Format Conversion Research Articles

Research on real-time object detection based on Yolo algorithm

Carrying controlled knives and guns and ammunition in public places is a serious threat to public safety, and the target proportion of images in monitoring is small, and the recognition background is complex. The existing monitoring methods in public places have problems that can not be well recognized automatically. Therefore, the YOLOV5 model is proposed to be applied in Intelligent Security to improve public safety. Focusing on the target detection of contraband in public under the application scenario of Intelligent Security, the characteristics and requirements of the detection task are analyzed, and the viewpoint of applying YOLOV5 algorithm to real-time detection of contraband is put forward. The history and basic principles of YOLO series algorithms and target detection are summarized. YOLOV5 performance test was carried out systematically to verify the feasibility of the method. Then, the detection dataset is made, format conversion and labeling are carried out, and the training data is expanded, and the related detection experiments are carried out on the computer. The results show that the contraband detection method based on YOLOV5 has great potential in practical application.

Review on optical nonlinearity of group-IV semiconducting materials for all-optical processing

Group-IV semiconductor compounds with intense optical nonlinearity have emerged as a new branch of all-optical processing materials benefiting from the manufacturing compatibility with silicon electronic and photonic integrated circuits. Due to the chemical reforming on the bonding or precipitating feature of the compositional atoms in the membrane matrix, either the orbital hybridization or the quantum self-assembly of interstitial composites can be employed to reform the electronic and optical characteristics. The recent development on enhancing the nonlinear refractive indices of the group-IV semiconductor materials has revealed significant progress to accelerate the all-optical switching logic, which greatly reduces the energy consumption to enable the constitution of the advanced multi-logic gating and the entry-level photonic computing circuits. This work not only overviews the group-IV semiconductor photonic data processing elements but also prospects for the future direction of optical quantum computation and communication. To date, the nonlinear refractive indices of the group-IV semiconductor materials can be obtained as 10−8 to 10−16 cm2/W in the range between 300 and 10 000 nm in 2022. The wavelength conversion and data switching with bit rate beyond 25 Gbps have been achieved via nonlinear photonic waveguide components. By taking the non-stoichiometric SiC-made micro-ring waveguide as an example, the n2 as high as 3.05 × 10−14 cm2/W of the resonant SiC micro-ring gate is retrieved from the pump–probe analysis. The eye-diagram of the wavelength converted data in the micro-ring achieves its signal-to-noise and on/off-extinction ratios (SNR and ER) of 5.6 and 11.8 dB, while up to 25-Gbps all-optical data-format inversion with 4.8-dB SNR and 10.2-dB ER is also performed during an ultrafast switching within rising and falling time of less than 22 ps. Such all-optical data processing including both wavelength switching and format conversion in the highly nonlinear optical SiC waveguide resonator can achieve error-free operation with corresponding bit-error-ratios of lower than 1 × 10−5 at 25 Gbps after forward error correction.

Isotope Calculation Gadgets: A Series of Software for Isotope-Tracing Experiments in Garuda Platform.

Isotope tracing is a powerful technique for elucidating intracellular metabolism. Experiments utilizing this technique involve various processes, such as the correction of natural isotopes. Although some previously developed software are available for these procedures, there are still time-consuming steps in isotope tracing including the creation of an isotope measurement method in mass spectrometry (MS) and the interpretation of obtained labeling data. Additionally, these multi-step tasks often require data format conversion, which is also time-consuming. In this study, the Isotope Calculation Gadgets, a series of software that supports an entire workflow of isotope-tracing experiments, was developed in the Garuda platform, an open community. Garuda is a graphical user interface-based platform that allows individual operations to be sequentially performed, without data format conversion, which significantly reduces the required time and effort. The developed software includes new features that construct channels for isotopomer measurements, as well as conventional functions such as natural isotope correction, the calculation of fractional labeling and split ratio, and data mapping, thus facilitating an overall workflow of isotope-tracing experiments through smooth functional integration.

Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau

The development of landslide hazards is spatially scattered, temporally random, and poorly characterized. Given the advantages of the large spatial scale and high sensitivity of InSAR observations, InSAR is becoming one of the main techniques for active landslide identification. The difficult problem is how to quickly extract landslide information from extensive InSAR image data. Since the instance segmentation model (Mask R-CNN) in deep learning can provide highly robust target recognition, we select the landslide-prone eastern edge of the Tibetan Plateau as a specific test area. Introducing and optimizing this model achieves high-speed and accurate recognition of InSAR observations. First, the InSAR patch landslide instance segmentation dataset (SLD) is established by developing a common object in context (COCO) annotation format conversion code based on InSAR observations. The Mask R-CNN+++ is found by adding three functions of the ResNext module to increase the fineness of the network segmentation results and enhance the noise resistance of the model, the DCB (deformable convolutional block) to improve the feature extraction ability of the network for geometric morphological changes of landslide patches, and an attention mechanism to selectively enhance usefully and suppress features less valuable to the native Mask R-CNN network. The model achieves 92.94% accuracy on the test set, and the active landslide recognition speed based on this model under ordinary computer hardware conditions is 72.3 km2/s. The overall characteristics of the results of this study show that the optimized model effectively enhances the perceptibility of image morphological changes, thereby resulting in smoother recognition boundaries and further improvement of the generalization ability of segmentation detection. This result is expected to serve to identify and monitor active landslides in complex surface conditions on a large spatial scale. Moreover, active landslides of different geometric features, motion patterns, and intensities are expected to be further segmented.

Measurement method of maize morphological parameters based on point cloud image conversion

The plant height (PH), stem thickness long axis (STLA), and stem thickness short axis (STSA) in maize morphological parameters can effectively reflect the growth, lodging resistance and yield information of maize plants. Terrestrial laser scanning (TLS) can achieve rapid measurement of crop phenotypic parameters. To address the problems of low automation and leaf interference in the existing measurement methods, TLS was used as the measurement sensor, and a morphological measurement method for PH, STLA and STSA of field maize based on point cloud image conversion was proposed. First, in the V3, V6, V9, and V12 stages, three-dimensional (3D) point cloud data of two maize varieties (Jingnongke 728 and Nongda 84) were obtained by TLS. Second, the point cloud processing software was used to match the collected maize point cloud data, obtain the registered multi-site cloud data, remove the background point cloud data, extract the maize row data, and carry out down-sampling. Programming was used to realize data format conversion and individual plant segmentation. Third, several methods, including plane segmentation, statistical filtering, pass filtering, maximum and minimum traversal, and Euclidean clustering were used to remove ground point clouds, judge whether there was maize plant, and extract area maize point clouds. A method of point cloud image conversion was proposed to realize the segmentation of maize stem and leaf. Finally, the height of the plant was measured by calculating the vertical distance from the highest point of the plant to its base. The point cloud of the stem at a specific location was identified, and the ellipse fitting method was used to measure the thickness of the long axis and the short axis of the stem. Compared with the measured value of artificial point cloud, the PH, STLA and STSA of maize in 4 growth stages were measured using automatic program. The root mean square error (RMSE) of the PH, STLA and STSA of Jingnongke 728 were 0.61 cm, 3.16 mm and 2.53 mm respectively, and the mean absolute percentage error (MAPE) were 0.52%, 7.90% and 9.70% respectively. The RMSE of Nongda 84 were 0.66 cm, 2.63 mm and 2.42 mm respectively, and the MAPE were 0.75%, 7.07% and 9.76% respectively. The results show that the point cloud image conversion method for measuring PH, STLA and STSA proposed in this article is suitable for maize of different growth periods and different maize varieties. It is highly consistent with the artificial point cloud measurement values and can replace manual measurement. It can provide breeders with a fast, automatic and accurate measurement program for the PH, STLA and STSA of maize.

Interpretation of the magnetic anomalies over the Mid-Atlantic Ocean Ridge using Swarm-A satellite

The procedures and interpretation of the magnetic data measured over the Mid-Atlantic Ocean Ridge by Swarm-A satellite is discussed in this paper. The data processing procedure has four phases after the data format conversion. The first phase is the selecting data whose Kp index is lower than 1. Secondly, the main magnetic field should be subtracted from the measured data for example through the IGRF model. This step is followed by removing trend, related to the satellite trajectory. The final step is eliminating the dipole nature of the magnetic field via Reduction to the Pole (RTP). Nonetheless, the RTP process might cause misinterpretation over the large study area and lower latitudes. Therefore, the amplitude of the analytic signal (AS) is used as an alternative to the RTP. The qualitative interpretation of the magnetic anomaly is conducted via AS anomaly, bathymetry, heat flow and tectonic map. Quantitatively correlation coefficients of bathymetry and heat flow with respect to AS are interpreted.

