Publications

Current developments towards multipin, dry electrodes in electroencephalography (EEG) are promising for applications in non-laboratory environments. Dry electrodes do not require the application of conductive gel, which mostly confines the use of gel EEG systems to the laboratory environment. The aim of this study is to validate soft, multipin, dry EEG electrodes by comparing their performance to conventional gel EEG electrodes. Fifteen healthy volunteers performed three tasks, with a 32-channel gel EEG system and a 32-channel dry EEG system: the 40 Hz Auditory Steady-State Response (ASSR), the checkerboard paradigm, and an eyes open/closed task. Within-subject analyses were performed to compare the signal quality in the time, frequency, and spatial domains. The results showed strong similarities between the two systems in the time and frequency domains, with strong correlations of the visual (p = 0.89) and auditory evoked potential (p = 0.81), and moderate to strong correlations for the alpha band during eye closure (p = 0.81-0.86) and the 40 Hz-ASSR power (p = 0.66-0.72), respectively. However, delta and theta band power was significantly increased, and the signal-to-noise ratio was significantly decreased for the dry EEG system. Topographical distributions were comparable for both systems. Moreover, the application time of the dry EEG system was significantly shorter (8 min). It can be concluded that the soft, multipin dry EEG system can be used in brain activity research with similar accuracy as conventional gel electrodes.

Monitoring of the temperature distribution inside the tissue to be treated plays the key role in success and safety of thermal therapies. Active microwave imaging represents a promising approach for non-invasive tissue temperature monitoring during hyperthermia treatment. In the present paper, an approach for quantitative non-invasive tissue temperature estimation via UWB imaging is described. Two types of imaging algorithms are considered - Delay and Sum beamforming and Truncated Singular Value Decomposition scheme. The capabilities of the proposed imaging algorithms are demonstrated by experiments with liquid phantoms. The results of our investigation show that both imaging algorithms can be applied for detection and estimation of the temperature induced dielectric property changes.

Convolutional Neural Networks (CNN) are widely used for image classification and have achieved significantly accurate performance in the last decade. However, they require computationally intensive operations for embedded applications. In recent years, FPGA-based CNN accelerators have been proposed to improve energy efficiency and throughput. While dynamic partial reconfiguration (DPR) is increasingly used in CNN accelerators, the performance of dynamically reconfigurable accelerators is usually lower than the performance of pure static FPGA designs. This work presents a dynamically reconfigurable CNN accelerator architecture that does not sacrifice throughput performance or classification accuracy. The proposed accelerator is composed of reconfigurable macroblocks and dynamically utilizes the device resources according to model parameters. Moreover, we devise a novel approach, to the best of our knowledge, to hide the computations of the pooling layers inside the convolutional layers, thereby further improving throughput. Using the proposed architecture and DPR, different CNN architectures can be realized on the same FPGA with optimized throughput and accuracy. The proposed architecture is evaluated by implementing two different LeNet CNN models trained by different datasets and classifying different classes. Experimental results show that the implemented design achieves higher throughput than current LeNet FPGA accelerators.

With a limited number of workers or staff in the hospital, it is not possible to manually monitor all of the medical device in the hospital. Many medical devices were lost by mistake, many assets went unused because they were not well-stocked, and many assets were destroyed without recognizing it. This will undoubtedly be very negative to hospitals in terms of resources, which are often expensive, and will, of course, diminish the effectiveness and efficiency of medical services. The necessity for hospitals to modernize their technology is apparent. The rapid advancement of technology allows us to overcome these issues, in fact, IoT-based technology is now so advanced that paper-based technology must be gradually phased out. The technology is a real-time location system (RTLS), there are many different ways to implement this technology, one of them is to use Ultra-Wide band (UWB) technology, with this solution, hospitals can track the location of their medical device, as well as other information - this including the Transesophageal Echo (TEE) Probe. DWM1001 is one of the UWB modules that researchers can develop, but its deployment in hospitals still need more research and reliability. This study will address techniques for improving the reliability of anchor mapping and hybrid Wi-Fi solutions as backup solutions.