Multiple Subbands Research Articles

Automatic Newcastle disease detection using sound technology and deep learning method

Newcastle disease (ND) is a common disease in poultry that has a great impact on poultry health and production. ND has destructive effects on the respiratory system, such as altering the acoustic features of bird vocalizations. For this reason, this research proposed a new method, the deep poultry vocalization network (DPVN), for the early detection of ND based on poultry vocalization. The method combined multiwindow spectral subtraction and high-pass filtering to reduce the influence of noise. In order to detect poultry vocalizations automatically, a multiple subband poultry vocalization endpoint detection method was proposed in this paper. The performance of the detection method was evaluated using the intersection-over-union (IOU) between the detected vocalizations and ground truth vocalizations. The recall of the detection method was 95.11%, and the precision was 96.54%. The audio features of poultry vocalizations are extracted by sound technology and used as the input of a deep learning network to recognize the vocalizations of poultry with Newcastle disease. Five different models were compared in the experiments. The method used in this paper achieves the best performance and the highest accuracy, recall and F1-score of 98.50%, 96.60% and 97.33%, respectively. The accuracies within the first, second, third and fourth days after infection were 82.15%, 90.00%, 93.60% and 98.50%, respectively. The experimental results show that the method proposed in this paper can be used to detect Newcastle disease in the early stage. It will be significant for improving animal welfare and the automated monitoring of poultry production.

Subband-resolved momentum-conserved resonant tunneling in monolayer graphene/h-BN/ABA-trilayer graphene small-twist-angle tunneling device

We demonstrate twist-controlled resonant tunneling in a monolayer graphene (MLG)/hexagonal boron nitride (h-BN)/ABA-stacked trilayer graphene (TLG) van der Waals (vdW) junction, in which MLG and TLG flakes are aligned with a small twist angle θ of ∼1.05° between their crystallographic orientations through a thin h-BN barrier. Owing to the small interlayer twist, resonant tunneling attributed to the conservation of momentum and energy was observed between the single linear band of MLG and multiple subbands of TLG. We show that different subbands of TLG—bilayer-graphene-like subbands and a MLG-like subband—exhibit distinctly different resonant tunneling behaviors. Therefore, we demonstrate subband-resolved resonant tunneling. This technique provides a method to determine band parameters (Slonczewski–Weiss–McClure parameters) and probes the band dispersion of different two-dimensional materials by utilizing a MLG electrode.

Double-Matrix Decomposition Image Steganography Scheme Based on Wavelet Transform with Multi-Region Coverage.

On the basis of ensuring the quality and concealment of steganographic images, this paper proposes a double-matrix decomposition image steganography scheme with multi-region coverage, to solve the problem of poor extraction ability of steganographic images under attack or interference. First of all, the cover image is transformed by multi-wavelet transform, and the hidden region covering multiple wavelet sub-bands is selected in the wavelet domain of the cover image to embed the secret information. After determining the hidden region, the hidden region is processed by Arnold transform, Hessenberg decomposition, and singular-value decomposition. Finally, the secret information is embedded into the cover image by embedding intensity factor. In order to ensure robustness, the hidden region selected in the wavelet domain is used as the input of Hessenberg matrix decomposition, and the robustness of the algorithm is further enhanced by Hessenberg matrix decomposition and singular-value decomposition. Experimental results show that the proposed method has excellent performance in concealment and quality of extracted secret images, and secret information is extracted from steganographic images attacked by various image processing attacks, which proves that the proposed method has good anti-attack ability under different attacks.

Motor Imagery EEG Decoding Based on New Spatial-Frequency Feature and Hybrid Feature Selection Method

Feature extraction and selection are important parts of motor imagery electroencephalogram (EEG) decoding and have always been the focus and difficulty of brain-computer interface (BCI) system research. In order to improve the accuracy of EEG decoding and reduce model training time, new feature extraction and selection methods are proposed in this paper. First, a new spatial-frequency feature extraction method is proposed. The original EEG signal is preprocessed, and then the common spatial pattern (CSP) is used for spatial filtering and dimensionality reduction. Finally, the filter bank method is used to decompose the spatially filtered signals into multiple frequency subbands, and the logarithmic band power feature of each frequency subband is extracted. Second, to select the subject-specific spatial-frequency features, a hybrid feature selection method based on the Fisher score and support vector machine (SVM) is proposed. The Fisher score of each feature is calculated, then a series of threshold parameters are set to generate different feature subsets, and finally, SVM and cross-validation are used to select the optimal feature subset. The effectiveness of the proposed method is validated using two sets of publicly available BCI competition data and a set of self-collected data. The total average accuracy of the three data sets achieved by the proposed method is 82.39%, which is 2.99% higher than the CSP method. The experimental results show that the proposed method has a better classification effect than the existing methods, and at the same time, feature extraction and feature selection time also have greater advantages.

