The need for improved diagnostic methods in ophthalmology is acute, especially in the underdeveloped regions with limited access to specialists and advanced equipment. Therefore, we introduce VisionUnite, a novel vision-language foundation model for ophthalmology enhanced with clinical knowledge. VisionUnite has been pretrained on an extensive dataset comprising 1.24 million image-text pairs, and further refined using our proposed MMFundus dataset, which includes 296,379 high-quality fundus image-text pairs and 889,137 simulated doctor-patient dialogue instances. Our experiments indicate that VisionUnite outperforms existing generative foundation models such as GPT-4V and Gemini Pro. It also demonstrates diagnostic capabilities comparable to junior ophthalmologists. VisionUnite performs well in various clinical scenarios including open-ended multi-disease diagnosis, clinical explanation, and patient interaction, making it a highly versatile tool for initial ophthalmic disease screening. VisionUnite can also serve as an educational aid for junior ophthalmologists, accelerating their acquisition of knowledge regarding both common and underrepresented ophthalmic conditions. VisionUnite represents a significant advancement in ophthalmology, with broad implications for diagnostics, medical education, and understanding of disease mechanisms.

This article introduces three-filters-to-normal$+$(3F2N$+$), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs). 3F2N$+$achieves this capability by utilizing a novel discontinuity discrimination module (DDM), which combines depth curvature minimization and correlation coefficient maximization through conditional random fields (CRFs). To evaluate the robustness of SNEs on noisy data, we create a large-scale synthetic surface normal (SSN) dataset containing 20 scenarios (ten indoor scenarios and ten outdoor scenarios with and without random Gaussian noise added to depth images). Extensive experiments demonstrate that 3F2N$+$achieves greater performance than all other geometry-based surface normal estimators, with average angular errors of 7.85$^\circ$, 8.95$^\circ$, 9.25$^\circ$, and 11.98$^\circ$on the clean-indoor, clean-outdoor, noisy-indoor, and noisy-outdoor datasets, respectively. We conduct three additional experiments to demonstrate the effectiveness of incorporating our proposed 3F2N$+$into downstream robot perception tasks, including freespace detection, 6D object pose estimation, and point cloud completion. Our source code and datasets are publicly available at https://mias.group/3F2Nplus.Note to Practitioners—The primary motivation behind this work arises from the need to develop a high-performing surface normal estimator for practical robotics and computer vision applications. While geometry-based surface normal estimators have been widely used in these domains, the existing solutions focus merely on discontinuity discrimination. To tackle this problem, this article introduces a plug-and-play module that leverages both depth curvature and correlation coefficient to quantify discontinuity levels, thereby optimizing surface normal estimation, particularly near or on discontinuous regions. Moreover, this article also introduces a large-scale public dataset with random noise added to depth images, providing a more realistic and robust platform for algorithm evaluation within this research community. Extensive experimental results demonstrate that our method outperforms other state-of-the-art algorithms.

Routing is a critical path-planning challenge in integrated circuit (IC) design, especially in analog IC design, where the complexity of the routing problem increases significantly owing to complex constraints and process parameters. Traditional automated routing techniques often rely on manual experience, limiting the effectiveness of the algorithms. To address this limitation, this study proposes a multilevel fast detour and rerouting algorithm based on reinforcement learning that integrates multiple symmetry and geometric constraints. The algorithm can effectively improve the design efficiency and reduce human error while optimizing the circuit performance and power consumption. The results show that the framework significantly improves the routing speed, reduces the number of vias, and is essentially free of DRC reporting errors on a variety of circuits in SMIC’s 180 nm technology. In addition, the algorithm roughly agrees with the manual routing results in terms of simulation performance and maintains good performance.

Passive near field communication (NFC) tags rely on the carrier-provided clock for operation. They can receive 100% amplitude shift keying (ASK)-modulated information but are unable to respond using 100% ASK modulation. This limitation restricts the tag’s resistance to interference and its communication range. This article proposes a design approach that enables passive NFC tags to employ 100% ASK modulation (termed “strong modulation” in this article, while non-100% ASK modulation is referred to as “typical modulation”) for response. Addressing the critical issue where the tag’s clock is lost due to the antenna carrier being turned off during the low signal bits of the modulation signal, preventing the tag from continuing to function, this article introduces a high-precision recovery clock circuit as a solution. The recovery clock circuit consists of a digitally controlled oscillator (DCO) circuit composed of 12 sets of current mirrors and a logic circuit DCO calibrator. The design feasibility was validated through the postlayout parasitic extraction and the AMS mixed-signal simulation in Cadence Virtuoso, ensuring correct communication between the tag and the reader. By implementing the tag’s strong modulation response, the anti-interference capability of the tag’s returned signal can be significantly enhanced, effectively reducing the difficulty of demodulation at the receiving end and improving the tag’s poor long-distance communication capabilities. Comparatively, the minimum antenna coupling coefficientkrequired for response under strong modulation is only 40.91% of that needed for typical modulation, enabling the tag to operate in weaker electromagnetic fields and exhibit better long-distance communication capabilities.

