Wyniki wyszukiwania - BazTech

1

An FPGA-based proof of a concept solution for datacentre energy efficiency improvement

Kraft M., Tomczyński J., Mańkowski T., Fularz M.

Measurement Automation Monitoring

|

2016

|

Vol. 62, No. 5

160--162

EN

As the Internet becomes more and more widespread, the power consumption associated with the Internet infrastructure grows rapidly, contributing to a significant increase in operational costs of service providers. The paper presents a proof of a concept solution consisting of an FPGA expansion card with a dedicated image processing accelerator, connected to a server via PCI Express interface. The use of a dedicated accelerator allows faster completion of the task performed by the server, resulting in over tenfold improvement in energy efficiency.

2

A test bench setup with programmable trajectory for IMU-based dataglove development

Mańkowski T., Tomczyński J., Kaczmarek P.

Measurement Automation Monitoring

|

2016

|

Vol. 62, No. 5

178--180

EN

This paper presents a test bench created during development of an IMU-based dataglove. The system allows gathering synchronised hand phantom posture and trajectory, together with inertial sensor data for evaluation of posture recognition algorithms. The setup includes an inertial measurement system, with IMU modules designed to be mounted on human hand or a phantom, together with proper embedded firmware and PC software. The system uses a collaborative Universal Robots UR3 robot arm for generation of trajectories and as a source of ground truth data. The program used to control the robot together with a method of gathered data synchronisation are described. The test bench has been successfully used during development of a joint angles estimating algorithm.

3

The EOG event recognition method in an EEG signal towards SSVEP BCI improvement

Kaczmarek P., Mańkowski T., Tomczyński J.

Measurement Automation Monitoring

|

2015

|

Vol. 61, No. 7

376--378

EN

This paper presents a method of recognizing EOG artifacts in an EEG signal. Moreover, it shows the possibility of determining the direction of eye movement. The idea behind this method is to develop a hybrid brain-computer interface relying on SSVEP phenomena and EOG artifacts acquired from the EEG signal. Recognition of an EOG event and its direction can be used to improve the SSVEP detection accuracy, overall system responsiveness, and increase the information transfer rate (ITR). Eye movement direction is recognized using a decision tree and histogram-based features calculated from EEG signals recorded in Fp1-O1 and Fp2-O2 points. The accuracy of 75% was achieved for a group of 8 subjects, while the average precision of detecting movement direction in horizontal plane was 78%.