An Autofocus Approach for UAV-Based Ultrawideband Ultrawidebeam SAR Data With Frequency-Dependent and 2-D Space-Variant Motion Errors

Unmanned-aerial-vehicle-based (UAV-based) ultrawideband and ultrawidebeam (UWB) synthetic aperture radar (SAR) is very sensitive to atmospheric turbulence and suffers from serious 2-D space-variant motion errors (SVMEs) caused by the ultrawide beam and frequency-dependent phase errors caused by the ultrawideband. This article proposes an autofocus approach for UAV-based UWB SAR data based on the quasi-polar grid fast factorized backprojection (FFBP) imaging framework, multiple subband local autofocus (MSBLA), and trajectory deviation estimation. First, based on an improved weighted phase gradient autofocus (WPGA) method for subband-division local images, MSBLA is introduced to solve the local motion error estimation problem with frequency-dependent phase errors. Then, trajectory deviation estimation based on the weighted least square (WLS) method is performed to solve the 2-D SVME problem. Finally, the subaperture trajectory deviations are fused into a full-aperture trajectory deviation by an improved fusion strategy based on piecewise weighting. This approach is applied to real data from a new UAV-based UWB SAR. The results of both simulation and real data experiments are presented and verify the effectiveness of the proposed approach.

Reconfigurable Nonuniform Filter Bank for Hearing Aid Systems

Filter banks with good reconfigurability are desired in hearing aid systems due to the individual requirements of patients with different hearing loss characteristics. This paper proposes a modularized design approach of a completely reconfigurable nonuniform filter bank for hearing aid systems. The proposed filter bank structure consists of a multiband generation module and a subband extraction module, where the frequency warping technique is adopted. Based on theoretical analyses of second order frequency warping, the subband distribution can be flexibly controlled. Moreover, to adapt the subband extraction module design to the multiband generation module, the relationship between control parameters is analyzed and a mapping formula is derived. Through the co-design of the two modules, superior reconfigurability over existing methods is achieved. Application examples show that the proposed filter bank is able to provide multiple subbands distribution schemes that satisfy the requirements of different audiograms. The proposed filter bank structure has lower complexity, smaller delay and smaller matching errors than existing filter bank structures due to its flexible structure.

Sub-Band Assignment and Power Control for IoT Cellular Networks via Deep Learning

As various Internet of things (IoT) communication services have recently received great attention, the development of resource allocation scheme that can support the connection of a number of IoT devices becomes an important task for next-generation communication systems. Motivated this challenge, we propose deep learning-based optimization algorithms for a joint resource allocation problem in uplink IoT cellular networks, in which the base station uses multiple sub-bands to serve IoT users and inter sub-band interference exists due to spectral leakage. Specifically, to maximize the achievable sum rate of IoT users with low complexity, we develop a two-stage optimization method built on convolutional neural networks (CNNs) that sequentially optimizes sub-band assignment and transmit power control. Moreover, in order to examine the performance according to the neural network structure, the proposed scheme is also implemented through fully-connected neural networks (FNNs) and compared with the CNN-based scheme. Simulation results show that our proposed CNN-based algorithm significantly improves the sum rate and reduces the required computation time compared to previous schemes without deep learning.

EVCsiNet: Eigenvector-Based CSI Feedback Under 3GPP Link-Level Channels

Recently, deep learning based methods have been widely used for wireless communications. In this letter, different from current researches considering full channel state information (F-CSI) feedback, the eigenvector based CSI feedback with deep learning approach is proposed, referred to as EVCsiNet, where the joint eigenvector concatenated from multiple subbands is compressed and recovered at the encoder and decoder, respectively. Simulation results verify the superiority of our schemes over CSI recovery accuracy and feedback overhead compared with conventional methods using codebooks on link-level channel models in different scenarios.

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction.

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.