As a safety-critical application, autonomous driving requires high-quality semantic segmentation and real-time performance for deployment. Existing method commonly suffers from information loss and massive computational burden due to high-resolution input-output and multiscale learning scheme, which runs counter to the real-time requirements. In contrast to channelwise information modeling commonly adopted by modern networks, in this article, we propose a novel real-time driving scene parsing framework named NDNet from a novel perspective of spacewise neighbor decoupling (ND) and neighbor coupling (NC). We first define and implement the reversible operations called ND and NC, which realize lossless resolution conversion for complementary thumbnails sampling and collation to facilitate spatial modeling. Based on ND and NC, we further propose three modules, namely, local capturer and global dependence builder (LCGB), spacewise multiscale feature extractor (SMFE), and high-resolution semantic generator (HSG), which form the whole pipeline of NDNet. The LCGB serves as a stem block to preprocess the large-scale input for fast but lossless resolution reduction and extract initial features with global context. Then the SMFE is used for dense feature extraction and can obtain rich multiscale features in spatial dimension with less computational overhead. As for high-resolution semantic output, the HSG is designed for fast resolution reconstruction and adaptive semantic confusion amending. Experiments show the superiority of the proposed method. NDNet achieves the state-of-the-art performance on the Cityscapes dataset which reports 76.47% mIoU at 240+ frames/s and 78.8% mIoU at 150+ frames/s on the benchmark. Codes are available at https://github.com/LiShuTJ/NDNet.

The development and application of various precision electronic devices require a large number of high-precision and low-cost temperature-compensated crystal oscillators (TCXO) to generate frequency references. In order to achieve higher compensation accuracy at lower cost, a new fully integrated analog-TCXO (ATCXO) is proposed in this paper. It uses an innovative temperature-compensated multiplier to generate cubic and quintic voltages. The experimental results show that it can achieve the ultra-high fitting of the frequency-temperature characteristic curve of the crystal. This effectively improves the compensation accuracy of the TCXO. The proposed ATCXO achieves a frequency stability of ± 0.8 ppm in the temperature range of$- 40\,\,^{\circ }\text{C}$to 95 °C. At the same time, the gain-temperature error of the proposed temperature compensated analog multiplier is$\le 0.13$dB. This paper is based on SMIC 130 nm process, the core area of the chip is only 0.124 mm2. Multiple modules such as bandgap circuit, n-th power voltage generation circuit, summation circuit, gain control circuit and voltage controlled oscillator using variable capacitor array are integrated.

The deployment and application of large-scale Internet of Things require a large number of 13.56-MHz radio-frequency identification (RFID) reader chips with lower cost. The existing RFID reader module needs to integrate multiple chips, such as microcontroller and high-frequency (HF) RFID reader IC on a single printed circuit board (PCB), which has the bottleneck of cost, power consumption, stability, and reliability. In order to adapt to the development trend of high integration, miniaturization, and low-power consumption of RFID reader, this article proposes a fully integrated low-cost RFID reader System-on-a-Chip (SoC), which integrates RF transceiver and analog circuit, baseband protocol processing, microcontroller, memory, and interface circuit into a single chip. The proposed reader IC supports communication protocols, such as ISO/IEC 14443 Type A and Type B, ISO/IEC 15693, and ISO/IEC 7816. The reader can communicate with contact and contactless IC cards. The modulation depth of the transmitter circuit ranges from 1% to 100%, and the maximum transmitting current is 126 mA. The receiver circuit is composed of I/Q clock generation, switched capacitor sampling circuit, variable gain amplifier (VGA) with filter, comparator, and pulse shaping circuit, which has good anti-noise performance. This 13.56-MHz RFID reader SoC is fabricated in a 180-nm CMOS process and is housed in a 64-pin QFP package. The total area is 8.75 mm2, including 1.47 mm2for analog and RF circuits.

6D object pose estimation is a longstanding computer vision problem. Existing deep learning-based methods have achieved inspiring results in this task. However, large-scale training data with annotations is extremely needed to guarantee these methods’ performance, and acquiring real 6D object pose annotations is fairly labor-intensive and time-consuming. To overcome this drawback, we propose a semi-supervised pose estimation method using labeled synthetic data and unlabeled real data. For unlabeled real data, we form a self-supervised pipeline by minimizing the distance between the input point cloud, which is under ground-truth pose, and the model points transformed based on predicted pose. The labeled synthetic data is used to supervise the network to converge correctly. And we utilize a feature mapping to eliminate the domain gap between the real and synthetic features to further enhance the network’s performance. Moreover, we propose an attention-based pose estimation network, which can concentrate more on the distinguishing features, thus improving the accuracy of pose estimation. Experiments show that our proposed semi-supervised method is able to achieve good performance without the real annotations and outperforms all other methods relying on synthetic data or self-supervision strategy, indicating that the proposed method is effective.

