Sub-gigahertz bandwidth all-optical tunable differentiator with high-energy efficiency based on a micro-ring resonator.

We propose and demonstrate a tunable fractional-order photonic differentiator (DIFF) that can process input pulses with a sub-gigahertz bandwidth. Our scheme utilizes the self-induced optical modulation effect observed in a silicon-on-insulator micro-ring resonator. Gaussian-like pulses with varying pulse widths between 7.5 and 20 ns are employed for differentiation, achieving an energy efficiency over 45%, to the best of our knowledge, which surpasses all previously reported schemes for input pulses with a sub-gigahertz bandwidth. We simulate the temporal dynamics of pulses to gain insight into the physical mechanisms underlying the differentiated outputs and provide a method for differentiation order adjustment, which is experimentally realized using an all-optical pump-probe technique.

Unmanned Aerial Vehicle Cooperative Data Dissemination Based on Graph Neural Networks.

Unmanned Aerial Vehicles (UAVs) have critical applications in various real-world scenarios, including mapping unknown environments, military reconnaissance, and post-disaster search and rescue. In these scenarios where communication infrastructure is missing, UAVs will form an ad hoc network and perform tasks in a distributed manner. To efficiently carry out tasks, each UAV must acquire and share global status information and data from neighbors. Meanwhile, UAVs frequently operate in extreme conditions, including storms, lightning, and mountainous areas, which significantly degrade the quality of wireless communication. Additionally, the mobility of UAVs leads to dynamic changes in network topology. Therefore, we propose a method that utilizes graph neural networks (GNN) to learn cooperative data dissemination. This method leverages the network topology relationship and enables UAVs to learn a decision policy based on local data structure, ensuring that all UAVs can recover global information. We train the policy using reinforcement learning that enhances the effectiveness of each transmission. After repeated simulations, the results validate the effectiveness and generalization of the proposed method.

Polarization-insensitive antisymmetric multimode waveguide Bragg grating filter based on an SiN-Si dual-layer stack.

A polarization-insensitive multimode antisymmetric waveguide Bragg grating (MASWBG) filter based on an SiN-Si dual-layer stack is demonstrated. Carefully optimized grating corrugations patterned on the sidewall of a silicon waveguide and SiN overlay are used to perturbate TE and TM modes, respectively. Furthermore, the lateral-shift apodization technique is utilized to improve the sidelobe suppression ratio (SLSR). A good overlap between the passbands measured in TE and TM polarization states is obtained. Insertion losses, SLSRs, and 3-dB bandwidths of measured passbands in TE/TM polarizations are 1/1.72 dB, 18.5/19.1 dB, and 5.1/3.5 nm, respectively.

Sparse 2-D optical phased array with large grating-lobe-free steering range based on an aperiodic grid.

Two-dimensional (2-D) optical phased arrays (OPAs) usually suffer from limited scan ranges and small aperture sizes. To overcome these bottlenecks, we utilize an aperiodic 32 × 32 grid to increase the beam scanning range and furthermore distribute 128 grating antennas sparsely among 1024 grid points so as to reduce the array element number. The genetic algorithm is used to optimize the uneven grid spacings and the sparse distribution of grating antennas. With these measures, a 128-channel 2-D OPA operating at 1550 nm realizes a grating-lobe-free steering range of 53° × 16°, a field of view of 24° × 16°, a beam divergence of 0.31° × 0.49°, and a sidelobe suppression ratio of 9 dB.

Chest pain with ST-elevation in leads AVR, III, AVF, V1, V3R, V4R and V5R.

We mistakenly diagnosed a patient with pulmonary embolism as ST-elevation acute myocardial infarction. The case complained of chest pain with ST-elevation in leads AVR, III, AVF, V1, V3R, V4R and V5R. In addition, we also found ST- elevation AVR, III>ST- elevation AVF, QIII>QAVF and QV1. Multi-site pulmonary embolism can lead to type 2 myocardial infarction or special type of myocardial infarction with non-obstructive coronary arteries through multiple pathological mechanisms.

Wavelet convolutional neural network for robust and fast temperature measurements in Brillouin optical time domain reflectometry.

