The market for Truly Random Pattern Generators (TRPGs) is experiencing robust growth, driven by increasing demand across diverse applications. The rising adoption of advanced technologies like 3D stereo machine vision, gesture recognition, and depth sensing in various sectors—including automotive, healthcare, and industrial automation—is a primary catalyst. The need for high-precision, secure, and unpredictable patterns in these applications fuels the demand for TRPGs, surpassing reliance on traditional pseudo-random number generators. We estimate the 2025 market size to be $350 million, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is further fueled by advancements in laser technology, particularly the 640nm and 830nm wavelengths, which offer enhanced performance and efficiency in TRPG applications. The key geographical markets are North America and Europe, with significant potential in the Asia-Pacific region, driven by rapid technological advancements and increasing industrialization. However, high initial investment costs and the need for specialized expertise could present challenges to market expansion. The segmentation of the TRPG market reveals strong growth across all application areas. 3D stereo machine vision currently holds the largest market share, driven by its critical role in autonomous vehicles and robotics. Gesture recognition and depth sensing are witnessing rapid growth, fueled by the increasing popularity of smart devices and interactive user interfaces. Volume measurement applications, particularly in industrial settings, contribute to steady market growth. In terms of wavelength, 830nm TRPGs are currently more prevalent due to their cost-effectiveness, though 640nm technologies are gaining traction for their higher precision in certain applications. Key players in the market, such as Laser Components GmbH and Xi'an Elite Photoelectric Technology Co., Ltd., are continuously innovating and developing new products and solutions to cater to the evolving market needs. This competitive landscape further fosters innovation and accelerates market growth.

The market for Truly Random Pattern Generators (TRPGs) is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 aren't provided, we can extrapolate based on available data and industry trends. Considering the applications mentioned (3D stereo machine vision, gesture recognition, depth sensing, volume measurement) and the technological advancements in laser-based systems (640nm and 830nm wavelengths being prominent), we can infer a significant and expanding market. The CAGR (Compound Annual Growth Rate), although unspecified, is likely within the range of 10-15% given the rapid development and adoption of these technologies in applications demanding high levels of security and randomness, such as cryptography and gaming. Key drivers include the burgeoning need for enhanced security solutions, the rise of sophisticated gaming and simulation technologies, and the proliferation of advanced machine vision systems in various industries. The segmentation by wavelength (640nm and 830nm) reflects the technological choices influencing market dynamics, with ongoing development possibly leading to the emergence of new wavelength segments in the future. Companies like Laser Components GmbH and Xi'an Elite Photoelectric Technology Co., Ltd., highlight a geographically diverse manufacturing base, indicating strong global participation in the TRPG market. Geographic expansion is expected, particularly in regions like North America and Asia-Pacific, due to strong technological adoption rates and substantial investment in related industries. Growth restraints might include the relatively high cost of implementing TRPGs in some applications and the ongoing challenges related to standardization and certification processes. However, these are likely to be offset by the ever-increasing demand for secure and reliable random number generation across diverse sectors. The forecast period (2025-2033) suggests continued market expansion, potentially driven by technological innovation leading to miniaturization, increased efficiency, and lower costs of TRPGs. This overall growth trajectory positions the TRPG market for considerable expansion in the coming decade. The historical period (2019-2024) likely saw significant growth as the foundation for the predicted expansion, laying the groundwork for the future innovations and applications driving the market forward.

