Piezoelectrically Actuated Linear Resonators on Ring-shaped Suspensions for Applications in MEMS Phase-sensitive Gyroscope

AbstractExcitation of linear resonators by means of thin aluminium nitride piezoelectric films processed on annular (ring-shaped) flexures was investigated. The in-plane resonance modes at 8-45kHz frequencies with quality factors >104 were measured for both single (uni-directional) and double (bi-directional) in-plane degree of freedom devices in vacuum. Annular springs have different spring constants for different directions of deformation allowing efficient decoupling of orthogonal modes in bi-directional symmetric devices. The primary application of the bi-directional resonators is in orbiting-motion MEMS gyroscopes that rely on phase-shift detection induced by external angular rates. To investigate the feasibility of differential piezoactuation for generation of motion in uni- and bi-directional devices by means of thin piezoelectric films processed on supporting annular springs, MEMS resonators were modelled, designed, fabricated and electrically characterized

Machine Learning Based Fall Detection for Elderly People with Neurodegenerative Disorders

Fall is one of the most serious clinical problems faced by the elderly people. Elder people with neurodegenerative disorders like Parkinson disease often fall. This leads to the damage of physical condi- tion and also mental condition. Therefore, elderly people should be taken care of all the time. However, it is not possible to take care of them ev- ery moment. Therefore, an automatic fall detection system is required to track elderly at any time. An automated fall detection system will provide timely assistance and hence, it will reduce medical care costs sig- nificantly. The recent developments in motion- sensor technologies have allowed the efficient use of wearable sensors in the overall treatment of the elderly. The paper presents a machine learning framework consisting of data collection, preprocessing of data, feature extraction and machine learning classifiers. They comprise C4.5, Random Forest, RepTree, and LMT (Logistic Model Tree). Dataset used in this research has been col- lected by using 3-axis accelerometer sensors which are mounted on a person’s waist. Features have been extracted from this dataset which are used by these classifiers. C4.5 gives the highest accuracy which is 97.36% in comparison to other classifiers

Socio-inspired ICT: Towards a socially grounded society-ICT symbiosis

Modern ICT (Information and Communication Technology) has developed a vision where the “computer” is no longer associated with the concept of a single device or a network of devices, but rather the entirety of situated services originating in a digital world, which are perceived through the physical world. It is observed that services with explicit user input and output are becoming to be replaced by a computing landscape sensing the physical world via a huge variety of sensors, and controlling it via a plethora of actuators. The nature and appearance of computing devices is changing to be hidden in the fabric of everyday life, invisibly networked, and omnipresent, with applications greatly being based on the notions of context and knowledge. Interaction with such globe spanning, modern ICT systems will presumably be more implicit, at the periphery of human attention, rather than explicit, i.e. at the focus of human attention.Socio-inspired ICT assumes that future, globe scale ICT systems should be viewed as social systems. Such a view challenges research to identify and formalize the principles of interaction and adaptation in social systems, so as to be able to ground future ICT systems on those principles. This position paper therefore is concerned with the intersection of social behaviour and modern ICT, creating or recreating social conventions and social contexts through the use of pervasive, globe-spanning, omnipresent and participative ICTValues and TechnologyTechnology, Policy and Managemen