Orbital magnetization senses topological phase transition in spin-orbit coupled α\alpha-T3T_3 system

The $\alpha$-$T_3$ system undergoes a topological phase transition(TPT) between two distinct quantum spin-Hall phases across $\alpha=0.5$ when the spin-orbit interaction of Kane-Mele type is taken into consideration. As a hallmark of such a TPT, we find that the Berry curvature and the orbital magnetic moment change their respective signs across the TPT. The trails of the TPT found in another physical observable e.g. the orbital magnetization(OM) are understood in terms of valley and spin physics. The valley-resolved OM(VROM) and the spin-resolved OM(SROM) exhibit interesting characteristics related to the valley and the spin Chern number when the chemical potential is tuned in the forbidden gap(s) of the energy spectrum. In particular, we find that the slope of the VROM vs the chemical potential in the forbidden gap changes its sign abruptly across the TPT which is also consistent with the corresponding change in the valley Chern number. Moreover, the slope of the SROM demonstrates a sudden jump by one unit of $e/h$, (where $e$ is the electronic charge and $h$ is the Planck's constant), across the TPT which is also in agreement with the corresponding change in the spin Chern number.Comment: 10 pages, 9 figure

Floquet engineering of low-energy dispersions and dynamical localization in a periodically kicked three-band system

Much having learned about Floquet dynamics of pseudospin-$1/2$ system namely, graphene, we here address the stroboscopic properties of a periodically kicked {three-band fermionic system such as $\alpha$-T$_3$ lattice. This particular model provides an interpolation between graphene and dice lattice via the continuous tuning of the parameter $\alpha$ from 0 to 1.} In the case of dice lattice ($\alpha=1$), we reveal that one can, in principle, engineer various types of low energy dispersions around some specific points in the Brillouin zone by tuning the kicking parameter in the Hamiltonian along a particular direction. Our analytical analysis shows that one can experience different quasienergy dispersions for example, Dirac type, semi-Dirac type, gapless line, absolute flat quasienergy bands, depending on the specific values of the kicking parameter. Moreover, we numerically study the dynamics of a wave packet in dice lattice. The quasienergy dispersion allows us to understand the instantaneous structure of wave packet at stroboscopic times. We find a situation where absolute flat quasienergy bands lead to a complete dynamical localization of the wave packet. {Aditionally, we calculate the quasienergy spectrum numerically for $\alpha$-T$_3$ lattice. A periodic kick in a perpendicular (planar) direction breaks (preserves) the particle-hole symmetry for $0<\alpha<1$. Furthermore, it is also revealed that the dynamical localization of wave packet does not occur at any intermediate $\alpha \ne 0,\,1$.}Comment: 12 pages, 11 figure

HUMS2023 Data Challenge Result Submission

We implemented a simple method for early detection in this research. The implemented methods are plotting the given mat files and analyzing scalogram images generated by performing Continuous Wavelet Transform (CWT) on the samples. Also, finding the mean, standard deviation (STD), and peak-to-peak (P2P) values from each signal also helped detect faulty signs. We have implemented the autoregressive integrated moving average (ARIMA) method to track the progression.Comment: This report is being submitted as part of the Data Challenge organized by HUmS202

FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling

In this paper, we present FVR-Net, which is a novel finger vein recognition network using a convolutional neural network (CNN) with a hybrid pooling mechanism. The scheme is based on the use of a block-wise feature extraction network to extract discrete features from interclass vein image samples, regardless of their visual quality. Input images to FVR-Net are subjected to preprocessing prior to being fed into the network in order to segment the vein patterns from the background. We designed a feature extraction network in which each block consists of a convolutional layer followed by hybrid pooling, whose output activation maps are concatenated before passing features to another block within the network. In the hybrid pooling layer, two subsampling layers of maxpooling and average pooling are placed in parallel where the former activates the most discrete features of the input, and the latter considers the complete extent of the input volume so better localization of features can be accessed. After the features are extracted, they are passed to three fully connected layers (FCLs) for classification. We conduct several experiments on two publicly available finger vein datasets based on visual quality of the images. When compared to conventional studies, the proposed model achieves outstanding recognition performance with accuracies of up to 97.84% and 97.22% for good and poor-quality images, respectively. By varying multiple network hyperparameters, we obtain optimal settings such that the model can guarantee the best recognition accuracy for a finger vein biometric system

Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves a high-quality reconstruction of the ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while providing compelling SR reconstruction time

Two new species of the South Asian catfish genus Pseudolaguvia from northeastern India (Teleostei: Sisoridae)

Tamang, Lakpa, Sinha, Bikramjit (2014): Two new species of the South Asian catfish genus Pseudolaguvia from northeastern India (Teleostei: Sisoridae). Zootaxa 3887 (1): 37-54, DOI: http://dx.doi.org/10.11646/zootaxa.3887.1.

FIGURE 5 in Physoschistura walongensis, a new species of loach (Teleostei: Nemacheilidae) from Arunachal Pradesh, northeastern India

FIGURE 5. Faroff view of Lohit River at Walong showing the type locality of Physoschistura dikrongensis (indicated by arrow)

Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication

Recently, digital displays and cameras have been extensively used as new data transmission and reception devices in conjunction with optical camera communication (OCC) technology. This paper presents three types of frequency-based data-embedding mechanisms for a display-to-camera (D2C) communication system, in which a commercial digital display transmits information and an off-the-shelf smartphone camera receives it. For the spectral embedding, sub-band coefficients obtained from a discrete cosine transform (DCT) image and predetermined embedding factors of three embedding mechanisms are used. This allows the data to be recovered from several types of noises induced in wireless optical channels, such as analog-to-digital (A/D) and digital-to-analog (D/A) conversion, rotation, scaling, and translation (RST) effects, while also maintaining the image quality to normal human eyes. We performed extensive simulations and real-world D2C experiments using several performance metrics. Through the analysis of the experimental results, it was shown that the proposed method can be considered as a suitable candidate for the D2C system in terms of the achievable data rate (ADR), peak signal-to-noise ratio (PSNR), and the bit error rate (BER)

Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves a high-quality reconstruction of the ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while providing compelling SR reconstruction time