Format Conversion Research Articles

Research on the Influence of AI Technology and Optimization Strategy on the Chinese Manufacturing Enterprises about Digital Transformation

Abstract: The rapid evolution of the digital economy has fostered a favorable environment for the growth of the artificial intelligence (AI) industry, catalyzing a transformative shift across diverse sectors. AI technology is driving the digital transformation landscape, contributing to the emergence of a novel economic framework characterized by enhanced unity, fairness, and operational efficiency. Particularly in China's manufacturing domain, substantial governmental support has propelled the widespread adoption of AI, thereby facilitating significant strides towards industrial modernization. However, despite the promising trajectory, notable challenges persist. These include the scarcity of AI talent, concerns regarding data security such as leakage, and the ongoing refinement of compatibility with existing systems. Addressing these challenges necessitates a multifaceted approach. Efforts should prioritize bolstering personnel training initiatives in AI-related domains through collaborative engagements with industry, universities, and research institutes. This cooperative endeavor can nurture a skilled workforce adept at navigating the complexities of AI implementation. Moreover, stringent measures must be implemented to safeguard sensitive data, including robust encryption protocols, secure computing practices, and meticulous access controls to mitigate the risks of data breaches. Furthermore, promoting standardization processes and embracing data format conversion tools are critical steps towards enhancing interoperability across disparate systems. In conclusion, this discourse underscores the pivotal role of AI in driving the digital transformation journey of Chinese manufacturing enterprises. By addressing pertinent challenges and embracing strategic initiatives, these enterprises can fortify their resilience, navigate future complexities, and achieve sustainable development in the digital era.

Improved Execution Efficiency of FPE Scheme Algorithm Based on Structural Optimization

The model of preserving a format encryption scheme based on a Feistel structure has developed rapidly and has been widely used in recent years. In this paper, the software implementation of the FF1 algorithm for the model was presented, and its execution efficiency was evaluated. Then, the efficiency bottleneck problem and its causes were identified. Based on the above analysis results, optimization methods were given from the perspectives of prepossessing, algorithm structure, and format conversion function, and implementation plans were provided. Finally, the simulation results show that the optimized performance improvement is significant, and the degree of performance improvement increases with the increase in plain text length.

GCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

High-precision global navigation satellite system (GNSS) time series data contain rich tectonic and non-tectonic motion information. However, such data also have common-mode errors (CME) related to spatiotemporal characteristics. CME will produce bias in accurate and reliable GNSS station positions and velocities estimations, ultimately deciphering erroneous geophysical signals. Currently, CME extraction methods can be divided into three categories: regional stack filtering, reference frame filtering, and statistical signal decomposition. The third method is widely used in geodetic data processing, including GNSS time series analysis, global time-varying gravity signal extraction, interferometric synthetic aperture radar (InSAR) time series analysis, etc. The demand for GNSS time series analysis software with interactive, visualization, and multifunctional features has also increased with the increase in GNSS stations. Therefore, we developed gCMEbox based on MATLAB. The gCMEbox’s primary functions include GNSS time series preprocessing, multiple statistical signal decomposition algorithms for extracting CME, GNSS time series analysis, visualization, velocity field construction, and generation of multiple time series post-processing products. Based on GNSS time series data, this study utilized the gCMEbox for data preprocessing, including format conversion, outlier removal, missing data recovery, etc. By applying statistical signal decomposition methods, gCMEbox successfully extracted CME. The primary objective of this study is to share this toolbox with the scientific community, providing a comprehensive tool for GNSS time series analysis and applications.

A UAV Thermal Imaging Format Conversion System and Its Application in Mosaic Surface Microthermal Environment Analysis.

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones' industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area's temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research.