The Pseudo-Random Pattern Generator (PRPG) market is experiencing robust growth, driven by increasing demand across diverse applications like 3D stereo machine vision, gesture recognition, depth sensing, and volume measurement. The market's expansion is fueled by advancements in laser technology, particularly the refinement of 660nm and 830nm wavelengths, which offer improved accuracy and performance in various applications. The proliferation of automated systems in manufacturing, robotics, and automotive sectors is significantly contributing to the market's growth. While precise market sizing data is unavailable, based on the provided information and general market trends for similar technologies with comparable CAGR values, a conservative estimate for the 2025 market size could be placed between $350 million and $450 million. The North American and European regions are currently leading the market due to higher adoption rates and technological advancements. However, the Asia-Pacific region is poised for significant growth in the coming years driven by burgeoning industrial automation in countries like China and India. Constraints on market growth primarily relate to the relatively high cost of advanced PRPG systems and the need for specialized expertise in implementation and maintenance. The forecast period from 2025 to 2033 anticipates sustained market growth, with a projected CAGR of approximately 12-15%. This growth trajectory is predicated on continued technological innovation, decreasing component costs, and expanding applications across various sectors including healthcare, consumer electronics, and augmented/virtual reality. While competition among key players like OSELA INC., Laser Components GmbH, and Xi'an Elite Photoelectric Technology Co., Ltd., is expected to intensify, market fragmentation presents opportunities for smaller niche players to enter with specialized solutions. Further growth will also be contingent on overcoming challenges related to the integration of PRPG technology into existing systems and addressing concerns about data security and privacy in applications involving personal data.

The Pseudo Random Pattern Generator (PRPG) market plays a crucial role in various applications, particularly in testing and validating electronic circuits, integrated circuits, and communication devices. PRPGs produce sequences of numbers that mimic the properties of random numbers, which is essential for testing th

The Truly Random Pattern Generator (TRPG) market has emerged as a pivotal player in various industries, including telecommunications, cybersecurity, gaming, and data analysis, owing to its ability to generate non-repeating, unpredictable sequences that enhance the performance and security of applications. Utilizing

# Synthetic Electrochemical Impedance Spectra Generator

This Python script generates synthetic EIS spectra for predefined circuits using the `impedance.models.circuits.CustomCircuit` library. It simulates realistic experimental datasets for educational purposes, incorporating random file names, missing values, and empty files.

## Features
- **Circuit Modeling**: Supports circuits like `R0-C0`, `R0-p(R1,C1)`, etc., with randomized parameters.
- **Custom Frequency Range**: Logarithmic sweep from \(10^5\) to \(10^{-2}\) Hz.
- **Realistic Data Challenges**:
- Random 3-line headers in files.
- Missing values in every other 100th file.
- Empty data in every 100th file.

## Output Format
- **Columns**: `Freq_Hz`, `Re_Z_Ohm`, `-Im_Z_Ohm`, `|Z|_Ohm`, `Phase_deg`.
- **File Naming**: Random 8-character alphanumeric strings.

Customize circuits, frequency range, and data patterns as needed.

The global market for Pseudo-Random Pattern Generators (PRPGs) is experiencing robust growth, driven by the increasing demand for advanced 3D imaging and sensing technologies across various sectors. The rising adoption of PRPGs in applications like 3D stereo machine vision, gesture recognition, depth sensing, and volume measurement is a key factor fueling this expansion. The market is segmented by wavelength (660nm, 830nm, and others), reflecting the diverse requirements of different applications. Companies like OSELA INC., Laser Components GmbH, and Xi'an Elite Photoelectric Technology Co., Ltd. are key players shaping the competitive landscape through innovation and strategic partnerships. The North American and European regions currently hold significant market share, but the Asia-Pacific region is projected to experience substantial growth in the coming years due to the increasing adoption of automation and advanced technologies in industries like manufacturing and automotive. The market's growth trajectory is expected to continue, driven by ongoing technological advancements, miniaturization of PRPG devices, and the expanding application areas. The market is projected to maintain a healthy Compound Annual Growth Rate (CAGR), leading to significant market expansion over the forecast period (2025-2033). While challenges such as the high initial investment costs for advanced PRPG systems and potential regulatory hurdles in certain regions might act as restraints, the overall market outlook remains positive. Ongoing research and development efforts are focused on enhancing the efficiency, accuracy, and cost-effectiveness of PRPG technology, further propelling market growth. The integration of PRPGs with other advanced technologies, such as artificial intelligence (AI) and machine learning (ML), is also expected to open up new avenues for growth and application within diverse industries. Specific market segment growth rates will vary based on technological advancements and industry adoption, but the overall trend points towards a consistently expanding market for PRPGs.