6D object pose estimation for texture-less objects from RGB images remains challenging, especially in occlusion scenarios. Instead of localizing sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion, we introduce GeoPose, a novel reconstruction guided pose estimation pipeline that predicts dense correspondences and leverages geometric consistency effectively. We first design a dense reconstruction network (ReconNet) to reconstruct pixel-wise object coordinates in normalization space. Dense 2D-3D correspondences are generated intuitively by our explicit parameterization for 3D object models, which dismisses keypoint selection efforts. These 2D-3D correspondences are then utilized to estimate 6D poses by the PnP algorithm with RANSAC iterations. Furthermore, a novel Cycle Loss is proposed to provide 3D prior supervision, which significantly correlates with the pose estimation task by guiding geometric consistency between reconstruction (pixel to 3D) and reprojection (3D to pixel). In addition, a training data augmentation method is proposed to handle the insufficiency of 6D datasets, the acquisition of which is error-prone and time-consuming. Extensive experiments demonstrate that, compared with existing RGB-based methods, our GeoPose can achieve state-of-the-art (SOTA) 6D pose estimation performance on the LINEMOD, Occlusion LINEMOD and T-LESS datasets.

This paper aims to solve the problem of estimating the 6D pose of an object under occlusion using RGB-D images. Most existing methods typically use the information of color and depth images separately to make predictions, which limits their performances in the presence of occlusion. Instead, we propose a pipeline to effectively fuse color and depth information and perform region-level pose estimation. Our method first uses a CNN to extract the color features, and then we obtain the fusion features by combining the color features into the point cloud. Unlike existing methods, the fusion features are in the form of point sets instead of feature maps. We further use a PointNet++-like network to process the fusion features, obtaining several region-level features. Each region-level feature can predict a pose with confidence. The pose with the highest confidence is chosen as the final output. Experiments show that the proposed method outperforms the state-of-the-art methods on both the LINEMOD and Occlusion LINEMOD datasets, indicating that the proposed pipeline can obtain accurate pose estimation results and is robust to occlusion.

With fast developments in video broadcasting and communication, high definition and high bit rate video applications with low latency are becoming more and more popular. This leads to the emergence of the intra-only encoding scheme as it can provide the required performance. Specific to these applications, a novel intra-only video coding algorithm is proposed in this paper. A node-cell macroblock pixel structure is defined and a 2-D interleaved DCT is proposed to encode the node pixels. The proposed algorithm introduces several new intra coding modes based on the proposed node-cell macroblock pixel structure. Combined with the existing MPEG-4 AVC intra coding modes, the proposed algorithm implements the mode decision through a rate distortion optimization process. To evaluate the performance, the proposed coding algorithm is integrated into the reference MPEG4-AVC codec. Experimental results confirm that the new coding algorithm can provide significant rate distortion performance gain over the reference MPEG-4 AVC codec.

This paper analyzes the optimal control of the wireless remote control pruning machine based on complex programmable logic device (CPLD). The power control circuit and the safety control circuit are all designed and analyzed. The appropriate choice of electronic devices and the reasonable control law are applied for different circuit. Firstly, by using the new idea of “power switch” in the transmitting system, the lifespan of the chargeable battery is extended strongly. Secondly, the control accuracy is improved by self-retain and cutoff delaying power supply in the receiving system. And last the ignition of the engine is controlled in emergency case. All these optimal control system are simple, reliable and effective. The integrated system has been extensively field tested and the results are very promising.

A wireless remote control pruning machine was developed for plantation forest. The new designed system mainly consists of two parts: a transmitting system in the form of a hand controller and a receiving system mounted on the frame of the machine to drive the mechanical system. The idea of using the complex programmable logic device (CPLD) to realize the digital signal processing for the control system was discussed in details. The structure of the electromechanical interface, the transmitting system and the receiving system were all presented in this paper. The point of technology for each part was analyzed. The whole remote control system has the advantages of simplicity, reliability of performance and function of anti-interference. It has been successfully used in the manufactured automatic pruning machine and the expected result is obtained.

As the ideal sliding motion can't be formed in the practical system using a sliding mode control, the sliding mode controller for switched reluctance motor has the problem of large speed ripples. In this paper, a biologically inspired shunting model is incorporated into the variable structure sliding mode control for a switched reluctance motor in order to improve its performance. The simulation by MATLAB demonstrates the effectiveness of the proposed control scheme. The simulation results show that the switched reluctance motor using the proposed biologically inspired sliding mode control has the better speed tracking performance than using the conventional sliding mode control. In addition, the switched reluctance motor controller keeps the features of parameter insensitivity and rapid recovery from load disturbances.