Explore the vast range of titles available.

Semiconductor broadband light emitters have emerged as ideal and vital light sources for a range of biomedical sensing/imaging applications, especially for optical coherence tomography systems. Although near-infrared broadband light emitters have found increasingly wide utilization in these imaging applications, the requirement to simultaneously achieve both a high spectral bandwidth and output power is still challenging for such devices. Owing to the relatively weak amplified spontaneous emission, as a consequence of the very short non-radiative carrier lifetime of the inter-subband transitions in quantum cascade structures, it is even more challenging to obtain desirable mid-infrared broadband light emitters. There have been great efforts in the past 20 years to pursue high-efficiency broadband optical gain and very low reflectivity in waveguide structures, which are two key factors determining the performance of broadband light emitters. Here we describe the realization of a high continuous wave light power of >20 mW and broadband width of >130 nm with near-infrared broadband light emitters and the first mid-infrared broadband light emitters operating under continuous wave mode at room temperature by employing a modulation p-doped InGaAs/GaAs quantum dot active region with a 'J'-shape ridge waveguide structure and a quantum cascade active region with a dual-end analogous monolithic integrated tapered waveguide structure, respectively. This work is of great importance to improve the performance of existing near-infrared optical coherence tomography systems and describes a major advance toward reliable and cost-effective mid-infrared imaging and sensing systems, which do not presently exist due to the lack of appropriate low-coherence mid-infrared semiconductor broadband light sources.

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service.

Object detection in aerial images is a challenging task due to various orientations of objects and the lack of discriminative features. Existing methods are usually in a dilemma between accuracy and speed. While one-stage anchor-free detectors inference more quickly than two-stage frameworks, their predictions are not as accurate as that of the opposite. This paper proposes a quick and accurate detector, Mask-guidEd Anchor-free Detector (MEAD). It can rapidly locate oriented objects in aerial images by means of per-pixel prediction. Furthermore, it embeds a cascade architecture to locate targets more precisely. To enhance feature discrimination, the mask-guided branch is employed to force features to attend the foreground regions. Comparative experiments are conducted on DOTA and HRSC2016 datasets. The results show that MEAD is better than current state-of-the-art anchor-free detectors, that is, mAP 74.33 on DOTA and 89.83 on HRSC2016.

Every closed-loop system holds a level of fault tolerance, which could be increased by using a fault tolerant control (FTC) scheme. In this paper, an efficient FTC scheme for a class of nonlinear systems (Euler–Lagrange ones) is proposed, which guarantees high performance and stability in a faulty system. This scheme was designed on the basis of a cascade control structure in which the inner loop is the closed-loop system and the external loop is the FTC, a generalized proportional integral (GPI) observer-based controller, which manages the fault tolerance level increment. An important issue of the proposed scheme is that the GPI observer-based controller jointly estimates disturbances and faults, providing information about the state of health of the system, and then compensates their effect. The scheme is efficient because only the inertia matrix is required for the controller design, it is able to preserve the nominal control law unchanged and can operate properly without explicit information about system faults (fault diagnostic module). Simulation results, on a pendulum model, show the effectiveness of the proposed scheme for tracking control.

Deep learning methods based on convolutional neural networks (CNNs) and Mamba have advanced medical image segmentation, yet two challenges remain: (1) trade-off in feature extraction, where CNNs capture local details but miss global context, and Mamba captures global dependencies but overlooks fine structures, and (2) limited feature aggregation, as existing methods insufficiently integrate inter-layer common information and delta details, hindering robustness to subtle structures. To address these issues, we propose a hybrid cascade and dual-path adaptive aggregation network (HCDAA-Net). For feature extraction, we design a hybrid cascade structure (HCS) that alternately applies ResNet and Mamba modules, achieving a spatial balance between local detail preservation and global semantic modeling. We further employ a general channel-crossing attention mechanism to enhance feature expression, complementing this spatial modeling and accelerating convergence. For feature aggregation, we first propose correlation-aware aggregation (CAA) to model correlations among features of the same lesions or anatomical structures. Second, we develop a dual-path adaptive feature aggregation (DAFA) module: the common path captures stable cross-layer semantics and suppresses redundancy, while the delta path emphasizes subtle differences to strengthen the model’s sensitivity to fine details. Finally, we introduce a residual-gated visual state space module (RG-VSS), which dynamically modulates information flow via a convolution-enhanced residual gating mechanism to refine fused representations. Experiments on diverse datasets demonstrate that our HCDAA-Net outperforms some state-of-the-art (SOTA) approaches.