Design and Analysis of MIMO F-OFDM Systems for 5G and Beyond Wireless Communications

F-OFDM (Filtered-OFDM) is a flexible waveform that has been considered suitable for 5G and beyond systems for its improved spectrum utilization, moderates PAPR, low OOB emission, multiple asynchronous sub-band transmission, and high robustness to frequency selectivity. It can attain a desirable balance between frequency and time localizations for narrow bandwidths. It is also MIMO friendly. In this paper, a comprehensive design and analysis have been made to evaluate the performance of MIMO (4×4) CP-OFDM and F-OFDM systems for message bits transmission using several digital modulation techniques (16-QAM, 16-PSK, 16-DPSK, 64-QAM, 64-PSK, and 64-DPSK), RA channel coding, different windowed (Hanning, Hamming, Blackman, Blackman-Harris, RRC) sinc FIR filters for length N = 513, and MMSE signal detection technique. From MATLAB based simulation results, it is observed that F-OFDM reduces spectrum leakage thus enhances spectrum efficiency than conventional CP-OFDM. F-OFDM based system offers lower BER (Bit Error Rate) performance than CP-OFDM based system.

The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network

The traditional methods for multi-focus image fusion, such as the typical multi-scale geometric analysis theory-based methods, are usually restricted by sparse representation ability and the transferring efficiency of the fusion rules for the captured features. Aiming to integrate the partially focused images into the fully focused image with high quality, the complex shearlet features-motivated generative adversarial network is constructed for multi-focus image fusion in this paper. Different from the popularly used wavelet, contourlet, and shearlet, the complex shearlet provides more flexible multiple scales, anisotropy, and directional sub-bands with the approximate shift invariance. Therefore, the features in complex shearlet domain are more effective. With of help of the generative adversarial network, the whole procedure of multi-focus fusion is modeled to be the process of adversarial learning. Finally, several experiments are implemented and the results prove that the proposed method outperforms the popularly used fusion algorithms in terms of four typical objective metrics and the comparison of visual appearance.

A Simple Multicarrier Transmission Technique Combining Transmit Diversity and Data Multiplexing for Non-Orthogonal Multiple Access

Since the intra-group co-channel interference is introduced intentionally by the non-orthogonal multiple access (NOMA) technique, the reliability of NOMA based networks is limited by far users, while the sum-rate is mainly contributed by near users. In this paper, a NOMA based multiband scheduling policy is proposed to overcome this problem. Unlike conventional NOMA where all users are served in a single frequency band, the proposed scheme divides the entire band into multiple sub-bands, and accommodates the near and far users in different manners by exploring the sub-bands. Particularly, the far users employ repetition-based transmission in all sub-bands, and the maximum ratio combination (MRC) is integrated with successive interference cancellation (SIC) to jointly decode their intended symbols. Closed-form expression of the achievable sum-rate of the proposed scheme is derived, and the outage performance is characterized by the derived upper-bounded outage probability. Numerical results show that the proposed scheme enables NOMA systems to achieve both high reliability and high sum-rate.

High precision measurements of interstellar dispersion measure with the upgraded GMRT

Context. Pulsar radio emission undergoes dispersion due to the presence of free electrons in the interstellar medium (ISM). The dispersive delay in the arrival time of the pulsar signal changes over time due to the varying ISM electron column density along the line of sight. Accurately correcting for this delay is crucial for the detection of nanohertz gravitational waves using pulsar timing arrays. Aims. We aim to demonstrate the precision in the measurement of the dispersion delay achieved by combining 400−500 MHz (BAND3) wide-band data with those at 1360−1460 MHz (BAND5) observed using the upgraded GMRT, employing two different template alignment methods. Methods. To estimate the high precision dispersion measure (DM), we measure high precision times-of-arrival (ToAs) of pulses using carefully generated templates and the currently available pulsar timing techniques. We use two different methods for aligning the templates across frequency to obtain ToAs over multiple sub-bands and therefrom measure the DMs. We study the effects of these two different methods on the measured DM values in detail. Results. We present in-band and inter-band DM estimates of four pulsars over the timescale of a year using two different template alignment methods. The DMs obtained using both these methods show only subtle differences for PSRs J1713+0747 and J1909−3744. A considerable offset is seen in the DM of PSRs J1939+2134 and J2145−0750 between the two methods. This could be due to the presence of scattering in the former and profile evolution in the latter. We find that both methods are useful but could have a systematic offset between the DMs obtained. Irrespective of the template alignment methods followed, the precision on the DMs obtained is about 10−3 pc cm−3 using only BAND3 and 10−4 pc cm−3 after combining data from BAND3 and BAND5 of the uGMRT. In a particular result, we detected a DM excess of about 5 × 10−3 pc cm−3 on 24 February 2019 for PSR J2145−0750. This excess appears to be due to the interaction region created by fast solar wind from a coronal hole and a coronal mass ejection observed from the Sun on that epoch. A detailed analysis of this interesting event is presented.