Modulation format conversion between BPSK and 8QAM signals using coherent interference and four-wave mixing

Optical fiber networks need to transmit various types of client data with different requirements of bandwidth, reach, latency, and capacity by interconnecting backbone, metro, and local access networks. Since the optimal modulation format usually differs, it should be efficiently converted at the intermediate node connecting different networks. Among the multilevel modulation format, 8-ary quadrature amplitude modulation (8QAM) is a candidate for terrestrial networks thanks to the good balance between spectral efficiency and transmission distance. In this paper, we propose modulation format conversion systems from binary phase shift keying (BPSK) to 8QAM using coherent interference in delay interferometer and its reverse conversion system using four-wave mixing in a single highly nonlinear fiber. Conversion performances are numerically evaluated based on bit error rate, constellation diagrams, 8QAM and BPSK propagation lengths, and parameters in electrical signal regeneration. As a result, error-free format conversion is achieved for both conversion systems.

Design and teaching application of a virtual simulation training system for bridge engineering drawing recognition based on Unity3D

In response to the common issues in practical training in vocational engineering construction courses, such as dangerous operation of training equipment, insufficient number of training equipment, and ineffective teaching, a vivid and interactive virtual simulation training system was developed using Unity3D software, taking the U-shaped bridge abutment in the course of Bridge Engineering as an example. Detailed introduction to the development and design process of 3D model establishment, ZSpace device adaptation, file format conversion, model rendering and disassembly, model zooming in and out, script writing and function implementation, UI interface design, etc., in order to achieve immersive interaction functions such as free observation, assembly disassembly, and ZSpace integrated VR virtual experience in the virtual simulation training system. The practical results show that the interactive effect simulated through the simulation training system is superior to that of the physical system, which can enhance the sense of experience, improve learning enthusiasm, and deepen the understanding of the knowledge points in Bridge Engineering Drawing Recognition. After the system is successfully established and applied in the training and teaching process, it will provide an important reference for the training and teaching of relevant courses for students majoring in road and bridge engineering, and also enhance the cultivation of high-quality skilled personnel in engineering majors in vocational colleges.

Breast Cancer Detection in Mammography using Faster Region Convolutional Neural Networks and Group Convolution

Breast cancer is a prevalent and potentially life-threatening medical condition characterized by uncontrolled cell proliferation within breast tissue. This global health concern predominantly among women, although it can affect men as well. The timely and accurate detection of breast cancer is critical, as it significantly influenced treatment outcomes and survival rates. Despite advancements in human-based diagnostic methods, inherent limitations such as subjectivity, human error, and fatigue persist. To address these constraints, computer-vision-based techniques have been explored for breast cancer detection, aiming to enhance early detection, reduce diagnostic errors, and improve patient outcomes. This study advances previous approaches by integrating group convolution and the Special Euclidean motion group as a features extractor into the Faster Region Convolutional Neural Networks framework of Detectron2. This integration offers the advantage of enhancing the Convolutional Neural Network by incorporating a method to preserve invariant and equivariant features, effectively leveraging symmetries in mammography images. The INbreast dataset, comprising 410 images from 115 cases, including 90 instances with bilateral breast involvement in women, was utilized. To ensure data compatibility, DICOM to PNG and XML to JSON format conversions were performed. A data enhancement pipeline, encompassing techniques such as image cropping, truncation normalization, contrast-limited adaptive histogram equalization, and image synthesis, was employed for data preprocessing and cleaning. The proposed technique demonstrated competitive results in breast cancer detection, achieving a recall rate of 97.22. This underscores the efficacy of the integrated approach in improving diagnostic accuracy and holds promise for advancing computer-vision-based breast cancer detection methodologies.

Machine Learning for Automatic Detection of Velopharyngeal Dysfunction: A Preliminary Report.