Lightweight display of railway four-electricity BIM model based on glTF format and WebGL technology

Aiming at the problems of large volume of railway four-electricity BIM model, high computer performance requirements and low data interaction efficiency, a research method of lightweight display of railway four-electricity model based on glTF format and WebGL technology was proposed. This method realizes the lightweight display of the railway four-electricity BIM model on the browser side through the format conversion of the model, the development of the Three.js rendering engine, and the design of the user interaction function. The research results show that this method reduces the size of the model while completing the model format conversion, and realizes the lightweight display and user interaction functions of the model on the browser side. This is conducive to the further development of the railway four-electricity information visualization platform and promotes the utilization of the railway four-electricity BIM model.

A Multifunctional Video Switching Matrix Design

The use of Video Switching Matrix can greatly improve the utilization rate of internal space such as ships and submarines. This paper designs a multifunctional video switching matrix, which can realize 16-channel HDMI and VGA video signal input and 8-channel HDMI and VGA video signal output function. It can meet the complex scenes of HDMI and VGA integrated video input and output. This design realizes the conversion of input and output video formats through the front-end HDMI to VGA unit and the back-end VGA to HDMI unit. The middle RGB switching matrix is used to realize 16 input and 8 output selection of VGA video signal. It realizes the fusion of multi-channel dual-standard video switching. The module has the prospect of being applied to high reliability requirements in aerospace, navigation, and military industries.

BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data.

Bioinformatic analysis-such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, file format conversion, and processing and analysis-is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses, but unified toolkits that conduct all these analyses are lacking. To address this gap, we introduce BioKIT, a versatile command line toolkit that has, upon publication, 42 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we conducted a comprehensive examination of relative synonymous codon usage across 171 fungal genomes that use alternative genetic codes, showed that the novel metric of gene-wise relative synonymous codon usage can accurately estimate gene-wise codon optimization, evaluated the quality and characteristics of 901 eukaryotic genome assemblies, and calculated alignment summary statistics for 10 phylogenomic data matrices. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwyk-biokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).

Flexible and Efficient QoS Provisioning in AXI4-Based Network-on-Chip Architecture

We propose a network-on-chip (NoC)-based whole system design, whose communication architecture is compatible with the advanced microcontroller bus architecture advanced extensible interface 4 (AXI4) protocol and supports high-performance multiple quality-of-service (QoS) schemes. In our system, the network interface (NI) between the NoC and the master/slave node is proposed to make the NoC architecture independent from the AXI4 protocol via message format conversion between the AXI4 signal format and the packet format, offering high flexibility to the NoC design. Besides, a QoS inheritance mechanism is applied in the slave-side NI to support QoS during packets’ round-trip transfer in the NoC. The NoC system contains time-division multiplexing (TDM) and virtual channel (VC) subnetworks to apply multiple QoS schemes to AXI4 signals with different QoS tags, and the NI is responsible for signals distribution between two subnetworks. Besides, a traffic converter is proposed in each NI to balance the traffic between the two subnetworks when necessary. The experimental results show that our proposed architecture ensures a high-throughput and low-latency NoC system. By applying the traffic converter, the packet latency can be improved.

Efficient FPGA architecture to implement non-separable fast Fourier transform for image and video applications

ABSTRACT Fast Fourier Transform (FFT) is widely used in image and video processing applications to convert the respective image or video frames into transform domain that is very helpful to extract the accurate features of that image or video frame for various real-time applications. In this paper, efficient non-separable 8-point FFT architecture (DIT-FFT) is proposed that is implemented on Spartan-6 (xc6slx45-3csg324) FPGA. The proposed architecture consists of Data Format Conversion, Addition, Subtraction, Multiplier Equivalent and D-FF blocks, respectively. The non-separable equations of 8-point DIT-FFT are derived from the respective Butterfly Diagram that is then implemented using basic logic gates, which optimises the hardware utilisations with the help of Complex Conjugate property. The constant multiplications present in the non-separable DIT-FFT equations are implemented through Adders and Shifters presents in Multiplier Equivalent block which further optimises the overall hardware utilisations. Moreover, the Q-format are used to increase the data accuracy of the architecture. The comparison results show that the proposed architecture is better than existing in different prospectives.