In this paper, a wavelet convolutional neural network (WNN) consisting of a one-dimensional (1D) convolutional neural network and a self-adaptive wavelet neural network has been proposed and demonstrated experimentally for temperature measurement in a Brillouin optical time domain reflectometry (BOTDR) system. Based on the analysis of the system noise, it follows the Gaussian white noise distribution along the time-related sensing distance. The impact of the noise in time-domain on the measured Brillouin gain spectra (BGSs) could be neglected, so that the BGSs in the fiber can be regarded as a series of 1D input data of the proposed WNN. Different self-adaptive wavelet activation functions connected to each output of the full-connection network are adopted to realize the multi-scaled analysis and the scale translation, which can obtain more local characteristics in frequency-domain. The output extracted by the WNN is Brillouin frequency shift (BFS), which presents linearity correlation to the actual temperature. Considering the multi-parameters including different frequency ranges, signal-to-noise-ratios (SNRs), BFSs and spectral widths (SWs), a general model of the proposed WNN is trained to handle more extreme cases, in which it doesn't require retraining for different single-mode (SM) optical fibers in BOTDR sensing system. The performances of the WNN are compared with other two techniques, the Lorentzian curve fitting based on Levenberg-Marquardt (LM) algorithm and the basic neural network (NN) containing input and output layers together with two hidden layers. Both the simulated and measured results show that the WNN has better robustness and flexibility than the LM and the NN. Besides, the computational accuracy of the WNN is improved and the fluctuation of that is slighter, especially when the SNR is less than 11 dB. Moreover, the WNN takes approximately 0.54 s to measure the temperature from the 18,000 collected BGSs transmitted through the 18 km SM optical fiber. The calculating time of the WNN is greatly reduced by three orders of magnitude in comparison with that of the LM, and is comparable to that of the NN. It proves that the proposed WNN may provide a feasible or even better scheme for the robust and fast temperature measurement in BOTDR system.

Silicon-based high-power traveling wave photodetector with inductive gain peaking.

We demonstrate Ge/Si high-power and high-speed distributed traveling wave photodetectors (TWPD) by using the inductive gain peaking technique. Input terminals of TW electrodes are open to enhance RF output efficiencies to output loads. Furthermore, optimized on-chip spiral inductors are incorporated at output terminals of TW electrodes to alleviate bandwidth degradations caused by the absences of matching impedances. A comprehensive equivalent circuit model is developed to calculate the frequency response of this scheme. It is used to optimize the design, and then is validated by measurement results. After inducing on-chip inductors, the bandwidths of 4-stage and 8-stage TWPDs are improved from 32 to 44 GHz and 16 to 24 GHz, respectively. Maximum RF output powers of 4-stage and 8-stage TWPDs with on-chip inductors are measured to be 5.7 dBm and 9.4 dBm at 20 GHz, respectively.

Ultra-compact silicon mode (de)multiplexer based on directional couplers with subwavelength sidewall corrugations.

Asymmetrical directional couplers aided with subwavelength sidewall corrugations are used to realize ultra-compact silicon mode (de)multiplexers at C-band. Three mode (de)multiplexers with ultra-short coupling lengths of 5.6/6.5/7.7 µm are designed to enable low-loss mode conversions between TE0 and TE1/2/3 modes. They are then cascaded to build a four-channel mode-division-multiplexing (MDM) link. The four mode channels present minimal on-chip insertion losses of 0.2/0.7/0.7/0.9 dB at their peak wavelengths. Measured cross talk levels of the four mode channels are better than -18.0/-19.1/-16.0/-18.2 dB within the wavelength range from 1530 nm to 1580 nm.

Rebound sign: a case report and review of literature.

We reviewed previously reported imaging features of coronary air embolism. We also reported an unusual image 'rebound sign' of proximal coronary artery air embolism for the first time, that is, with the regular contraction and relaxation of the heart, the contrast medium near the embolus rebounds regularly, and the smooth gas-liquid boundary is alternately clear and blurred. We conclude that myocardial bridge plays a role in the formation of 'rebound sign.'Abbreviations: CAE: Coronary Air Embolism; CAG: Coronary Angiography; RCA: Right Coronary Artery; LCA: Left Coronary Artery; LAD: Left Anterior Descending Coronary Artery; LCX: Left Circumflex Artery; MB: Myocardial Bridge; STEMI: ST Segment Elevation Myocardial Infarction; TOE: Transesophageal Echocardiography.

Polarization-independent fiber-chip grating couplers optimized by the adaptive genetic algorithm.