This folder contains an executable code of the pattern-backtracking FPT algorithm and an experimental data set used for the WSP with class-independent constraints in:J. Crampton, A. Gagarin, G. Gutin, and M. Jones, “On the workflow satisfiability problem with class-independent constraints,” Proc. 10th International Symposium on Parameterized and Exact Computation (IPEC 2015), 16-18 September, Patras, Greece, LIPIcs series, Schloss Dagstuhl - Leibniz Center for Informatics, 2015. The pattern-backtracking FPT algorithm is implemented in C++. The project is compiled using Eclipse Standard/SDK, Version: Kepler Service Release 1. The executable code is created on a MacBook Pro computer having a 2.6 GHz Intel Core i5 processor, 8 GB 1600 MHz DDR3 RAM 2 and running Mac OS X Version 10.9.5. This is a preliminary implementation of the pattern-backtracking FPT algorithm which is still under development. This version only decides whether an instance is satisfiable or not, without returning an actual assignment solution for satisfiable instances. The random generator used to create the experimental data set is a development of the random generator described in:D. Cohen, J. Crampton, A. Gagarin, G. Gutin, and M. Jones, “Algorithms for the workflow satisfiability problem engineered for counting constraints,” J. Combinatorial Optimization (2015), in press.In particular, the random instance generator avoids generation of trivially unsatisfiable instances with respect to class-independent constraints, and the current calendar time is used as a random seed value. Each instance of the original WSP is stored in a file with extension .wsp and named WSP_input_#S_#U_#C_a_b_c_d.wsp, where #S is the number of steps in the instance, #U is the number of users, #C is the number of equivalence classes of users, a is the number of user-independent not-equals (separation-of-duty) constraints, b is the number of user-independent at-most constraints, c is the number of class-independent equivalence constraints (requiring a pair of steps to be performed by users from the same equivalence class), and d is the number of class-independent non-equivalence constraints (requiring a pair of steps to be performed by users from different equivalence classes). The corresponding formulation of the instance in terms of the pseudo-Boolean satisfiability (PB SAT) problem is stored in a file with extension .opb and named WSP_input_#S_#U_#C_a_b_c_d_PBSAT.opb: the reader can install the PB SAT solver SAT4J and run it on these PB SAT formulation input files. The outputs of our runs of SAT4J on the PB SAT formulation are stored in files named WSP_input_#S_#U_#C_a_b_c_d_PBSAT_output.txt. The PB SAT solution converted back to the original WSP solution is stored in files named WSP_input_#S_#U_#C_a_b_c_d_PBSAT_output_WSPsoln.txt (clearly, only solved satisfiable PB SAT instances provide non-empty WSP solution files). The outcome decisions of the FPT algorithm (“satisfiable” or “unsatisfiable”), together with some basic information about its runs (numbers of patterns generated and considered in the search space, the run times) are stored in files named WSP_input_#S_#U_#C_a_b_c_d_FPTgen_soln.txt. A new version of the FPT algorithm (under development) is going to return actual assignment solutions in case of satisfiable instances and stop the computational process when the FPT algorithm is not likely to be able to solve an instance. Summary Excel tables for the computational experiments are included as well. The executable code of the FPT algorithm and the experimental data set are provided for non-commercial use only. When using this executable code or the data set, please cite the conference paper above.