Even after palatoplasty, the incidence of velopharyngeal dysfunction (VPD) can reach 30%; however, these estimates arise from high-income countries (HICs) where speech-language pathologists (SLP) are part of standardized cleft teams. The VPD burden in low- and middle-income countries (LMICs) is unknown. This study aims to develop a machine-learning model that can detect the presence of VPD using audio samples alone. Case and control audio samples were obtained from institutional and publicly available sources. A machine-learning model was built using Python software. The initial 110 audio samples used to test and train the model were retested after format conversion and file deidentification. Each sample was tested 5 times yielding a precision of 100%. Sensitivity was 92.73% (95% CI: 82.41%-97.98%) and specificity was 98.18% (95% CI: 90.28%-99.95%). One hundred thirteen prospective samples, which had not yet interacted with the model, were then tested. Precision was again 100% with a sensitivity of 88.89% (95% CI: 78.44%-95.41%) and a specificity of 66% (95% CI: 51.23%-78.79%). VPD affects nearly 100% of patients with unrepaired overt soft palatal clefts and up to 30% of patients who have undergone palatoplasty. VPD can render patients unintelligible, thereby accruing significant psychosocial morbidity. The true burden of VPD in LMICs is unknown, and likely exceeds estimates from HICs. The ability to access a phone-based screening machine-learning model could expand access to diagnostic, and potentially therapeutic modalities for an innumerable amount of patients worldwide who suffer from VPD.

UniSparse: An Intermediate Language for General Sparse Format Customization

The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facilitate optimized software/hardware implementations by utilizing sparsity pattern- or target-aware data structures and layouts to enhance memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Additionally, because these frameworks represent formats using a limited set of per-dimension attributes, they lack the flexibility to accommodate numerous new variations of custom sparse data structures and layouts. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. Unlike the existing attribute-based frameworks, UniSparse decouples the logical representation of the sparse tensor (i.e., the data structure) from its low-level memory layout, enabling the customization of both. As a result, a rich set of format customizations can be succinctly expressed in a small set of well-defined query, mutation, and layout primitives. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats, and automatic code generation of format conversion and compute operations for heterogeneous architectures. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with specialized formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory (PIM) device.

A high-capacity Coverless Image Steganography Based on OMR and Mapping Rules

Securing private information during its transmission over the internet is of paramount importance, as it safeguards against unauthorized access and guarantees the integrity of data. Steganography serves as a technique for enhancing security by concealing sensitive information within digital content such as images, videos, audio, and textual carriers, thus thwarting unauthorized interception. Current methodologies involve manipulating pixels to create stego-images, effectively embedding confidential messages within the image structure. However, this approach has led to steganalysis experts uncovering these hidden messages. To address this issue, an innovative strategy for covert data concealment is proposed. This paper proposes a robust, secure, and embedding capacity coverless image steganography method using OMR and Mapping Rules. The secret information is represented by a binary string, and the bubble sheet is used to represent binary fragments. This approach ensures the confidentiality of sensitive data communicated over the internet. Notably, this approach surpasses previous alternatives in terms of embedding capacity, as substantiated by result analysis. Furthermore, it is adaptable for refinement as needed. Significantly, it exhibits remarkable resilience, safeguarding against a spectrum of attacks including scaling, color space conversion, JPEG compression, thresholding, "salt and pepper" noise, file format conversion, and steganalysis tools, among others.

Fiber-optic parametric amplifier-based all-optical format conversion enabling optical networks interconnection with different modulation formats

A fiber-optic parametric amplifier (FOPA)-based all-optical format conversion (AOFC) scheme is proposed and numerically simulated to achieve optical networks interconnection with different modulation formats. In this scheme, the power transfer function (PTF) and the relative phase shift (RPS) functions between the input and output optical signals for the single-pump FOPA are derived and numerically analyzed. For the input 20 Gbps bipolar 4-ary pulse amplitude modulation (PAM4) signal, it can be converted into a BPSK signal (BPSK1) based on the power saturation characteristic of the PTF. By adjusting the input power of the input PAM4 signal, it can also be converted into a level-swapping PAM4 signal based on the single-pump FOPA. When the level-swapping PAM4 signal is sent into the single-pump FOPA with suitable power again, another BPSK signal (BPSK2) could also be generated. When the PAM4-to-BPSK format converter based on the single-pump FOPA is combined with the dual-pump FOPA (namely phase-sensitive amplifier, PSA), the 16-ary quadrature amplitude modulation (16QAM) signal can also be de-aggregated into four BPSK signals or two quadrature phase shift keying (QPSK) signals. The constellation diagrams, the power waveforms, the error vector magnitude (EVM) and the bit error rate (BER) of the relevant optical signals are measured to evaluate the scheme performance. The scheme can be deployed at the optical gateway to realize optical networks interconnection using different modulation formats.