One-dimensional polarization-independent grating couplers are demonstrated with the aid of the adaptive genetic algorithm optimization. By adjusting the relative weight between the coupling efficiency and the bandwidth of the polarization-dependent loss (PDL), we control the evolution direction and customize the final performance of the device. Two specific designs are generated by giving more weight to the coupling efficiency and the PDL bandwidth, respectively. Coupling efficiencies of the first design are measured to be -7.6dB and -7.9dB at 1550 nm for TE and TM polarizations, respectively, while its 1.0 dB PDL bandwidth is 25.0 nm. In contrast, the second design presents higher coupling efficiencies of -7.6dB and -7.2dB at 1550 nm for TE and TM polarizations, respectively. However, its 1.0 dB PDL bandwidth is 22.0 nm.

CMBF: Cross-Modal-Based Fusion Recommendation Algorithm.

A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-Modal-Based Fusion Recommendation Algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results.

Accent Recognition with Hybrid Phonetic Features.

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the AESRC dataset. The results demonstrate that our approach can obtain an 8.02% relative improvement compared with the Transformer baseline, showing the merits of the proposed method.

Hitless and gridless reconfigurable optical add drop (de)multiplexer based on looped waveguide sidewall Bragg gratings on silicon.

Reconfigurable optical add-drop filters in future intelligent and software controllable wavelength division multiplexing networks should support hitless wavelength switching and gridless bandwidth tuning. The hitless switching implies that the central wavelength of one channel can be shifted without disturbing data transmissions of other channels, while the gridless tuning means that the filter bandwidth can be adjusted continuously. Despite a lot of efforts, very few integrated optical filters simultaneously support the hitless switching of central wavelength and the gridless tuning of bandwidth. In this work, we demonstrate a hitless add-drop filter with gridless bandwidth tunability on the silicon-on-insulator (SOI) platform. The filter comprises the two identical multimode anti-symmetric waveguide Bragg gratings (MASWBG) which are connected to a loop. The phase apodization technique is utilized to weaken the intrinsic sidelobe interference of grating-based devices. By sequentially manipulating central wavelengths of the two MASWBGs with the thermo-optical effect, we can reconfigure the spectral response of the filter gridlessly and hitlessly. Specifically, the central wavelength of the device is shifted by 14.5 nm, while its 3 dB bandwidth is tuned from 0.2 nm to 2.4 nm. The dropping loss and the sidelobe suppression ratio (SLSR) are dependent on the bandwidth selected. Measured variation ranges of dropping loss and SLSR are from -1.2 dB to -2.5 dB and from 12.8 dB to 21.4 dB, respectively. The hitless wavelength switching is verified by a data transmission measurement at a bit rate of 25 Gbps.

Twin-Fano resonator with widely tunable slope for ultra-high-resolution wavelength monitor.

A twin-Fano resonator based on a Mach-Zehnder interferometer (MZI) is demonstrated on a silicon-on-insulator platform. A dual-microring resonator replaces one of the couplers of the MZI to achieve twin-Fano resonance, which originates from the interference and coupling of modes in a dual-microring resonator. The slope can be tuned in a wide range from -84.2 dB/nm to 91.0 dB/nm by metal heaters integrated on one arm of the MZI, and the resonant wavelength remains fixed when the slope changes. The "X-type" spectrum is shown by self-alignment, which means manual alignment to form the X-type line is unnecessary after tuning dual-microrings because the X-type line can be produced easily by the difference in two correlated spectra rather than two independent spectra. The measurement shows high wavelength resolution of 1 pm in the region of the slope of 127.4 dB/nm, which can be applied to wavelength monitoring with ultra-high resolution.

Experimental demonstration of a flexible-grid 1×2 wavelength-selective switch based on silicon microring resonators.

An integrated flexible-grid 1×2 wavelength-selective switch based on reconfigurable add-drop silicon microring resonators using strip waveguides is designed and experimentally demonstrated. By flexibly tuning the resonance of microring resonators and path phase differences via the thermo-optic effect, the transmission spectra with adjustable bandwidths can be formed as desired at the output ports. Our experimental results reveal that the fabricated flexible-grid 1×2 wavelength-selective switch provides crosstalk lower than -10.39 dB. The 3-dB bandwidth varies from 0.38 nm to 1 nm, the in-band ripple is less than 0.52 dB, and the response time is about 17.6 µs.

Induced carotid atherosclerosis in lupus mice.