File Convert with Easy and Efficient Operation

Converting files formats is become very important in today’s technical world, to use that file on particular platforms. Although, there exist a large number of software available today however most of them are failing to achieve accuracy and consistency while the platform which have the accuracy but they charge their users to use their platform. Researches, those involved heavily in format converter, needed a accurate, consistent and efficient software for these conversions. Therefore, here we propose a File ConvertEEO system, which contain a lot of formats in a single platform in which you want to convert your file. It also contains a feature of applying filters on doc file and then it can be converted into pdf format.

Artificial Intelligence in the Auxiliary Guidance Function of Athletes’ Movement Standard Training in Physical Education

Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. This paper is based on the research of AI in the auxiliary guidance function of athletes’ standard training in physical education. It aims to conduct data mining from different aspects such as different joint angular speeds, motion injury screening and different parts of sports injuries and then integrate these aspects. Create a way to reduce athletes’ injuries and scientific training. In order to improve the recognition efficiency of athletes’ movement patterns, nonlinear auto regressive neural networks are used to recognize the movement patterns of athletes’ limb surface signals. Through this research work, it can provide a certain reference basis and practice platform for the research on the auxiliary guidance role of AI in the sports standard training of athletes in physical education. Performance design and implementation include four modules: Image acquisition, preprocessing, motion detection and human motion recognition. Between them, the image acquisition module uses a memory mapping path to determine the format of the camera frame image, and the image format conversion is completed through channel conversion. Experimental data shows that athletes’ strength quality, speed of action response, technical continuity, psychological stability and physical control ability have all been greatly improved. Among them, the most obvious is that with the assistance of AI technology, the psychological stability has reached 9.2; the strength quality has reached 9.1.

Fast and flexible processing of large FRET image stacks using the FRET-IBRA toolkit.

Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.

Design of Data Acquisition Software for 128-channel μSR Spectrometer Prototype

The muon is one of the 12 elementary particles in the standard model, it is a typical particle probes that can extract features such as microscopic magnetic or electronic environments in materials. The technique of injecting spin-polarized muons into a material as a probe and then detecting their spin interaction can be called the μSR technique. A high-intensity muon source experimental device is a prerequisite for the research of μSR technology, with the complettion of China Spallation Neutron Scoure, μSR research has become possible. The data acquisition software introduced in this paper is an experimental 128-channel μSR spectrometer prototype applied to the China Spallation Neutron Source, it is written in C++ based on Qt software, and realizes data acquisition and electronic system control in the form of underlying packet capture by using Winpcap. DAQ software includes data acquisition, command issuing, data format conversion and rough cumulative time spectrum display functions, Each feature will be shown accordingly in the experiment.

LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow

Abstract The ever-increasing networks and quantity of seismic data drive the need for seamless and automatic workflows for rapid and accurate earthquake detection and location. In recent years, machine learning (ML)-based pickers have achieved remarkable accuracy and efficiency with generalization, and thus can significantly improve the earthquake location accuracy of previously developed sequential location methods. However, the inconsistent input or output (I/O) formats between multiple packages often limit their cross application. To reduce format barriers, we incorporated a widely used ML phase picker—PhaseNet—with several popular earthquake location methods and developed a “hands-free” end-to-end ML-based location workflow (named LOC-FLOW), which can be applied directly to continuous waveforms and build high-precision earthquake catalogs at local and regional scales. The renovated open-source package assembles several sequential algorithms including seismic first-arrival picking (PhaseNet and STA/LTA), phase association (REAL), absolute location (VELEST and HYPOINVERSE), and double-difference relative location (hypoDD and GrowClust). We provided different location strategies and I/O interfaces for format conversion to form a seamless earthquake location workflow. Different algorithms can be flexibly selected and/or combined. As an example, we apply LOC-FLOW to the 28 September 2004 Mw 6.0 Parkfield, California, earthquake sequence. LOC-FLOW accomplished seismic phase picking, association, velocity model updating, station correction, absolute location, and double-difference relocation for 16-day continuous seismic data. We detected and located 3.7 times (i.e., 4357) as many as earthquakes with cross-correlation double-difference locations from the Northern California Earthquake Data Center. Our study demonstrates that LOC-FLOW is capable of building high-precision earthquake catalogs efficiently and seamlessly from continuous seismic data.