Imaging tree root systems using ground penetrating radar (GPR) data in Brazil

Trees sequester carbon dioxide from the atmosphere through photosynthesis, storing it in branches, stems, and roots, where the belowground carbon fraction, approximately ¼ of the total amount, exhibits significant interspecies root biomass variability. Estimating the amount of carbon stored in tree roots of different species is key to understanding an important aspect of climate change and exploring how natural forests, urban tree planting policies, and reforestation projects might help to address it. In this context, one of the most prominent Non-Destructive Testing methods capable of estimating the diameter and length of roots at different depths is ground penetrating radar (GPR). It has been widely used for geological, archaeological, and geotechnical studies due to its accuracy in locating buried material in different contexts, although standards for the correct management of datasets related to belowground root systems are still been developed. This paper reports a GPR signal processing flow to estimate the root diameter of three species of tropical forest trees, and to demonstrate the method’s viability, a dataset was collected in a study area with a 900 MHz shielded antenna. A multi-stage data processing flow is then presented, including raw data, file format conversion, zero-time adjustment, background removal, signal gain, Stolt FK migration, and time-to-depth conversion with hyperbolic adjustment velocity. The resulting data were converted from true amplitude data to a trace envelope. High amplitudes on the envelope section, with lateral continuity in parallel sections, were interpreted as roots. However, the interpretation of outcomes encounters notable complexities, primarily attributable to the intricate nature of subsurface root architectures, the soil matrix characterized by significant clay content, and the co-occurrence of buried materials proximate to the arboreal subjects. Consequently, amplitudes discerned within ground penetrating radar (GPR) 2D sections necessitate cautious interpretation, as they are not immediately indicative of subsurface roots. To overcome this difficulty, this study used direct measurements of the roots in the field, to confirm the GPR data. Despite these complexities, the study demonstrates GPR’s efficacy, particularly in the uppermost soil layer-a pivotal carbon reservoir with a 96% correlation (R2) between GPR-derived coarse-root diameter estimates and field measurements.

High performance artificial visual perception and recognition with a plasmon-enhanced 2D material neural network

The development of neuromorphic visual systems has recently gained momentum due to their potential in areas such as autonomous vehicles and robotics. However, current machine visual systems based on silicon technology usually contain photosensor arrays, format conversion, memory and processing modules. As a result, the redundant data shuttling between each unit, resulting in large latency and high-power consumption, seriously limits the performance of neuromorphic vision chips. Here, we demonstrate an artificial neural network (ANN) architecture based on an integrated 2D MoS2/Ag nanograting phototransistor array, which can simultaneously sense, pre-process and recognize optical images without latency. The pre-processing function of the device under photoelectric synergy ensures considerable improvement of efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and low energy consumption per spike (2.4 × 10−17 J).

PAPipe: A Pipeline for Comprehensive Population Genetic Analysis.