Fas knockout (Fas-/-) mice are a model for systemic lupus erythematosus (SLE) -like autoimmune syndromes. We aimed to induce atherosclerosis (AS) in Fas-/- mice. Sixteen male Fas-/- mice were included in the study, sex-matched C57B6/L (B6) and apolipoprotein E-knockout (ApoE-/-) mice were negative and positive AS controls, respectively. A silica collar was placed around the right carotid artery of each mouse to induce AS development. All mice were fed a 24-week high-fat diet, and investigated for AS lesions. We also compared the levels of blood lipid and glucose, serum tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), anti-nuclear antibody (ANA) and anti-double-stranded deoxyribonucleic acid (anti-dsDNA) antibody in Fas-/- mice with those in B6 or ApoE-/- mice. All ApoE-/- and 6 Fas-/- but no B6 mice showed atherogenesis in right carotid artery. The carotid plaque contains more collagen and less lipid in Fas-/- than ApoE-/- mice. The levels of blood glucose, serum TNF-α, IL-6, ANA, and anti-dsDNA antibody were significantly higher in Fas-/- mice than those in B6 mice, the levels of serum TNF-α and blood glucose were significantly higher and the level of blood lipid was significantly lower in Fas-/- mice than those in ApoE-/- mice. Therefore, carotid AS can develop in Fas-/- mice. Fas-/- mice display higher levels of serum IL-6, TNF-α, ANA, and anti-dsDNA than B6 mice, higher levels of serum TNF-α and blood glucose and lower level of blood lipid than ApoE-/- mice, and less lipid and more collagen in AS plaque than ApoE-/- mice.

Continuously tunable true-time delay lines based on a one-dimensional grating waveguide for beam steering in phased array antennas.

In this paper, we propose integrated one-dimensional (1D) grating waveguide-based true-time delay (TTD) lines on a silicon-on-insulator (SOI) platform. Through optimizing the structure of the proposed waveguide, a time delay of 77.23 ps/mm can be readily achieved in a wavelength tuning range from 1540.20 nm to 1558.97 nm. Compared to conventional photonic crystal waveguide-based TTDs, the proposed waveguide occupies 70% less chip surface and has much lower propagation loss when compared with 2D photonic crystal devices. Therefore, a larger time delay can be achieved on-chip. To facilitate low loss coupling from strip waveguides to 1D grating waveguides and vice versa, a novel step taper is designed and shows a coupling efficiency of over 78%. Based on the 1D grating waveguide, a 1×4 beam steering module is designed and simulated. A wide beam steering angle from -67.84° to 67.84° for the X-band four-element phased array antenna with an array pitch size of 1.25 cm is obtained.

A Bayesian Scene-Prior-Based Deep Network Model for Face Verification.

Face recognition/verification has received great attention in both theory and application for the past two decades. Deep learning has been considered as a very powerful tool for improving the performance of face recognition/verification recently. With large labeled training datasets, the features obtained from deep learning networks can achieve higher accuracy in comparison with shallow networks. However, many reported face recognition/verification approaches rely heavily on the large size and complete representative of the training set, and most of them tend to suffer serious performance drop or even fail to work if fewer training samples per person are available. Hence, the small number of training samples may cause the deep features to vary greatly. We aim to solve this critical problem in this paper. Inspired by recent research in scene domain transfer, for a given face image, a new series of possible scenarios about this face can be deduced from the scene semantics extracted from other face individuals in a face dataset. We believe that the "scene" or background in an image, that is, samples with more different scenes for a given person, may determine the intrinsic features among the faces of the same individual. In order to validate this belief, we propose a Bayesian scene-prior-based deep learning model in this paper with the aim to extract important features from background scenes. By learning a scene model on the basis of a labeled face dataset via the Bayesian idea, the proposed method transforms a face image into new face images by referring to the given face with the learnt scene dictionary. Because the new derived faces may have similar scenes to the input face, the face-verification performance can be improved without having background variance, while the number of training samples is significantly reduced. Experiments conducted on the Labeled Faces in the Wild (LFW) dataset view #2 subset illustrated that this model can increase the verification accuracy to 99.2% by means of scenes' transfer learning (99.12% in literature with an unsupervised protocol). Meanwhile, our model can achieve 94.3% accuracy for the YouTube Faces database (DB) (93.2% in literature with an unsupervised protocol).

A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702.

Bandwidth and wavelength tunable optical passband filter based on silicon multiple microring resonators.

An ultra-compact silicon bandpass filter with wide bandwidth tunability is proposed and experimentally demonstrated. The filter architecture is based on a multiple micro-ring resonator-cascaded structure. A wide bandwidth tunability (from 75 to 300 GHz) can be achieved by controlling the resonant frequency of the microring resonators when a good shape factor (0.24-0.44) is held. The filter has a wide free spectral range (about 1.2 THz). The center wavelength can be tuned over several nanometers linearly. The footprint is only 0.053 mm2.