Vicario, D., & Mills, P. J.. (2023). An exploratory investigation of human biofield responses to encountering a sacred object. Explore

Plain numerical DOI: 10.1016/j.explore.2023.01.007
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Lidke, J. S.. (2016). The potential of the bi-directional gaze: A call for neuroscientific research on the simultaneous activation of the sympathetic and parasympathetic nervous systems through tantric practice. Religions

Plain numerical DOI: 10.3390/rel7110132
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Lidke, J. S.. (2011). The resounding field of visualised self-awareness: The generation of synesthetic consciousness in the Śri Yantra Rituals of Nityasodaśikśrnava tantra. Journal of Hindu Studies

Plain numerical DOI: 10.1093/jhs/hir035
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Bolton, N. J., Nicol, D., & Macleod, G.. (1977). The geometry of the Śrī-yantra. Religion

Plain numerical DOI: 10.1016/0048-721X(77)90008-2
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Ragas

Dandawate, Y. H., Kumari, P., & Bidkar, A.. (2015). Indian instrumental music: Raga analysis and classification. In 2015 1st International Conference on Next Generation Computing Technologies (NGCT) (pp. 725–729). IEEE
Plain numerical DOI: 10.1109/NGCT.2015.7375216
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“Raga played by indian instrument is the actually soul of the indian classical music. indian classical music is famous around all over the world for its particular structure and well soundness. our work is related to analyze and classify the instrumental music according to their features. this will help to non-professional and music learner for understanding and acquire knowledge about the music using the system intelligence. there are various features for analysis of music but our approach is towards the spectral and temporal features. for extraction of feature we take spectrum, chromagram, centroid, lower energy, roll off, histogram etc. at very first we just collect clips of ragas and find out the spectral and temporal features. these features show the better result. we are using four ragas namely:- bhairav, bhairavi, todi and yaman. for classification we use different types of classifier just like knn classifier and svm classifier they gives approximate 87% and 92% accuracy respectively.”
Rao, B. T., Mandhala, V. N., Bhattacharyya, D., & Kim, T.. (2015). Automatic Instrumental Raaga ? A Minute Observation to Find Out Discrete System for Carnatic Music. International Journal of Multimedia and Ubiquitous Engineering, 10(6), 99–112.
Plain numerical DOI: 10.14257/ijmue.2015.10.6.10
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“The objective of this paper is to evolve a system, which automatically mines the raaga of an indian classical music. in the first step note transcription is applied on a given audio file in order to generate the sequence of notes which are used to play the song. in the next step, the features related to arohana – avarohana are extracted. the features of two/three songs are then selected in random and given as input to the training system. totally songs of 72 melakartha raagas and 45 janya raagas are considered. subsequently, work testing is done by extracting features of one or two songs of each raaga, which are given as inputs in the training part. the generated output indicates the identification of each raaga. unique labeling has been done for each raaga, for the system to identify the set of trained raagas. in this work 7 instruments namely veena, saxophone, violin, nadaswaram, mandolin, flute and piano are used. the database generated is trained and tested by using (1) gaussian mixed model (2) hidden markov model (3) k-nearest neighbor using cosine distance and earth mover distance to draw appropriate conclusions.”
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