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Pioneering research

In the last decade, a number of research groups in Europe and the Americas have conducted studies into the safety and effectiveness of psychedelics for conditions such as depression and post-traumatic stress disorder (PTSD), but the Imperial Centre for Psychedelic Research is the first to gain this level of stature within a major academic institution.

When delivered safely and professionally, psychedelic therapy holds a great deal of promise for treating some very serious mental health conditions.

Dr Robin Carhart-Harris

Head of the Centre for Psychedelic Research

Ours was the first Centre in the world to investigate the brain effects of LSD using modern brain imaging and the first to study psilocybin – the active compound in magic mushrooms – for treating severe depression. These studies have laid the groundwork for larger trials that are now taking place around the world. Other pioneering work from the group includes breakthrough neuroimaging research with psilocybin, MDMA and DMT (the psychoactive compounds found in ecstasy and ayahuasca respectively).

Earlier this year the group began a new trial directly comparing psilocybin therapy with a conventional antidepressant drug in patients with depression – a study for which they are still recruiting volunteers. Building on this, they also plan to begin another new trial next year to explore the safety and feasibility of psilocybin for treating patients with anorexia.

Dr Carhart-Harris adds: “It may take a few years for psychedelic therapy to be available for patients, but research so far has been very encouraging. Early stage clinical research has shown that when delivered safely and professionally, psychedelic therapy holds a great deal of promise for treating some very serious mental health conditions and may one day offer new hope to vulnerable people with limited treatment options.”


If you are a student interested in conducting research with our Centre, please see the page join our research team.

Research publications

Citation

BibTex format

@article{Dekkers:2022:10.1016/j.inffus.2021.07.022,
author = {Dekkers, G and Rosas, F and van, Waterschoot T and Vanrumste, B and Karsmakers, P},
doi = {10.1016/j.inffus.2021.07.022},
journal = {Information Fusion},
pages = {196--210},
title = {Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities},
url = {http://dx.doi.org/10.1016/j.inffus.2021.07.022},
volume = {77},
year = {2022}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high energy consumption, commercialisation of battery-powered wireless acoustic sensor networks has been limited. To increase the network's lifetime, this paper explores the joint use of decision-level fusion and dynamic sensor activation. Hereby adopting a topology where processing – including feature extraction and classification – is performed on a dynamic set of sensor nodes that communicate classification outputs which are fused centrally. The main contribution of this paper is the comparison of decision-level fusion with different dynamic sensor activation strategies on the use case of automatically classifying domestic activities. Results indicate that using vector quantisation to encode the classification output, computed at each sensor node, can reduce the communication per classification output to 8 bit without loss of significant performance. As the cost for communication is reduced, local processing tends to dominate the overall energy budget. It is indicated that dynamic sensor activation, using a centralised approach, can reduce the average time a sensor node is active up to 20% by leveraging redundant information in the network. In terms of energy consumption, this resulted in an energy reduction of up to 80% as the cost for computation dominates the overall energy budget.
AU - Dekkers,G
AU - Rosas,F
AU - van,Waterschoot T
AU - Vanrumste,B
AU - Karsmakers,P
DO - 10.1016/j.inffus.2021.07.022
EP - 210
PY - 2022///
SN - 1566-2535
SP - 196
TI - Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities
T2 - Information Fusion
UR - http://dx.doi.org/10.1016/j.inffus.2021.07.022
UR - https://www.sciencedirect.com/science/article/pii/S1566253521001470?via%3Dihub
VL - 77
ER -