ObjectivesEffectiveness of Foot Reflexology (FR) on the pain intensity in Parkinson’s disease (PD) compared with Sham Massage (SM).DesignMonocentric, longitudinal, prospective, double-blind, randomized controlled trial. Randomization with a random number generator in the R software. Fixed-sized block randomization of 3 implemented into Clinsight.ParticipantsIdiopathic PD patients with chronic pain (Visual Analogue Scale (VAS)≥4) were recruited from the Toulouse University Hospital between the 14th of April 2021 and the 25th of May 2025.InterventionFour one-hour long FR or SM sessions three weeks apart with the same specialized FR researcher.Main outcome measurePain intensity change measured by the mean VAS before and after full completed interventions. The difference was compared between group using a Wilcoxon Mann Witney test. Exploratory outcome: brain functional connectivityResults30 PD patients were randomized and analyzed. Interventions were delivered as planned for all patients. Clinical variables did not significantly differed between FR and SM groups. Mean VAS decreased by −12.3 mm ± 15.2 in FR group (n = 15) and −17.9 mm ± 29.4 in SM group (n = 15). Analyses did not reveal any significant difference between the FR and SM groups (p-value = 0.88). There are different patterns in connectivity changes in the medial pain system between responders (at least 30% pain reduction) and non-responders to both therapies. There were no adverse events.ConclusionFR is not more effective than SM in relieving chronic pain in PD. The differences in connectivity patterns within the medial pain pathway may underlie the response to tactile stimulation (FR and SM).Trial registrationClinicalTrials.gov NCT04705207.

Multi-Parameter Spatio-Temporal Gaussian-Neighbourhood Ensemble Generation for Renewable Energy Applications

Description

This dataset contains ensemble forecasts generated using a novel multi-parameter spatio-temporal gaussian-neighbourhood method specifically designed for renewable energy meteorological applications. The method produces physically consistent, spatially coherent ensemble members that preserve cross-variable relationships critical for wind, solar, and hydropower energy assessments.

Key Features

Multi-Scale Perturbation Framework: The ensemble generation employs a sophisticated three-scale perturbation approach:

• Synoptic Scale (150 km): Captures large-scale air mass uncertainty and synoptic pattern variations
• Mesoscale (50 km): Represents intermediate-scale processes including convective organization and orographic effects
• Local Scale (15 km): Addresses fine-scale variability related to surface heterogeneity and boundary layer processes

Physics-Informed Constraints: All ensemble members maintain meteorological realism through:

• Thermodynamic consistency (Clausius-Clapeyron constraints)
• Mass conservation for wind fields
• Energy balance relationships between radiation, clouds, and temperature
• Geostrophic balance preservation

Renewable Energy Variables: Each ensemble member includes derived variables optimized for energy applications:

• Wind power density at 10m and 100m heights
• Hub-height wind speed extrapolation using stability-dependent power laws
• Clear sky index and photovoltaic capacity factor estimation
• Basin-averaged precipitation for hydropower assessment

Technical Implementation

Mathematical Framework:

X^(i)(s,t) = X(s,t) + ε^(i)(s,t)
ε^(i)(s,t) = Σ_k α_k × G_σk × η_k^(i)(s,t)

Where:

• X^(i)(s,t): Ensemble member i at location s, time t
• α_k: Scale-dependent amplitude weights
• G_σk: Gaussian spatial correlation filter
• η_k^(i)(s,t): Independent random fields at scale k

Temporal Correlation: Implemented through first-order autoregressive process with scale-dependent persistence (synoptic: 12h, mesoscale: 6h, local: 2h)

Data Sources

The ensemble generation method is compatible with multiple input data sources:

• ARCO-ERA5: Primary reanalysis dataset with 0.25° spatial resolution
• WRF Model Output: High-resolution numerical weather prediction data

Variables Included

Core Meteorological Variables:

• 2-meter temperature (K)
• 10-meter wind components (u, v) (m/s)
• 100-meter wind components (u, v) (m/s)
• Total precipitation (mm)
• Surface pressure (Pa)
• Surface solar radiation downwards (W/m²)
• Mean sea level pressure (Pa)

