The central theme of this thesis is the development of both a generalised computational model for large-scale brain networks and the neuroinformatics platform that enables a systematic exploration and analysis of those models. In this thesis we describe the mathematical framework of the computational model at the core of the tool The Virtual brain (TVB), designed to recreate collective whole brain dynamics by virtualising brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. We also review previous studies related to brain network models and multimodal neuroimaging integration and detail how they are related to the general model presented in this work. Practical examples describing how to build a minimal *in silico* primate brain model are given. Finally, we explain how the resulting software tool, TVB, facilitates the collaboration between experimentalists and modellers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis, and simulation of structural and functional data in an accessible, web-based interface. / The central theme of this thesis is the development of both a generalised computational model for large-scale brain networks and the neuroinformatics platform that enables a systematic exploration and analysis of those models. In this thesis we describe the mathematical framework of the computational model at the core of the tool The Virtual brain (TVB), designed to recreate collective whole brain dynamics by virtualising brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. We also review previous studies related to brain network models and multimodal neuroimaging integration and detail how they are related to the general model presented in this work. Practical examples describing how to build a minimal *in silico* primate brain model are given. Finally, we explain how the resulting software tool, TVB, facilitates the collaboration between experimentalists and modellers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis, and simulation of structural and functional data in an accessible, web-based interface.

This thesis purposes and develops inductively coupled LC (inductive-capacitive) pH sensors based on pH-sensitive electrode pair. The LC resonator circuit is based on a varactor and measures the low frequency potential difference. For wireless pH monitoring, the resonator circuit is integrated with a pH-sensitive electrode pair. This sensor demonstrates a linear response over 2 to 12 pH dynamic range, 0.1 pH accuracy and long-term stability. Accurate measurement of pH using electrode-based sensors is affected by temperature variation. A technique of simultaneously measuring two parameters, pH and temperature, with a single RLC resonator based sensor is presented. An algorithm is developed, which applies both pH and temperature measurement to incorporate temperature compensation in pH measurement. For in-fluid applications, an encapsulation method is applied to the LC resonator based sensor to reduce the influence of medium permittivity and conductivity on the sensor measurement. Non-invasive way to obtain reliable pH information from bacterial culture bioprocesses is demonstrated with the fluid embeddable sensor. The pH sensor is remodeled to an acidic and basic volatile sensor by embedding the electrodes in a hydrogel host electrolyte. Tests demonstrate that the volatile sensor has a detection limit of 1.5 ppm and 2 ppm for ammonia and acetic acid vapor, respectively. Application of the volatile sensor to fish spoilage monitoring shows that the sensor is capable of detecting the product rejection level with good sensitivity in real-time. It is important to develop low cost wireless passive pH sensor technologies for embedded applications such as bioprocess and food spoilage monitoring. The electrode-based passive LC sensor approach employed in this thesis overcomes drawbacks of some of the early developed passive pH sensors and can lead to an inexpensive implementation using printed electronics technology.