Fast Modulation Research Articles

Weed Detection Method Based on Lightweight and Contextual Information Fusion

Weed detection technology is of paramount significance in achieving automation and intelligence in weed control. Nevertheless, it grapples with several formidable challenges, including imprecise small target detection, high computational demands, inadequate real-time performance, and susceptibility to environmental background interference. In response to these practical issues, we introduce CCCS-YOLO, a lightweight weed detection algorithm, built upon enhancements to the Yolov5s framework. In this study, the Faster_Block is integrated into the C3 module of the YOLOv5s neck network, creating the C3_Faster module. This modification not only streamlines the network but also significantly amplifies its detection capabilities. Subsequently, the context aggregation module is enhanced in the head by improving the convolution blocks, strengthening the network’s ability to distinguish between background and targets. Furthermore, the lightweight Content-Aware ReAssembly of Feature (CARAFE) module is employed to replace the upsampling module in the neck network, enhancing the performance of small target detection and promoting the fusion of contextual information. Finally, Soft-NMS-EIoU is utilized to replace the NMS and CIoU modules in YOLOv5s, enhancing the accuracy of target detection under dense conditions. Through detection on a publicly available sugar beet weed dataset and sesame weed datasets, the improved algorithm exhibits significant improvement in detection performance compared to YOLOv5s and demonstrates certain advancements over classical networks such as YOLOv7 and YOLOv8.

Push-Pull Heptamethines Near the Cyanine Limit Exhibiting Large Quadratic Electro-Optic Effect.

Harnessing the quadratic electro-optic (QEO) of near-infrared polymethine chromophores over broad telecom wavelength bands is a subject of immense potential but remains largely under-investigated. Hereina series of push-pull heptamethines containing the tricyanofuran (TCF) acceptors and indoline or benzo[e]indoline donors are reported. These dipolar chromophores can attain a highly delocalized "cyanine-like" electronic ground state in solvents spanning a wide range of polarities, in some cases even closer to the ideal polymethine state than symmetrical cyanines.A transmission-mode electromodulation spectroscopy is used to study the electric-field-induced changes in optical absorption and refraction of polymer films doped with heptamethine chromophores, and large and thermally stable QEO effect with high efficiency-loss figure-of-merits that compare favorably to those from dipolar polyenes in poled or unpoled polymers and III-V semiconductors is obtained.The study opens a path for developing organic materials based on cyanine-like merocyanines for complementary metal oxide semiconductor -compatible, fast, efficient, and low-loss electro-optic modulation.

ERF-RTMDet: An Improved Small Object Detection Method in Remote Sensing Images

A significant challenge in detecting objects in complex remote sensing (RS) datasets is from small objects. Existing detection methods achieve much lower accuracy on small objects than medium and large ones. These methods suffer from limited feature information, susceptibility to complex background interferences, and insufficient contextual information. To address these issues, a small object detection method with the enhanced receptive field, ERF-RTMDet, is proposed to achieve a more robust detection capability on small objects in RS images. Specifically, three modules are employed to enhance the receptive field of small objects’ features. First, the Dilated Spatial Pyramid Pooling Fast Module is proposed to gather more contextual information on small objects and suppress the interference of background information. Second, the Content-Aware Reassembly of Features Module is employed for more efficient feature fusion instead of the nearest-neighbor upsampling operator. Finally, the Hybrid Dilated Attention Module is proposed to expand the receptive field of object features after the feature fusion network. Extensive experiments are conducted on the MAR20 and NWPU VHR-10 datasets. The experimental results show that our ERF-RTMDet attains higher detection precision on small objects while maintaining or slightly enhancing the detection precision on mid-scale and large-scale objects.

Three-dimensional image reconstruction of opaque particle swarms by lightweight dual-residual deep learning computed tomography

High quality of three-dimensional particle swarm images is always highly desired in some important areas like experimental fluid mechanics and combustion field measurement, etc. as particle distributions in these fields can characterize the field properties well. Recently, deep learning techniques have been applied for the computed tomography to improve three-dimensional image reconstruction quality of an object. However, how to promote the network processing efficiency and quality to reduce artifact noise, missing-particles and ghost-particles in the reconstruction image is still a challenge at present. For this purpose, we propose a lightweight dual-residual deep learning approach for improvement of tomographic image reconstruction of particle swarms in this paper. It includes the innovative designs in lightweight dual-residual down-sampling, fast feature convergence module and high-SNR network input datasets to acquire image features more effectively. Numerical analysis and experimental test have demonstrated that the proposed method can effectively reduce not only the missing particles and ghost-particles in the reconstructed images under sparse-sampling, but also the network training time, in contrast to the performances of some typical deep learning networks under the same conditions.

Optically Addressing Exciton Spin and Pseudospin in Nanomaterials for Spintronics Applications.

Oriented exciton spins that can be generated and manipulated optically are of interest for a range of applications, including spintronics, quantum information science, and neuromorphic computing architectures. Although materials that host such excitons often lack practical coherence times for use on their own, strategic transduction of the magnetic information across interfaces can combine fast modulation with longer-term storage and readout. Several nanostructure systems have been put forward due to their interesting magneto-optical properties and their possible manipulation using circularly polarized light. These material systems are presented here, namely two-dimensional (2D) systems due to the unique spin-valley coupling properties and quantum dots for their exciton fine structure. 2D magnets are also discussed for their anisotropic spin behavior and extensive 2D magnetic states that are not yet fully understood but could pave the way for emergent techniques of magnetic control. This review also details the experimental and theoretical tools to measure and understand these systems along with a discussion on the progress of optical manipulation of spins and magnetic order transitions.

Defect detection of MicroLED with low distinction based on deep learning

MicroLED has great prospects in visible light communication, optical detection and other fields. To solve limited efficiency and reliability issues caused by current manual quality control processes in MicroLED production environments, we propose a micro-vision-based automatic scanning system (MVASS) used to capture the MicroLED defect images and performing defect prediction inference. Since MicroLED defects are weak, they are very similar to normal MicroLEDs with low distinction, and the large number of MicroLEDs in an image, making a poor detection effect for the existing method. In this paper, a real-time detection method named AMS-YOLO base on lightweight deep convolutional neural network for MicroLED defects with low-distinction is proposed, utilizing the attention residual module (ARM) to extract multi-level effective features by focusing on the key features in the defect images by self-attention, and introducing the multi-scale feature fast fusion module (M3FM) to fuses the multi-level feature effectively and quickly to better capture the global information of the defect image, and using the selective feature pyramid module (SFPM) to selectively fuse the extracted features to enhance the network's classification and positioning capabilities and ultimately achieve precise MicroLED classification and detection. Experimental results on the MicroLED dataset show that AMS-YOLO demonstrates a better performance, with the mAP, Precision, Recall and speed increasing by 3.0 %, 3.5 %, 6.0 % and 4.3 % respectively, compared to the YOLOv5s.

Optoelectronic tuning of plasmon resonances via optically modulated hot electrons.

Fast optical modulation of nanoplasmonics is fundamental for on-chip integration of all-optical devices. Although various strategies have been proposed for dynamic modulation of surface plasmons, critical issues of device compatibility and extremely low efficiency in the visible spectrum hamper the application of optoplasmonic nanochips. Here we establish an optoplasmonic system based on Au@Cu2-xS hybrid core-shell nanoparticles. The optical excitation of hot electrons and their charge transfer to the semiconductor coating (Cu2-xS) lead to lowered electron density of Au, which results in the red shift of the localized surface plasmon resonance. The hot electrons can also transport through the Cu2-xS layer to the metal substrate, which increases the conductance of the nanogap. As such, the coupled gap plasmon blue-shifts with a magnitude of up to ∼15nm, depending on the excitation power and the thickness of the coatings, which agrees with numerical simulations. All of this optoelectronic tuning process is highly reversible, controllable and fast with a modulated laser beam, which is highly compatible and sufficiently useful for on-chip integration of nanophotonic devices.

Improving phytoplankton abundance estimation accuracy for autonomous microscopic imaging systems

A novel method to accurately estimate phytoplankton abundance is proposed for an autonomous microscopic imaging system (AMIS) herein. To this end, a fast fluorescence detection module is developed and added to an imaging in-flow cytometer to record the fluorescence and side-scattered signals of individual phytoplankton particles, including of those that cannot be photographed by the AMIS. Image information and the coupling relationship between the fluorescence and side-scattered signals are used to accurately detect and estimate the phytoplankton counts in water samples. The performance of the proposed estimation method is evaluated on water samples containing Alexandrium tamarense, Chattonella marina, and Scrippsiella trochoidea. The abundance estimation accuracies for these species are found to be better than 95%, 97%, and 93%, respectively, when compared to results obtained using counting chambers. The performance of the method is further evaluated by mixing the collected data of the three phytoplankton species and classifying them based on fluorescence and side-scattered signals only, assuming that these species are included in the image data but not photographed individually. The overall estimation accuracy based on this complex matrix of the three species is found to be 95.3%. These results demonstrate the suitability and practicality of the proposed method for accurately evaluating phytoplankton abundance in water. The algorithm used in this study can be a reference for other imaging in-flow cytometers.

Fast 2-photon stimulation using holographic patterns.

Two decades after its introduction, optogenetics - a biological technique to control the activity of neurons or other cell types with light - remains a cutting edge and promising tool to study biological processes. Its increasing usage in research varies widely from causally exploring biological mechanisms and neural computations, to neurostimulation and sensory restauration. To stimulate neurons in the brain, a variety of approaches have been developed to generate precise spatiotemporal light patterns. Yet certain constrains still exists in the current optical techniques to activate a neuronal population with both cellular resolution and millisecond precision. Here, we describe an experimental setup allowing to stimulate a few tens of neurons in a plane at sub-millisecond rates using 2-photon activation. A liquid crystal on silicon spatial light modulator (LCoS-SLM) was used to generate spatial patterns in 2 dimensions. The image of the patterns was formed on the plane of a digital micromirror device (DMD) that was used as a fast temporal modulator of each region of interest. Using fluorescent microscopy and patch-clamp recording of neurons in culture expressing the light-gated ion channels, we characterized the temporal and spatial resolution of the microscope. We described the advantages of combining the LCoS-SLM with the DMD to maximize the temporal precision, modulate the illumination amplitude, and reduce background activation. Finally, we showed that this approach can be extended to patterns in 3 dimensions. We concluded that the methodology is well suited to address important questions about the role of temporal information in neuronal coding.

10 Hz Stable Mode-locked Er-Fiber Laser

Abstract: In this paper, a stable output at the reduction repetition rate from 80 MHz mode-locked Er-doped fiber laser (EDFL) was demonstrated by adjusting the operating point for external LiNbO3 Mach-Zehnder‎ electro-optic intensity modulator to suppress harmonic mode-locked pulses. More than 70% of the laser beam was launched into the external pigtail Mach-Zehnder intensity modulator using single mode fiber with collimating lens and fiber (ferrule connector) FC adapter. The optimum values of Mach-Zehnder modulator bias voltages V0 and Vπ (half wave voltage) which resulted in maximum and minimum laser output power to leave the modulator were found at 6.5 and 0.29 V, respectively. The best modulation was achieved for the RF modulating signal with an opening time of about 8 ns. For driving voltage equal to Vπ, a clean mode-locked pulse train was generated with a pulse shape and energy similar to the input pulse, albeit with a low repetition rate. The minimum repetition rate reached as low as 10 Hz, resulting in pulse energy and peak power of 0.32 nJ and 1.5 kW, respectively. Second-harmonic generation of the low repetition rate pulse train was also produced by using a PPLN crystal. This opens up the potential for using this laser in single photon detection experiments and as a seed laser for many applications. Keywords: Er-fiber laser, Passively mode-locked fiber laser, Nonlinear polarization rotation, Mach-Zehnder modulator, Fast electro-optic modulator. PACS: Fiber lasers, 42.55.Wd, Mode locking, 42.60.Fc.

LWR-Net: Robust and Lightweight Place Recognition Network for Noisy and Low-Density Point Clouds.

Point cloud-based retrieval for place recognition is essential in robotic applications like autonomous driving or simultaneous localization and mapping. However, this remains challenging in complex real-world scenes. Existing methods are sensitive to noisy, low-density point clouds and require extensive storage and computation, posing limitations for hardware-limited scenarios. To overcome these challenges, we propose LWR-Net, a lightweight place recognition network for efficient and robust point cloud retrieval in noisy, low-density conditions. Our approach incorporates a fast dilated sampling and grouping module with a residual MLP structure to learn geometric features from local neighborhoods. We also introduce a lightweight attentional weighting module to enhance global feature representation. By utilizing the Generalized Mean pooling structure, we aggregated the global descriptor for point cloud retrieval. We validated LWR-Net's efficiency and robustness on the Oxford robotcar dataset and three in-house datasets. The results demonstrate that our method efficiently and accurately retrieves matching scenes while being more robust to variations in point density and noise intensity. LWR-Net achieves state-of-the-art accuracy and robustness with a lightweight model size of 0.4M parameters. These efficiency, robustness, and lightweight advantages make our network highly suitable for robotic applications relying on point cloud-based place recognition.