Quality Control

All ensemble members undergo comprehensive quality control:

• Physical bounds checking for all variables
• Cross-variable consistency validation
• Spatial and temporal continuity assessment
• Energy balance verification

Applications

This ensemble dataset is particularly valuable for:

• Wind Energy: Turbine siting, power forecasting, ramp event analysis
• Solar Energy: PV system planning, intermittency assessment, grid integration
• Hydropower: Basin-scale precipitation uncertainty, runoff modeling
• Energy System Operations: Portfolio risk assessment, demand forecasting
• Climate Risk Assessment: Extreme event probability, long-term resource planning

Methodology Reference

The methodology is based on the multi-parameter spatio-temporal gaussian-neighbourhood approach developed within the EnergAIze project, combining classical ensemble generation techniques with modern physics-informed constraints specifically tailored for renewable energy applications.

Technical Specifications

Spatial Resolution: Typically 0.25° (approximately 25 km) for demonstration cases Temporal Resolution: Hourly time steps Ensemble Size: 20-50 members (configurable) Domain Coverage: Central European focus with global applicability Format: NetCDF-4 with CF-compliant metadata

Computational Performance

• Generation Speed: 10-25 ensemble members per minute for typical domains
• Memory Requirements: <2GB for 3-day case studies
• Scalability: Linear scaling with ensemble size and domain area
• Reproducibility: Deterministic results with fixed random seeds

Validation Results

The ensemble system demonstrates:

• Statistical Consistency: Flat rank histograms across all variables
• Spread-Skill Relationships: Strong correlation (r > 0.7) between ensemble spread and forecast error
• Probabilistic Skill: 15-25% improvement in Continuous Ranked Probability Score (CRPS) over traditional methods
• Physical Realism: Maintenance of atmospheric dynamics and energy balance relationships

Citation

If you use this dataset in your research, please get in touch for citation.

Acknowledgments

This work was conducted as part of the EnergAIze project, focusing on artificial intelligence applications for renewable energy meteorology. The development was supported by [funding information].

Contact

For questions about the dataset or methodology, please contact: irene.schicker@geosphere.at

Version History

• v1.0: Initial release with ARCO-ERA5 and WRF support
• v1.1: Added enhanced wind calculations and visualization improvements
• v1.2: Integrated machine learning extensions and pattern recognition capabilities

Overview This repository contains 742,688 samples of simulated Convergent Beam Electron Diffraction patterns (CBEDs); the training data for the paper "Phase Object Reconstruction for 4D-STEM using Deep Learning". The folder contains multiple hdf5 datasets. Each dataset has a corresponding Excel-sheet containing detailed information and simulation parameters for every datapoint, as well as a summary-report containing the parameter distributions, hdf5-infos and random number generator settings. This makes every dataset reproducible, using the simulation codes provided in https://github.com/ThFriedrich/ap_data_generation. Technical details Every Datapoint consists of a 3x3 set of adjacent Convergent Beam Electron Diffraction pattern (CBEDs), the coherent exit wave phase and amplitude in real and reciprocal space, and the probe functions phase and amplitude in real space. All patterns are 64x64 pixel in 16 bit unsigned integer data format. Every hdf5 file has the following structure: Attributes 'Seed': 6108236 'State': 251786606 ... 'Type': 'twister' 'arch': 'glnxa64' 'gpu': 'NVIDIA GeForce RTX 3080' 'matlab_ver': '2021a' Dataset 'features' Size: 64x64x9x5000 Datatype: H5T_STD_U16LE (uint16) Dataset 'labels_k' Size: 64x64x2x5000 Datatype: H5T_STD_U16LE (uint16) Dataset 'labels_r' Size: 64x64x2x5000 Datatype: H5T_STD_U16LE (uint16) Dataset 'probe_r' Size: 64x64x2x5000 Datatype: H5T_STD_U16LE (uint16) Dataset 'meta' Size: 19x5000 Datatype: H5T_IEEE_F32LE (single) The data was written to hdf5 in matlab. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly. The integer arrays were scaled to use the full range of the uint16 datatype. The scaling values are stored under "meta". To restore the original values in floating point numbers, convert the arrays like this: Matlab: hdf_file = ['db_h5_b_5_Training.h5']; n = 128; % load n k-space exit waves x = single(h5read(hdf_file, '/labels_k', [1,1,1,1], [64,64,2,n])); % meta contains parameters and scaling factors for a given datapoint in following order: [E_0(keV), cond_lens_outer_aper_ang(mrad), collection angle(rA), step_size(A), scale_cbed_1 ... scale_cbed_9, scale_phase_k, scale_amp_k, scale_phase_r, scale_amp_r, scale_probe_phase_r, scale_probe_amp_r] s = h5read(hdf_file, '/meta', [14,1], [2,n]); amplitude = zeros(64,64,n); phase = zeros(64,64,n); for ix = 1:n phase(:,:,n) = (x(:,:,1,n)*s(1,ix) / 65536) - pi; amplitude(:,:,n) = (x(:,:,2,n)*s(2,ix)) / 65536; end % The 9 CBEDs correspond to a 3x3 kernel of patterns. The order in [x,y] is: %[[3, 6, 9]; % [2, 5, 8]; % [1, 4, 7]]

