소형 안테나 ( Small Antennas )

Background and History. Resonator and Filter Topologies. Baw Device Basics. Design and Fabrication of BAW Devices. FBAR Resonators and Filters. Comparison with SAW Devices. Films Deposition for BAW Devices. Characterization of BAW Devices. Monolithic Integration. System-in-Package (SiP) Integration. Index.

RF bulk acoustic wave filters for communications

We report the first experimental generation of high-order Mathieu beams and confirm their propagation invariance over a limited range. In our experiment we use a computer-generated phase hologram. The peculiar behaviour of the vortices in Mathieu beams gives rise to questions about their orbital-angular-momentum content, which we calculate by performing a decomposition in terms of Bessel beams.

Holographic generation and orbital angular momentum of high-order Mathieu beams : Special issue on atoms and angular momentum of light

Recently, orbital angular momentum (OAM) based vorticose communication has attracted much attention because of its potential to significantly increase the spectrum efficiency (SE) of wireless communications. However, the multiple radio frequency (RF) chains used for multiple OAM modes lead to an unexpected cost for wireless vorticose communications. In this paper, we first propose the mode modulation (MM) based OAM system to allow multiple OAM modes sharing a common RF chain. The proposed MM cannot only reduce the hardware cost, but also boost the SE by introducing the mode as an additional dimension for data transmission. Moreover, without the channel state information (CSI) feedback, we develop an equal-probability mode modulation (EMM) scheme to maximize the SE of MM-based OAM systems with the limited RF chains. In addition, with the CSI feedback, we develop a Huffman coding based adaptive mode modulation (AMM) scheme, which can adaptively choose the OAM modes to further increase the SEs of OAM-based wireless communications. We also develop the joint power and probability allocation policy for the AMM scheme to achieve the maximum SEs for OAM-based vorticose communications. Numerical results demonstrate that the MM can offer a new mode dimension for vorticose communications and the AMM scheme can achieve larger SE than the EMM scheme.

Mode Modulation for Wireless Communications With a Twist

Electromagnetic (EM) waves with helical wavefront carry orbital angular momentum (OAM), which is associated with the azimuthal phase of the complex electric field. OAM is a new degree of freedom in EM waves and is promising for channel multiplexing in communication system. Although the OAM-carrying EM wave attracts more and more attention, the method of OAM generation at microwave frequencies still faces challenges, such as efficiency and simulation time. In this work, by using the circuit theory and equivalence principle, we build two simplified models, one for a single scatter and one for the whole metasurface to predict their EM responses. Both of the models significantly simplify the design procedure and reduce the simulation time. In this paper, we propose an ultrathin complementary metasurface that converts a left-handed (right-handed) circularly polarized plane wave without OAM to a right-handed (left-handed) circularly polarized wave with OAM of arbitrary orders and a high transmission efficiency can be achieved.

Ultrathin Complementary Metasurface for Orbital Angular Momentum Generation at Microwave Frequencies

An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysis

Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal.

This report presents the design of a dual-port dual-band annular slot antenna suitable for use in the feed-back path of an oscillator resulting in an active antenna configuration. The proposed antenna is designed to radiate and transmit equal amounts of power. The return loss of the proposed antenna is higher than that desired for one-port antenna application at the desired dual-band frequencies. The antenna is fabricated on one side of a Neltec substrate (e r =3.2 and thickness = 0.762 mm) and the microstrip feeding line is fabricated on the opposite side of the board. The antenna was designed to operate at WLAN bands centered at 2.4 and 5.2 GHz. The simulated and measured results show fairly good agreement. The measured return losses were found 20.5 dB at 2.4 GHz and 13.9 dB at 5.2GHz while simulated return losses were 9.36 dB at 2.4 GHz and 31.35 dB at 5.2 GHz. The radiation pattern is approximately omnidirectional at both frequencies in H-plane.

Dual port ASA for frequency switchable active antenna

This letter describes the use of slot array equivalence of single square-shaped patch antenna for the generation of orbital angular momentum (OAM) wave. Single patch antenna with phase-shifted power divider network behaves as 4-element slot array and can generate +1 or −1 OAM mode. Design, analysis, and measurement of the proposed novel OAM antenna that resonates at frequency 5.5 GHz are presented.

OAM Wave Generation Using Square-Shaped Patch Antenna as Slot Array Equivalence

/pdf/one-dimensional-convolutional-neural-network-architecture-2zt7cfb3.pdf

Madhur Deo Upadhayay

Papers

Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal.

Dual port ASA for frequency switchable active antenna

OAM Wave Generation Using Square-Shaped Patch Antenna as Slot Array Equivalence

One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram