Breakthrough In Speed Research Articles

Ultrafast Silicon/Graphene Optical Nonlinear Activator for Neuromorphic Computing

AbstractOptical neural networks (ONNs) have shown great promise in overcoming the speed and efficiency bottlenecks of artificial neural networks. However, the absence of high‐speed, energy‐efficient nonlinear activators significantly impedes the advancement of ONNs and their extension to ultrafast application scenarios like real‐time intelligent signal processing. In this work, a novel silicon/graphene ultrafast all‐optical nonlinear activator, leveraging the hybrid integration of silicon slot waveguides, plasmonic slot waveguides, and monolayer graphene is demonstrated. Exploiting the exceptional picosecond‐scale photogenerated carrier relaxation time of graphene, the response time of the activator is markedly reduced to ≈93.6 ps, establishing all‐optical activator as the fastest known in silicon photonics to knowledge. Moreover, the all‐optical nonlinear activator holds a low threshold power of 5.49 mW and a corresponding power consumption per activation of 0.51 pJ. Its feasibility and capability for use in ONNs, manifesting performance comparable with commonly used activation functions are experimentally confirmed. This breakthrough in speed and energy efficiency of all‐optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs.

Study on Consumption Preference of New Energy Vehicles

The sales volume and sales volume of new energy vehicles in the market reflect the public's use of new energy vehicles. According to the feedback and demand of different people, the purchase proportion of new energy vehicles and new energy vehicles is different. The impact on economic development and the impact on the purchasing economy. The psychological impact of people using new energy vehicles. Consumers' pursuit of new energy vehicle performance (breakthroughs in speed and road use quality. Changes in conventional engine power output and motor output. The psychological impact on the use of new energy vehicles. Consumers' pursuit of new energy vehicle performance. After-sales quality assurance and service, the impact of different age groups on new energy vehicles. And the impact on the economic development of the population and the purchasing population, different age groups have different demands for new energy vehicles. After-sales service has also improved with market changes, and more and more people are pursuing the purchase of new energy vehicles. China's new energy vehicle policy will have an impact on the price of cars and buyers.

Face Mask Recognition based on YOLOV3

Wearing masks is considered one of the effective ways to prevent epidemics and protect physical health. In densely populated public places, detecting whether the mask is correctly worn has important application value, which can help managers reduce the potential risk of disease transmission. Previous works mainly rely on manual features, which are limited to fully expressing complex scene information. Thanks to the rapid development of artificial intelligence technology, automatic mask detection methods based on deep learning have achieved breakthroughs in speed and accuracy, while mask detection in complex scenes is still an open issue. To alleviate this issue, this paper proposes a mask detection method based on YOLOV3. Specifically, this study introduces regularization methods such as Drop Block and Label Smoothing, as well as data augmentation strategies such as Gridmask and Mosaic. This study further explores the recognition performance for refined scenarios including no mask, standard mask-wearing and incorrect mask-wearing. Extensive experimental results show the effectiveness of our method. By introducing the improved YOLOV5 algorithm to realize the mask detection function, the data structure is optimized through pruning, the data volume is reduced, the calculation speed is accelerated, and the mask detection is well realized.

A Field Full of Opportunities: Transforming the Future of Separation Science

A Field Full of Opportunities: Transforming the Future of Separation Science August 1, 2022 Jared L. Anderson LCGC North America, August 2022, Volume 40, Issue 8 Pages: 354–356 For four decades, LCGC has served to keep the field connected with new innovations and exciting product lines while concurrently educating new generations of scientists entering the field. Celebration of the 40th anniversary of LCGC allows us to reflect on future opportunities within the field of separation science. Innovations in separation science continue to be driven by important breakthroughs in speed, selectivity, and resolution. Meanwhile, the field of separation science is being advanced by tremendous discoveries in the fields of mass spectrometry (MS) and sample preparation. A recent consensus study report by the National Academies of Sciences offered recommendations to improve the field of separations. Collaborative opportunities with scientists in other fields, such as material science, spectroscopy, and chemical engineering, will serve to address important challenges and advance discoveries in three-dimensional (3D) printing, stationary phase development, and sustainable separation platforms. The recruitment of young, talented entrepreneurs within academia and industry is needed to tackle many of the challenges that exist today and that will emerge tomorrow.

Ultrasound Pulse Emission Spectroscopy Method to Characterize Xylem Conduits in Plant Stems.

Although it is well known that plants emit acoustic pulses under drought stress, the exact origin of the waveform of these ultrasound pulses has remained elusive. Here, we present evidence for a correlation between the characteristics of the waveform of these pulses and the dimensions of xylem conduits in plants. Using a model that relates the resonant vibrations of a vessel to its dimension and viscoelasticity, we extract the xylem radii from the waveforms of ultrasound pulses and show that these are correlated and in good agreement with optical microscopy. We demonstrate the versatility of the method by applying it to shoots of ten different vascular plant species. In particular, for Hydrangea quercifolia, we further extract vessel element lengths with our model and compare them with scanning electron cryomicroscopy. The ultrasonic, noninvasive characterization of internal conduit dimensions enables a breakthrough in speed and accuracy in plant phenotyping and stress detection.

Multilevel symmetric pattern design and optimization for high-speed and high-accuracy 3D shape measurement

The binary defocusing technique has enabled speed breakthroughs for 3D shape measurement, yet simultaneously achieving high accuracy and high speed remains difficult. To overcome this limitation, we propose to utilize multilevel symmetric pattern for high-speed and high-accuracy 3D shape measurement. Compared to conventional binary patterns, multilevel pattern could bring more flexibility for eliminating undesired high-frequency harmonics, thus has the potential to greatly enhance the phase quality and measurement accuracy. In this paper, the symmetric pattern design principle and related optimization procedure are presented in order to find the “best” multilevel fringe patterns. Both simulation and experiments verify that with respect to conventional methods, the proposed method could consistently generate better fringe patterns for a wide range of fringe periods. Furthermore, we developed an absolute 3D shape measurement system with the speed of 667 Hz, verifying that the proposed method is applicable for high-speed, high-accuracy applications.

Motion-induced error reduction for binary defocusing profilometry via additional temporal sampling.

The recently proposed binary defocusing technique has brought speed breakthroughs for three-dimensional (3D) shape measurement with a digital fringe projection system. Despite this, motion-induced phase error is still inevitable due to the multi-shot nature of the phase-shifting algorithm. To alleviate this problem, this paper proposes a motion-induced error reduction method by taking advantage of additional temporal sampling. Particularly, each illuminated fringe pattern will be captured twice in one projection cycle, resulting in two sets of phase shifted fringe images being obtained. Due to the mechanism of binary defocusing projection, the motion-induced phase error could be effectively separated from the fixed phase shift value by evaluating the difference between the two phase maps. Based on this, an iterative compensation strategy is further applied to compensate the phase error until high-quality phase maps are generated. Meanwhile, different synchronization schemes are also proposed and tested to evaluate the error compensation effects. Both simulation and experiments demonstrated that the proposed methods can substantially reduce motion-introduced measurement errors. Since defocused 1-bit binary patterns are utilized to bypass rigid camera-projector synchronization, this method has potential for high-speed applications.

A Novel Multi-Scale Adaptive Convolutional Network for Single Image Super-Resolution

Recently, the methods based on deep convolutional neural networks have shown superior performance on image restoration. By using a faster and deeper network, the existing methods have achieved the breakthroughs in speed and accuracy. But, they still work hard to recover sharper edge and finer texture details for the large upscaling factor. In this paper, we propose a novel multi-scale adaptive convolutional network to solve this problem. The proposed network is mainly built on the multi-scale adaptive convolutional block which is composed of three different scales sub-blocks. Each sub-block consists of two convolutional layers, one parametric rectified linear unit layer, and some adaptive shortcuts. Different scaling parameters are assigned to these shortcuts to extract and connect diversified and complicated features adaptively. Due to the special design of MACB, the proposed algorithm can enhance the combination of various levels features and provide different ranges of image context for super-resolution (SR) reconstruction. Besides the stack of MACB, the skip connections with identity mapping are used to further aggregate the features of two specific layers in the proposed network. Moreover, in order to infer photo-realistic natural images, a perceptual loss function is proposed to supervise the reconstruction, which consists of four parts of loss: feature loss, style loss, total variation loss, and pixel loss. These losses push our reconstructed image to the target image. The experimental results on the public benchmark datasets demonstrate that the proposed algorithm achieves the best super-resolution reconstruction among the state-of-the-art SR methods.