Fast generation of Schrödinger cat states using a Kerr-tunable superconducting resonator

Schrödinger cat states, quantum superpositions of macroscopically distinct classical states, are an important resource for quantum communication, quantum metrology and quantum computation. Especially, cat states in a phase space protected against phase-flip errors can be used as a logical qubit. However, cat states, normally generated in three-dimensional cavities and/or strong multi-photon drives, are facing the challenges of scalability and controllability. Here, we present a strategy to generate and preserve cat states in a coplanar superconducting circuit by the fast modulation of Kerr nonlinearity. At the Kerr-free work point, our cat states are passively preserved due to the vanishing Kerr effect. We are able to prepare a 2-component cat state in our chip-based device with a fidelity reaching 89.1% under a 96 ns gate time. Our scheme shows an excellent route to constructing a chip-based bosonic quantum processor.

P255 Effects of EDG-5506, a fast myosin modulator, on function and biomarkers of muscle damage in adults with Becker muscular dystrophy (BMD)

P255 Effects of EDG-5506, a fast myosin modulator, on function and biomarkers of muscle damage in adults with Becker muscular dystrophy (BMD)

Millimeter field-of-view miniature two-photon microscopy for brain imaging in freely moving mice.

Development of miniature two-photon microscopy (m2PM) has made it possible to observe fine structure and activity of neurons in the brain of freely moving animals. However, the imaging field-of-view of existing m2PM is still significantly smaller than that of miniature single-photon microscopy. Here we report that, through the design of low-magnification objective, large field-of-view scan lens and small tilt angle microscanner, a 2.5-g m2PM achieved a field-of-view of 1000 × 788 µm2, comparable to that of a typical single-photon miniscope. We demonstrated its capability by imaging neurons, dendrites and spines in the millimeter field-of-view, and simultaneous recording calcium activities, through a gradient-index lens, of approximately 400 neurons in the dorsal hippocampal CA1 in a freely moving mouse. Integrated with a detachable 1.2-g fast z-scanning module, it enables a 1000 × 788 × 500 µm3 volumetric neuronal imaging in the cerebral cortex. Thus, millimeter FOV m2PM provides a powerful tool for deciphering neuronal population dynamics in experimental paradigms allowing for animal's free movement.

Real-Time Video Super-Resolution with Spatio-Temporal Modeling and Redundancy-Aware Inference.

Video super-resolution aims to generate high-resolution frames from low-resolution counterparts. It can be regarded as a specialized application of image super-resolution, serving various purposes, such as video display and surveillance. This paper proposes a novel method for real-time video super-resolution. It effectively exploits spatial information by utilizing the capabilities of an image super-resolution model and leverages the temporal information inherent in videos. Specifically, the method incorporates a pre-trained image super-resolution network as its foundational framework, allowing it to leverage existing expertise for super-resolution. A fast temporal information aggregation module is presented to further aggregate temporal cues across frames. By using deformable convolution to align features of neighboring frames, this module takes advantage of inter-frame dependency. In addition, it employs a hierarchical fast spatial offset feature extraction and a channel attention-based temporal fusion. A redundancy-aware inference algorithm is developed to reduce computational redundancy by reusing intermediate features, achieving real-time inferring speed. Extensive experiments on several benchmarks demonstrate that the proposed method can reconstruct satisfactory results with strong quantitative performance and visual qualities. The real-time inferring ability makes it suitable for real-world deployment.

Experimental realization of free-space continuous-variable quantum key distribution based on fiber Sagnac interferometer.

The Gaussian-modulated coherent state (GMCS) is a well-known continuous-variable quantum key distribution (CV-QKD) protocol that is robust to incoherent background noise and can effectively suppress ambient light in free space. However, it is difficult to implement this protocol in free space using existing polarization coding schemes. In this Letter, we propose a polarization coding structure based on a self-compensating fiber Sagnac interferometer, which can reduce the required modulation voltage by two orders of magnitude and achieve fast and arbitrary polarization modulation, and experimentally demonstrate polarization coding-based GMCS CV-QKD for, it is believed, the first time. The proposed polarization modulation structure, which uses off-the-shelf fiber components, is compact, simple, and suitable for mobile terminals, such as flying lifts.

A new fast PWM modulation strategy for power electric converter based on finite set state machine theory

The finite set state machine theory belongs to the field of computer science. It is used to model and create multitasking systems. The control and modulation system for power electronics converters is a multitasking system as well. To increase the control speed without being constrained by the triangular wave module, the finite set state machine theory can be applied to converter modulation. In this paper, a modulation technique for finite state machines was proposed. This strategy fully uses the relationship between the affective state and converter switch states. Then, the structure of the modulation modular is presented in detail. This paper has analyzed the mathematical model of the finite set state machine modular. Finally, finite set state machine theory is used to Boost converter modulation modular to control the output voltage stability. Additionally, the effectiveness of the modulation strategy is validated by the experimental results.