Emotion Recognition by Audio Signals as one of the Ways to Combat Phone Fraud

The relevance of the study is dictated by the current state in the field of telephone fraud. According to research conducted by Kaspersky Lab, the share of users who encountered various unwanted spam calls in the spring of 2022 was at the level of 71%. The subject of the research is machine learning and deep learning technologies for determining emotions by the timbre of the voice. The authors consider in detail such aspects as: the creation of a marked-up dataset; the conversion of WAV audio format into a numerical form convenient for fast processing; machine learning methods for solving the problem of multiclass classification; the construction and optimization of neural network architecture to determine emotions in real time. A special contribution to the study of the topic is that the authors implemented a fast method of conversion sound formats into numerical coefficients, which significantly increased the speed of data processing, practically without sacrificing their informativeness. As a result, the models were trained by machine learning algorithms quickly and efficiently. It should be particularly noted that the architecture of a convolutional neural network was modeled, which allowed to obtain the quality of model training up to 98%. The model turned out to be lightweight and was taken as the basis for training the model to determine emotions in real time. The results of the real-time operation of the model were comparable with the results of the trained model. The developed algorithms can be implemented in the work of mobile operators or banks in the fight against telephone fraud. The article was prepared as part of the state assignment of the Government of the Russian Federation to the Financial University for 2022 on the topic "Models and methods of text recognition in anti-telephone fraud systems" (VTK-GZ-PI-30-2022).

Comparable Results of Helicobacter pylori Antibiotic Resistance Testing of Stools vs Gastric Biopsies Using Next-Generation Sequencing

Comparable Results of Helicobacter pylori Antibiotic Resistance Testing of Stools vs Gastric Biopsies Using Next-Generation Sequencing

Research on BIM model lightweighting methods and IoT technology application in the context of WebGL

In view of the fact that most of the existing building information modeling (BIM) platforms are based on the Client/Server (C/S) mode of client-side applications, which brings about problems such as weak data interoperability, poor model semantic integrity and bad user-friendliness, this paper proposed an IFC-JSON and IFC-glTF model file format conversion process based on WebGL and an improved Draco algorithm that can further lighten glTF files. The experimental results showed that this option can effectively reduce data redundancy, then combined IoT (Internet of Things) as well as sensor technology in project practice to collect and transmit building-related O&M data, and finally visualised the equipment status of the building on the Web based on WebGL technology, providing a new application way for the combination of BIM technology and IOT technology in the browser/server mode.

Design and Research of M2M Message Transfer Mechanism of Looms for Information Transmission

Machine-to-machine (M2M) communication is a key implementation technology for future Industrial Internet of Things (IIoT) applications. The requirements for flexibility, efficiency, and crossplatform compatibility in intermodule communication between the connected machines raise challenges for the M2M messaging mechanism. For the loom message transmission mechanism, it is necessary to have certain flexibility and meet certain M2M communication requirements to understand each other. In this paper, we analyze and study the data model, data exchange language, MQTT protocol and logo resolution technology of looms to address the current problems of low data transmission efficiency, poor flexibility and lack of flexibility in the network.By establishing the data model of looms and classifying them by topic, selecting JSON format as the transmission format of data, and using MQTT protocol to realize data transmission between loom networks, an MQTT-based messaging mechanism is established.The data involved in the communication of the loom equipment are grouped according to the communication requirements, and only the loom data required by the application are subscribed. At the same time, the hardware and software parts of the communication module are designed with ESP32 as the core, so that it can complete the functions of loom network communication, data analysis, format conversion and so on. In addition, the real-time requirements of the loom network communication and the network requirements for future intelligent manufacturing must be met. This paper uses the BP neural network to intelligently optimize the loom network, and the existing problems related to performance indices are analyzed. The original loom network is optimized by integrating the RSSI, QoS requirements, transmission priority, real-time communication and data transmission volume of nodes. In the meantime, the particle swarm optimization algorithm is introduced to optimize the BP neural network to avoid the problem that it easily can fall into the local optimal solution and effectively improving the network optimization efficiency. Finally, the feasibility of this method is proven by using the experimental platform, which can solve the problems of low network transmission efficiency and difficult network optimization of looms and provide an effective technical example of loom information transmission and network optimization for industry.