High dynamic range 3D measurement based on spectral modulation and hyperspectral imaging.

High-speed and high-accuracy three-dimensional (3D) measurement is of great importance in numerous areas. Recently proposed binary defocusing techniques have enabled breakthroughs in speed by utilizing 1-bit binary fringe patterns with the advanced digital light processing (DLP) projection platform. Meanwhile, much research has also been conducted to enhance measurement accuracy by temporally or spatially modulating the binary patterns. However, it is still challenging to use such techniques for measuring objects with high dynamic range (HDR) of surface reflectivity such as metal parts. To this end, we propose a novel HDR 3D measurement method based on spectral modulation and hyperspectral imaging. By modulating the illumination light with a spectral filter, and acquiring the fringe patterns with a hyperspectral camera, high-contrast HDR fringe imaging and 3D measurement can finally be achieved. Experiments were carried out to demonstrate the effectiveness of the proposed strategy.

Spin-Noise-Detected Two-Dimensional Nuclear Magnetic Resonance at Triple Sensitivity.

A major breakthrough in speed and sensitivity of 2 D spin‐noise‐detected NMR is achieved owing to a new acquisition and processing scheme called “double block usage” (DBU) that utilizes each recorded noise block in two independent cross‐correlations. The mixing, evolution, and acquisition periods are repeated head‐to‐tail without any recovery delays and well‐known building blocks of multidimensional NMR (constant‐time evolution and quadrature detection in the indirect dimension as well as pulsed field gradients) provide further enhancement and artifact suppression. Modified timing of the receiver electronics eliminates spurious random excitation. We achieve a threefold sensitivity increase over the original snHMQC (spin‐noise‐detected heteronuclear multiple quantum correlation) experiment (K. Chandra et al., J. Phys. Chem. Lett. 2013, 4, 3853) and demonstrate the feasibility of spin‐noise‐detected long‐range correlation.

MethBLAST and methPrimerDB: web-tools for PCR based methylation analysis

BackgroundDNA methylation plays an important role in development and tumorigenesis by epigenetic modification and silencing of critical genes. The development of PCR-based methylation assays on bisulphite modified DNA heralded a breakthrough in speed and sensitivity for gene methylation analysis. Despite this technological advancement, these approaches require a cumbersome gene by gene primer design and experimental validation. Bisulphite DNA modification results in sequence alterations (all unmethylated cytosines are converted into uracils) and a general sequence complexity reduction as cytosines become underrepresented. Consequently, standard BLAST sequence homology searches cannot be applied to search for specific methylation primers.ResultsTo address this problem we developed methBLAST, a sequence similarity search program, based on the original BLAST algorithm but querying in silico bisulphite modified genome sequences to evaluate oligonucleotide sequence similarities. Apart from the primer specificity analysis tool, we have also developed a public database termed methPrimerDB for the storage and retrieval of validated PCR based methylation assays. The web interface allows free public access to perform methBLAST searches or database queries and to submit user based information. Database records can be searched by gene symbol, nucleotide sequence, analytical method used, Entrez Gene or methPrimerDB identifier, and submitter's name. Each record contains a link to Entrez Gene and PubMed to retrieve additional information on the gene, its genomic context and the article in which the methylation assay was described. To assure and maintain data integrity and accuracy, the database is linked to other reference databases. Currently, the database contains primer records for the most popular PCR-based methylation analysis methods to study human, mouse and rat epigenetic modifications. methPrimerDB and methBLAST are available at and .ConclusionWe have developed two integrated and freely available web-tools for PCR based methylation analysis. methBLAST allows in silico assessment of primer specificity in PCR based methylation assays that can be stored in the methPrimerDB database, which provides a search portal for validated methylation assays.