The progressive scaling in semiconductor technology allows for advanced miniaturization of intelligent systems like implantable biosensors for low-molecular weight analytes. A most relevant application would be the monitoring of glucose in diabetic patients, since no commercial solution is available yet for the continuous and drift-free monitoring of blood sugar levels. We report on a biosensor chip that operates via the binding competition of glucose and dextran to concanavalin A. The sensor is prepared as a fully embedded micro-electromechanical system and operates at GHz frequencies. Glucose concentrations derive from the assay viscosity as determined by the deflection of a 50 nm TiN actuator beam excited by quasi-electrostatic attraction. The GHz detection scheme does not rely on the resonant oscillation of the actuator and safely operates in fluidic environments. This property favorably combines with additional characteristics-(i) measurement times of less than a second, (ii) usage of biocompatible TiN for bio-milieu exposed parts, and (iii) small volume of less than 1 mm-to qualify the sensor chip as key component in a continuous glucose monitor for the interstitial tissue.

No abstract

No abstract

No abstract

An integration concept for an implantable biosensor for the continuous monitoring of blood sugar levels is presented. The system architecture is based on technical modules used in cardiovascular implants in order to minimize legal certification efforts for its perspective usage in medical applications. The sensor chip operates via the principle of affinity viscometry, which is realized by a fully embedded biomedical microelectromechanical systems (BioMEMS) prepared in 0.25-µm complementary metal–oxide–semiconductor (CMOS)/BiCMOS technology. Communication with a base station is established in the 402–405 MHz band used for medical implant communication services (MICS). The implant shall operate within the interstitial tissue, and the hermetical sealing of the electronic system against interaction with the body fluid is established using titanium housing. Only the sensor chip and the antenna are encapsulated in an epoxy header closely connected to the metallic housing. The study demonstrates that biosensor implants for the sensing of low-molecular-weight metabolites in the interstitial may successfully rely on components already established in cardiovascular implantology.

antenna is the suitable structure for ISM band frequency of 2.45 GHz for the field of biomedical engineering applications. ABSTRACT: Evaluation of ground-penetrating radar measurements is often done manually by an operator, which is a time-intensive task. However, for a large number of images, an automatization of radargrams by means of digital signal processing is advantageous and demanded. An appropriate technique for automated extraction of hyperbolas' parameters, which in turn can be used to draw conclusions on the background medium's properties as well as the targets under investigation buried in ground, is, for example, among many others, the Hough transform. However, the Hough transformation is difficult to handle in real-world scenarios due to its demanding computational requirements.In either cases, a preselection of domains potentially containing hyperbola structures is of great interest: in the case of manual evaluation, highlighting appropriate domains in the radargrams may provide visual support to the operator, hence accelerating the process. For subsequent digital signal processing, a preselection may significantly reduce processing time as only domains of interest are passed to the next processing stage. In this article, we present an approach by means of which such a preselection of domains containing hyperbola patterns can be obtained. Our approach incorporates phase congruency (PC), which yields a contrast-independent measure for feature content; hence, it is applicable also for low-contrast radar data corrupted by noise and clutter. Furthermore, our approach may be used to highlight appropriate features to increase their contrast utilizing PC for hyperbola identification.

No abstract

No abstract