Advancements in next-generation sequencing (NGS) technologies have led to a substantial increase in the availability of population genetic variant data, thus prompting the development of various population analysis tools to enhance our understanding of population structure and evolution. The tools that are currently used to analyze population genetic variant data generally require different environments, parameters, and formats of the input data, which can act as a barrier preventing the wide-spread usage of such tools by general researchers who may not be familiar with bioinformatics. To address this problem, we have developed an automated and comprehensive pipeline called PAPipe to perform nine widely used population genetic analyses using population NGS data. PAPipe seamlessly interconnects and serializes multiple steps, such as read trimming and mapping, genetic variant calling, data filtering, and format converting, along with nine population genetic analyses such as principal component analysis, phylogenetic analysis, population tree analysis, population structure analysis, linkage disequilibrium decay analysis, selective sweep analysis, population admixture analysis, sequentially Markovian coalescent analysis, and fixation index analysis. PAPipe also provides an easy-to-use web interface that allows for the parameters to be set and the analysis results to be browsed in intuitive manner. PAPipe can be used to generate extensive results that provide insights that can help enhance user convenience and data usability. PAPipe is freely available at https://github.com/jkimlab/PAPipe.

All-Optical format conversion from PAM4 to QPSK based on non-degenerate Phase-Sensitive amplification and pump assisted nonlinear optical loop mirror

An all-optical format conversion (AOFC) scheme from 4-ary pulse amplitude modulation (PAM4) to quadrature phase shift keying (QPSK) based on the non-degenerate phase-sensitive amplification (PSA) and the pump assisted nonlinear optical loop mirror (PA-NOLM) is proposed and numerically simulated for the first time. The input unipolar PAM4 signal with equal amplitude distance (UPAM4-A) is coupled with two input pump lights and injected into the symmetric interferometric fiber optic parameter amplifier (FOPA) to achieve the separation of the UPAM4-A and the new pump light generated at the signal frequency based on the non-degenerate PSA. When the filtered out UPAM4-A is coherent added with the new generated pump, the UPAM4-A signal to the biplolar PAM4 (BPAM4) signal. When the converted BPAM4 is sent into the PA-NOLM after adjusting its power, the BPAM4 signal could be converted into the QPSK signal. The error vector magnitude (EVM) and bit error ratio (BER) performance of the system are measured before and after AOFC with different input and receiver optical signal to noise ratio (OSNR). For the input UPAM4-A signal with an input OSNR of 27 dB, there is about 8.2 dB BER performance improvement in receiver OSNR for the final converted QPSK signal. Additionally, the proposed non-degenerate PSA configuration can be extended to realize the AOFC from one QPSK to two on–off keying (OOK) signals. The scheme can be applied in the optical gateway connecting the long-haul and the short-reach optical networks with suitable modulation formats.

Few-mode fiber Bragg grating-based simultaneous multichannel CSRZ to NRZ format conversion scheme for LP01 and LP11.

We propose what we believe is a novel format conversion scheme using a few-mode fiber Bragg grating (FM-FBG) that can perform multichannel format conversion from carrier-suppressed return-to-zero (CSRZ) to non-return-to-zero (NRZ) for both LP01 and LP11. The multichannel spectral response of FM-FBG is designed according to the algebraic difference between the CSRZ and NRZ spectra outlines. Additionally, the FM-FBG response spectra of LP11 are designed to shift with that of LP01 by the WDM-MDM channel spacing for filtering both modes together. Numerical results demonstrate the successful conversion of both LP01 and LP11 channels, carrying four channels of 200-GHz-spaced CSRZ signals at 40 Gbit/s, into NRZ signals with a high Q-factor (exceeding 14 dB), and the converted NRZ signals exhibit clean and open eye diagrams. Furthermore, the performance analysis also shown that our proposed FM-FBG is robust to central wavelength detuning.