(Invited) Quantum Dots and Superconductivity in Silicon Systems

Ge/Si core/shell nanowires are suitable candidates for electrically driven spin qubits, and for the creation of Majorana fermions [1]. In highly tuneable hole quantum dots [2, 3], we observe shell filling of new orbitals and corresponding Pauli spin blockade [4]. In nanowires with superconducting Al leads we create a Josephson junction via proximity-induced superconductivity. A gate-tuneable supercurrent is observed with a maximum of ~60 nA [5]. We identify two different regimes: Cooper pair tunnelling via multiple subbands in the open regime the device [6], while near depletion the supercurrent is carried by single-particle levels of a quantum dot operating in the few-hole regime [5,7,8].Secondly, we create ambipolar quantum dots in silicon nanoMOSFETs. We investigate the conformity of Al, Ti and Pd nanoscale gates by means of transmission electron microscopy [9]. We define low-disorder electron quantum dots with Pd gates [10], and depletion-mode hole quantum dots in undoped silicon [11]. For the latter we use fixed charge in a SiO2/Al2O3 dielectric stack to induce a 2DHG at the Si/SiO2 interface. The depletion-mode design avoids complex multilayer architectures requiring precision alignment and allows directly adopting best practices already developed for depletion dots in other material systems. Finally, we have realized ambipolar charge sensing: we have fabricated a single-electron transistor next to a single-hole transistor, and tuned both quantum dots to sense charge transitions of the other quantum dot. Using active charge sensing the single-electron transistor can detect the few-charge regime in the hole quantum dot [12].[1] C. Kloeffel et al., Phys. Rev. B 84, 195314 (2011). [2] M. Brauns et al., Applied Physics Letters 109, p. 143113 (2016). [3] F. Froning et al., Applied Physics Letters 113, p. 073102 (2018). [4] M. Brauns et al., Phys. Rev. B 94, 041441(R) (2016). [5] J. Ridderbos et al., Advanced Materials, 1802257, (2018). [6] J. Xiang et al., Nature Nanotechnology 1, 3 (2006). [7] J. Ridderbos et al., Physical Review Materials, 3, 084803 (2019). [8] J. Ridderbos et al., Nano Letters 20, 1, p. 122 (2020). [9] P. C. Spruijtenburg et al., Nanotechnology, (2018). [10] M. Brauns et al., Scientific Reports 8, 5690, (2018). [11] S. V. Amitonov et al., Applied Physics Letters 112, p. 023102 (2018). [12] A. J. Sousa de Almeida et al., Phys. Rev. B 101, 201301(R) (2020). Figure 1

Spectrum allocation scheme considering spectrum slicing in elastic optical networks

Recent advances in physical layer optics have made slicing of optical bands into multiple subbands with different bandwidths possible. Due to this development, spectrum allocation has become easier in elastic optical networks (EONs). More specifically, owing to the slicing technology, the defragmentation problem in EONs can be addressed easily, i.e., more demands can be fit into empty spectrum slots by breaking the demands as needed using the slicing technology. This paper proposes a spectrum allocation scheme considering the slicing process at any nodes, e.g., source node and intermediate nodes, in EONs. Slicing-and-stitching technology is applied to break the contiguous-spectrum constraint in an EON so that spectrum fragmentation is reduced. While slicing a demand from one node to another, the following questions must be answered: (1) Which parts of the spectrum band should be sliced? (2) In which node(s) along the path of a demand should the slicings be done to reduce the bandwidth blocking rate? To answer the above questions, we formulate a mixed-integer linear programming (MILP) model that jointly addresses the above questions as well as minimizes the total number of slicers in a network. To measure the performance of the MILP, we used the bandwidth blocking ratio (BBR) of the network as a performance metric. Our results from the MILP show that introducing slicing at every node in the network improves the BBR by as much as 68% compared to a conventional case where slicing a demand is allowed only at the source node.