The early and precise diagnosis of suspected pathological tissues or organs has increasingly embraced the utilization of 3D real-time visualization and discrimination of intricate structures facilitated by miniature optical coherence tomography (OCT) endoscopic probes. Those miniature side-viewing endoscopic fiber probes are indispensable for 3D imaging with small, narrow lumens, eliminating the potential for tissue trauma associated with direct-viewing techniques. Nevertheless, current manufacturing techniques pose limitations on the overall imaging prowess of these miniaturized side-viewing probes, hindering their widespread adoption. To surmount this challenge, an ultra-compact side-viewing OCT fiber-optic endoscopic probe with extended depth of focus (DOF) and high lateral resolution is designed based on the all-fiber composite structure. The quantitative relationship between the imaging performance and the fiber structural parameters has been theoretically analyzed. The imaging performance of the fiber probe can be flexibly tailored by adjusting the geometric parameters of the fiber-optic cascade structure. The applicability and feasibility of fiber probe prototype have been convincingly demonstrated through linear scanning and rotational scanning methodologies. This ultra-compact side-viewing OCT fiber probe’s capacity to deliver microscopic structural insights paves the way for minimally invasive applications, expected to advance the frontier of early and precise diagnosis and treatment of suspected lesion tissues.

This paper investigates the design of the tenth-order allpass phase compensator with cascade structure. This cascade-type compensator consists of five biquadratic (biquad) allpass sections, and those biquad sections are cascade-connected. To design this cascade-type phase compensator, we first derive a phase error function called quintic fractional (QF) error function, and then employ this QF-error function as the cost function that is minimized for optimizing the coefficients of the tenth-order phase compensator. The compensator coefficients are optimized such that the compensator’s phase response fits a prescribed ideal phase response (phase specification) with the maximum of the QF-error function being minimized. The QF-error function is a rational function whose numerator and denominator are the quintics of the unknown compensator coefficients. Utilizing the QF-error function as a cost function in optimizing the compensator’s coefficients enables the nonlinear minimization to be carried out from a reasonably good starting point, which in turn leads to a convergent design solution. This is the key motivation for deriving the QF-error function and then employing it to design the tenth-order cascade-type phase compensator. Moreover, since the tenth-order compensator comprises exclusively the biquad allpass sections, and the stability of the biquad sections is considerably easy to check, one can confirm the stability of the designed tenth-order compensator by checking the stability of the five cascade-connected biquads. Two illustrative design examples are included for demonstrating the usefulness of the QF-error function in terms of starting the minimization from a good initial point and thus producing a convergent solution. The two examples also illustrate the simplicity of utilizing the cascade structure for the stability check.

Due to the multi-scale and spectral features of remote sensing images compared to natural images, there are significant challenges in super-resolution reconstruction (SR) tasks. Networks trained on simulated data often exhibit poor reconstruction performance on real low-resolution (LR) images. Additionally, compared to natural images, remote sensing imagery involves fewer high-frequency components in network construction. To address the above issues, we introduce a new high–low-resolution dataset GF_Sen based on GaoFen-2 and Sentinel-2 images and propose a cascaded network CSWGAN combined with spatial–frequency features. Firstly, based on the proposed self-attention GAN (SGAN) and wavelet-based GAN (WGAN) in this study, the CSWGAN combines the strengths of both networks. It not only models long-range dependencies and better utilizes global feature information, but also extracts frequency content differences between different images, enhancing the learning of high-frequency information. Experiments have shown that the networks trained based on the GF_Sen can achieve better performance than those trained on simulated data. The reconstructed images from the CSWGAN demonstrate improvements in the PSNR and SSIM by 4.375 and 4.877, respectively, compared to the relatively optimal performance of the ESRGAN. The CSWGAN can reflect the reconstruction advantages of a high-frequency scene and provides a working foundation for fine-scale applications in remote sensing.

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.