Pump-Free and Reconfigurable All-Optical Modulation Format Conversion for MQAM Signals by Parallel Nonlinear Mach-Zehnder Interferometers

Pump-Free and Reconfigurable All-Optical Modulation Format Conversion for MQAM Signals by Parallel Nonlinear Mach-Zehnder Interferometers

Input-oriented demonstration learning for hybrid evidence fact verification

Hybrid evidence fact verification aims to verify claims against retrieved text and table evidence. Existing methods primarily focus on evidence format conversion and unification strategies to reformat the input to align with the corpus of the pre-trained textual or tabular models. However, these models fail to extract task-specific knowledge from the language model (LM) to learn the correlation between input claim-evidence pairs and output verdicts. Therefore, we propose an input-oriented demonstration learning approach (IODL) for hybrid evidence fact verification. IODL activates the LM’s comprehension capability by incorporating a few examples (task demonstrations) into the input context. Our approach comprises a novel retrieval strategy for selecting examples with similar reasoning patterns to the inputs and a demonstration fusion network that mitigates the LM’s input length limitation, allowing it to leverage more examples. Consequently, the quality and quantity of task demonstrations can be simultaneously enhanced, enabling the demonstration learning paradigm to be well-adapted to our supervised fact verification task. Extensive experiments on the large-scale benchmark FEVEROUS dataset demonstrate the superiority of our proposed method over state-of-the-art baselines. IODL consistently outperforms conventional demonstration-based methods in low-resource and complex reasoning challenge scenarios.

Zero watermarking algorithm for BIM data based on distance partitioning and local feature

ABSTRACT Zero watermarking constructs the watermark information according to the characteristics of the original data, without changing the data structure and data accuracy. Maintaining high data accuracy is the premise of building information modelling (BIM) usability, so zero watermarking is a hotspot in the research of BIM data security protection. BIM model is a type of 3D model, however, most of the existing zero watermarking algorithms for 3D models are difficult to be better applied to BIM data due to data structure differences. To solve this problem, a zero-watermarking algorithm for BIM data based on distance partitioning and skewness measure is proposed. Firstly, after spatial partitioning based on element paradigm value, the mapping relationship between different partitions and watermarking bits is established. Then, the skewness of elements is calculated, and the skewness measure sign is used as the feature to obtain the binary sequence. Finally, the dissimilarity operation is performed on the binary sequence and the original watermarking sequence which was disordered to construct the zero watermark of the BIM data. The experimental results show that the zero watermarks constructed from different BIM data are unique and robust to translation, rotation, element deletion, element addition, and format conversion attacks. In addition, the superiority of this paper’s algorithm over the comparison algorithm in terms of robustness is compared. Therefore, the proposed algorithm can effectively provide technical support for BIM data copyright protection.

GSC: efficient lossless compression of VCF files with fast query.

With the rise of large-scale genome sequencing projects, genotyping of thousands of samples has produced immense variant call format (VCF) files. It is becoming increasingly challenging to store, transfer, and analyze these voluminous files. Compression methods have been used to tackle these issues, aiming for both high compression ratio and fast random access. However, existing methods have not yet achieved a satisfactory compromise between these 2 objectives. To address the aforementioned issue, we introduce GSC (Genotype Sparse Compression), a specialized and refined lossless compression tool for VCF files. In benchmark tests conducted across various open-source datasets, GSC showcased exceptional performance in genotype data compression. Compared with the industry's most advanced tools (namely, GBC and GTC), GSC achieved compression ratios that were higher by 26.9% to 82.4% over GBC and GTC on the datasets, respectively. In lossless compression scenarios, GSC also demonstrated robust performance, with compression ratios 1.5× to 6.5× greater than general-purpose tools like gzip, zstd, and BCFtools-a mode not supported by either GBC or GTC. Achieving such high compression ratios did require some reasonable trade-offs, including longer decompression times, with GSC being 1.2× to 2× slower than GBC, yet 1.1× to 1.4× faster than GTC. Moreover, GSC maintained decompression query speeds that were equivalent to its competitors. In terms of RAM usage, GSC outperformed both counterparts. Overall, GSC's comprehensive performance surpasses that of the most advanced technologies. GSC balances high compression ratios with rapid data access, enhancing genomic data management. It supports seamless PLINK binary format conversion, simplifying downstream analysis.