No abstract
In microwave breast cancer imaging the signals are ultra-wideband and because of this, according to the nyquist theorem, the sampling rate of the signal is vey high. In this paper a novel breast cancer imaging method using compressive sensing (CS) is proposed. In this method, the received signals are sampled with a rate less than the nyquist rate, then using compressive sensing, we reconstruct the original signals and perform the minimum variance distortionless response (MVDR) beamforming for breast cancer imaging. Comparing the results of the proposed method with the results of the previous MVDR beamforming method, we conclude that using our method we can detect the tumor place with lower samples than the nyquist rate. We compare the signal-to-clutter-ratio (SCR) of these two methods.I.
A significant challenge for future virtual reality (VR) applications is to deliver high quality-of-experience, both in terms of video quality and responsiveness, over wireless networks with limited bandwidth. This paper proposes to address this challenge by leveraging the predictability of user movements in the virtual world. We consider a wireless system where an access point (AP) serves multiple VR users. We show that the VR application process consists of two distinctive phases, whereby during the first (proactive scheduling) phase the controller has uncertain predictions of the demand that will arrive at the second (deadline scheduling) phase. We then develop a predictive scheduling policy for the AP that jointly optimizes the scheduling decisions in both phases.In addition to our theoretical study, we demonstrate the usefulness of our policy by building a prototype system. We show that our policy can be implemented under Furion, a Unity-based VR gaming software, with minor modifications. Experimental results clearly show visible difference between our policy and the default one. We also conduct extensive simulation studies, which show that our policy not only outperforms others, but also maintains excellent performance even when the prediction of future user movements is not accurate.
The long time for collecting the data and a considerable amount of data are important technical challenges in microwave imaging for the detection of breast cancer. From the other point of view, compressive sensing (CS) is an interesting representation and analysis of sparse signals. In this study, a new imaging method for monostatic ultra-wideband microwave imaging of breast cancer using CS is presented. Instead of using all of the conventional radar returned signals, a few received signals, by random choosing the antenna, are sufficient for obtaining reliable images even at high noise levels. Using simulations done, it is shown that sparser images are obtained comparing to the delay-and-sum beamforming technique using only a few received signals.
Motor imagery brain computer interface designs are considered difficult due to limitations in subject-specific data collection and calibration, as well as demanding system adaptation requirements. Recently, subject-independent (SI) designs received attention because of their possible applicability to multiple users without prior calibration and rigorous system adaptation. SI designs are challenging and have shown low accuracy in the literature. Two major factors in system performance are the classification algorithm and the quality of available data. This paper presents a comparative study of classification performance for both SS and SI paradigms. Our results show that classification algorithms for SS models display large variance in performance. Therefore, distinct classification algorithms per subject may be required. SI models display lower variance in performance but should only be used if a relatively large sample size is available. For SI models, LDA and CART had the highest accuracy for small and moderate sample size, respectively, whereas we hypothesize that SVM would be superior to the other classifiers if large training sample-size was available. Additionally, one should choose the design approach considering the users. While the SS design sound more promising for a specific subject, an SI approach can be more convenient for mentally or physically challenged users.
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