Plane wave beamforming with adaptively weighted frequency compound using bandpass filtering

We previously proposed the frequency and plane-wave compounding minimum variance distortion-free response (FPWC-MVDR) beamformer for ultrasonic reception beamforming, which weights the transmission angle and frequency band adaptively. This method required multiple subband transmissions and receptions for each transmission angle, reducing the frame rate. Here, we propose a filtered-FPWC-MVDR method that extracts and uses subband components via signal processing from the echo received after a single wideband pulse transmission. After confirming that this method provides the same performance as the original FPWC-MVDR, we experimentally investigate the subband conditions required, i.e. the bandwidth and number of subbands, when using this method. As a result, to improve the point target resolution, we find that there is an appropriate subbandwidth condition involving a trade-off between ensuring echo localization and increasing the degrees of freedom for frequency weight determination. Additionally, for improved contrast, the subbands were arranged appropriately to cover the entire usable frequency band.

Approach to reduce PAPR in Orthogonal Frequency Division Multiplexing Technique

Orthogonal Frequency Division Multiplexing (OFDM) is an efficient and attractive modulation technique that can be considered as the heart of all major wireless communication applications used as well as in development today. OFDM is an advanced version of Frequency Division Multiplexing (FDM) technique that separates the channel bandwidth into multiple narrow sub-bands in order to transfer information. With the progress of advance communication field there should be high data rate along with power efficiency and lower Bit Error Rate (BER). Due to advent of Internet of Things (IoT), Artificial Intelligence (AI), 5G and other network demanding technologies network congesting scenarios are becoming common and because of interfering channels noise is observed caused by adjacency of communicating devices. So, the fulfillment of current as well as future demand of high data rate and greater power efficiency are very much essential. These requirements are achieved very well with OFDM technique but at the cost of high Peak to Average Power Ratio (PAPR). High PAPR leads to in band distortion and Out of Band radiation along with power inefficiency while using practical power amplifier at the transmitter. This paper presents a review of OFDM and analysis of a technique for PAPR reduction. Additionally, the scopes and challenges in PAPR reduction have been documented which describes clipping technique to reduce PAPR through MATLAB simulation models.

IRCI‐free multiple subband MIMO SAR

An inter-range cell interference (IRCI)-free multiple subband multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) algorithm is proposed to obtain a high-resolution wide swath (HRWS) imaging. The received signals of the proposed configuration are formulated as multiple-input single-output (MISO) system identification problems using the principle of the displaced phase centre in a way that all the subband waveforms are processed simultaneously without the need to separate them at the receiver. This would maximise the bandwidth utilisation efficiency. The channel impulse response in the range dimension is identified using a frequency domain system identification--based estimation algorithm, which is free from IRCI, instead of using conventional matched filters. A pulse repetition frequency lower than the Doppler bandwidth is used to obtain the HRWS imaging, and the resulted azimuth ambiguity is removed using a set of spatial filters applied on the received signals formulated as separate MISO system identification problems. Finally, both simulated and constructed raw data are used to validate the efficiency of the proposed algorithm.

Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals

Deep brain simulations play an important role to study physiological and neuronal behavior during Parkinson’s disease (PD). Electroencephalogram (EEG) signals may faithfully represent the changes that occur during PD in the brain. But manual analysis of EEG signals is tedious, and time consuming as these signals are complex, non-linear, and non-stationary nature. Therefore EEG signals are required to decompose into multiple subbands (SBs) to get detailed and representative information from it. Experimental selection of basis function for the decomposition may cause system degradation due to information loss and an increased number of misclassification. To address this, an automated tunable Q wavelet transform (A-TQWT) is proposed for automatic decomposition. A-TQWT extracts representative SBs for analysis and provides better reconstruction for the synthesis of EEG signals by automatically selecting the tuning parameters. Five features are extracted from the SBs and classified different machine learning techniques. EEG dataset of 16 healthy controls (HC) and 15 PD (ON and OFF medication) subjects obtained from ”openneuro” is used to develop the automated model. We have aimed to develop an automated model that effectively classify HC subjects from PD patients with and without medication. The proposed method yielded an accuracy of 96.13% and 97.65% while the area under the curve of 97% and 98.56% for the classification of HC vs PD OFF medication and HC vs PD ON medication using least square support vector machine, respectively.