Radio frequency (RF) techniques have the potential to provide blood glucose readings through sensing the glucose dependent change in dielectric properties of the biological tissue. Such technique can enable much desired non-invasive and continuous monitoring of blood glucose level. In this work, we present realistic glucose dependence of dielectric properties as well as basic understanding of resonator behaviour while radiating towards the lossy biological tissue. To investigate the potential of RF techniques, two resonators, operating at microwave frequencies when placed radiating towards the biological tissue, are designed and fabricated. The spiral resonator is tested with liquid and semi-solid phantoms containing different amounts of sugar. An analytical formulation to retrieve the dielectric properties of the biological tissues is improved. In order to perform realistic tests, novel tissue mimicking materials for an extremely wide frequency range are proposed. Glucose dependance of the blood mimicking material dielectric properties are further investigated by adding realistic glucose amounts to the blood mimicking material and dielectric spectroscopy is performed. Next, a single pole Cole-Cole model is fitted to the median of the dielectric property measurements. In addition, a patch resonator is simulated with four-layered digital phantom and tested with the four-layered physical tissue mimicking phantom. Finally, a double parameter measurement platform is constructed by combining the patch resonator and a commercial force sensor to perform controlled experiments with humans. Also, the force dependant response of the patch resonator is quantified. Soda tests is performed on five subjects with the platform, all subjects were asked to apply the same level of force. Spiral resonator is also applied to examine the glucose changes of two human subjects during the soda test. The results suggests that, although the glucose-dependance of the dielectric properties is relatively small, the input impedance of a microwave resonator is still sensitive to such small alterations.

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Oscillatory states in the Electroencephalogram (EEG) reflect the rhythmic synchronous activation in large networks of neurons. Time-frequency methods quantify the spectral content of the EEG as a function of time. As such, they are well suited as tools for the study of spontaneous and induced changes in oscillatory states. We have used time-frequency techniques to analyze the flow of activity patterns between two strongly connected brain structures: the entorhinal cortex and the hippocampus, which are believed to be involved in information storage. EEG was recorded simultaneously from the entorhinal cortex and the hippocampus of behaving rats. During the recording, low-intensity trains of electrical pulses at frequencies between 1 and 40 Hz were applied to the olfactory (piriform) cortex. The piriform cortex projects to the entorhinal cortex, which then passes the signal on to the hippocampus. Several time-frequency methods, including the short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and multiple window (MW) time-frequency analysis (TFA), were used to analyse EEG signals. To monitor the signal transmission between the entorhinal cortex and hippocampus, the time-frequency coherence functions were used. The analysed results showed that stimulation-related power in both sites peaked near 15 Hz, but the coherence between the EEG signals recorded from these two sites increased monotonically with stimulation frequency. Among the time-frequency methods used, the STFT provided time-frequency distributions not only without cross-terms which were present in the WVD, but also with higher resolutions in both time and frequency than the MW-TFA. The STFT seems to be the most suitable time-frequency method to study the stimulation-induced signals presented in this thesis. The MW-TFA, which gives low bias and low variance estimations of the time-frequency distribution when only one realization of data is given, is suitable for stochastic and nonstationary signals such as spontaneous EEG. We also compared the performance of the MW-TFA using two different window functions: Slepian sequences and Hermite functions. By carefully matching the two window functions, we found no noticeable difference in time-frequency plane between them. / Thesis / Master of Engineering (ME)

Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode. / Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.