Real three-dimensional medical interactive display system based on integral imaging and gesture recognition

In this paper, we developed a real three-dimensional medical interactive display system based on integral imaging and gesture recognition. The system includes a fast image processing module, a high-performance three-dimensional display module, and a gesture recognition module. The three-dimensional model is obtained through gray-scale interval overlay processing. The non-matching element image method and 4 K screen are used for three-dimensional integral imaging display with a large viewing range and high resolution. A pair of binocular cameras is used to capture and recognize gestures, and the maximum interaction frame rate is 22 FPS. Optical experiments were performed to validate the proposed medical interactive three-dimensional display system with existing patient skeletal data. The results showed that the proposed system can effectively solve the problem that surgeons cannot take off their gloves to operate in surgery and provide an intuitive and comfortable three-dimensional display. Our proposed system is expected to be widely used in scenarios such as preoperative simulation, surgical instruction, and medical teaching.

Extreme Ultra‐Wideband Optoelectronic Frequency‐Modulated Continuous‐Wave Terahertz Radar

AbstractA novel photonic terahertz measurement system based on a frequency‐modulated continuous‐wave (FMCW) radar approach is presented. In previous works, fast frequency modulation has been demonstrated in connection with a continuous wave terahertz spectroscopy setup based on the photomixing principle. In this paper, a terahertz radar based on both a photomixing transmitter and a photomixing receiver, in contrast to the rigid spectroscopy approach, is reported. Hereby, frequency modulation bandwidths of more than 1.65 THz in radar operation is achieved. This corresponds to an order of magnitude more than what is previously achieved by terahertz radar systems. At the same time, measurement rates can be achieved that are comparable with radar systems based on Monolithic Microwave Integrated Circuits (MMICs) according to the current state of the art. Within the scope of the work, two operating modes are realized, one with a measurement rate of about 560 Hz at 600 GHz modulation bandwidth and one with 200 Hz at 1.65 THz modulation bandwidth, which can be set within the spectrum from 50 GHz to about 4.5 THz. The possibility to adjust the operating range of the radar without necessary hardware adaptations is another unique feature of the presented system, which allows the operator to choose a suitable frequency band that corresponds best to a certain measurement scheme via software settings. Besides the potential for multi‐layer thickness inspections, the capabilities of this technique for terahertz imaging applications are presented.

Modelling the Influence of the Tube Support Plate on the Eddy Current Testing of the Steam Generator: Numerical Tools and Experimental Validation

Eddy current testing (ECT) of Steam Generator (SG) tubes is part of the maintenance program of Nuclear Power Plants. Tube Support Plates (TSP) are usually only considered as extraneous signals for tube inspection. However, for long-term operation of SGs, there is a safety concern related to the build-up of deposits in the TSPs flow holes. The clogging-up of the TSPs flow holes affect the safe thermal-hydraulic operation of SGs. The deposits come from the feedwater train and are mostly iron oxides (magnetite) with a weak electrical conductivity and magnetic permeability. The ability of ECT probes to detect and characterize TSPs deposits is therefore of great interest. The most common and industrial ECT technique, the bobbin coil, averages the surrounding electromagnetic field over 360°, whereas the geometry of tri-foiled and quadri-foiled TSPs, and thus of clogging, is rather complex and non-axisymmetric, hence motivating the evaluation of the performance of conventional ECT rotating probes used for SGs tubes inspection. Although simulation is a powerful tool to support such a study, it requires dedicated models to be made available to NDT experts, and CEA develops ECT physical models in CIVA for this purpose. In addition to standard fast CIVA modules based on 3D semi-analytical or 2D numerical calculations restricted to canonical or axisymmetric parts, respectively, CEA develops a module dedicated to the simulation of SG tube inspection based on a 3D numerical simulation. This module allows tube deformation such as ovalization, bending or tube expansion. It also allows the addition of external objects such as anti-vibration bars, different geometries of tube support plates and now their clogging by deposits. This model is used to study various influent parameters and to perform benchmarks in the framework of a scientific collaboration between the CEA and the IRSN, related for example to the inspection of the U-bend tubes or of a friction wear under the anti-vibration bars. Here, the specificity of the TSPs and their clogging justifies further investigations between CEA and IRSN to qualify the simulation model before evaluating the influence of material and geometry properties of the deposit as well as the performance of the inspection techniques. We present the main characteristics of the simulation module in CIVA.