An autonomous biochemical sensor capsule for the remote monitoring of an algae culture cultivated in a photobioreactor is presented. The encapsulated system has a spherical shape with a diameter of 44 mm. Radio communication with a carrier frequency of 433 MHz is used to transfer the acquired sensor data in the bioreactor to a base station outside. Experiments with the first prototype inside the bioreactor filled with nutrient solution were performed to determine the reliability of the communication link. The packet error rate converged to 26% and the path loss on average was 96 dB. The functionality of the signal processing chain of acquired biochemical sensor values was evaluated using an experimental setup. Our second capsule prototype with potentiometric sensors demonstrates the pH-measurements of different buffer solutions dropped on the sensor electrodes with a pipette.
A biochemical sensor capsule for the remote monitoring of an algae culture cultivated in a photobioreactor is presented. In this context a concept of data acquisition and transmission is developed. The data transmission from inside the bioreactor to a receiver outside was analyzed. Hereby recorded packet error rate converged to 30 %
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.
For the emergency medical service a reliable sharing and transmitting of medical data and electronic patient records between the ambulance and the hospital is of great importance for the quality of patient care. During the last years new mobile technologies have evolved with much higher bandwidths than before. Nevertheless, the available network bandwidth strongly varies from area to area. In this paper we present an approach to investigate the predictability of the mobile network performance based on the statistical evaluation of the gathered transmission data on frequently used routes. This allows one to pro-actively adapt the available bandwidth to improve the utilization of the mobile network capacity and achieve a more reliable transmission of the medical data. We performed data rate measurements on different routes and derived predictions of the available mobile resources. As a first approach, the predictions are based on the average of former measurements, but taking into account the current position and temporal variation of the mobile resources. Compared with the results obtained through the straightforward reference approach our predictions are more accurate and precise. Consequently, our empirical studies confirm that despite high variations of the available wireless bandwidth reasonable predictions for known routes are possible.
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