This folder contains an executable code of the pattern-backtracking FPT algorithm and an experimental data set used for the WSP with class-independent constraints in:J. Crampton, A. Gagarin, G. Gutin, M. Jones, and M. Wahlstrom, “On the workflow satisfiability problem with class-independent constraints for hierarchical organizations,” 2015. The pattern-backtracking FPT algorithm is implemented in C++. The project is compiled using Eclipse Standard/SDK, Version: Kepler Service Release 1. The executable code is created on a MacBook Pro computer having a 2.6 GHz Intel Core i5 processor, 8 GB 1600 MHz DDR3 RAM 2 and running Mac OS X Version 10.9.5. This is an advanced and more efficient implementation of the pattern-backtracking FPT algorithm which returns a solution assignment in the case of solved satisfiable instances and explicitly checks correctness of the obtained solution assignment. Some memory and time control features are added as well. The random generator used to create the experimental data set is a development of the random generator described in:D. Cohen, J. Crampton, A. Gagarin, G. Gutin, and M. Jones, “Algorithms for the workflow satisfiability problem engineered for counting constraints,” J. Combinatorial Optimization (2015), in press.In particular, the random instance generator avoids generation of trivially unsatisfiable instances with respect to class-independent constraints. The current calendar time is used as a random seed value. Each instance of the original WSP is stored in a file with extension .wsp and named WSP_input_#S_#U_#C_a_b_c_d.wsp, where #S is the number of steps in the instance, #U is the number of users, #C is the number of equivalence classes of users, a is the number of user-independent not-equals (separation-of-duty) constraints, b is the number of user-independent at-most constraints, c is the number of class-independent equivalence constraints (requiring a pair of steps to be performed by users from the same equivalence class), and d is the number of class-independent non-equivalence constraints (requiring a pair of steps to be performed by users from different equivalence classes). The corresponding formulation of the instance in terms of the pseudo-Boolean satisfiability (PB SAT) problem is stored in a file with extension .opb and named WSP_input_#S_#U_#C_a_b_c_d_PBSAT.opb. A reader can install the PB SAT solver SAT4J and run it on these PB SAT formulation input files. The outputs of our runs of SAT4J on the PB SAT formulation are stored in files named WSP_input_#S_#U_#C_a_b_c_d_PBSAT_output.txt. The PB SAT solution converted back to the original WSP solution is stored in files named WSP_input_#S_#U_#C_a_b_c_d_PBSAT_output_WSPsoln.txt (clearly, only solved satisfiable PB SAT instances provide non-empty WSP solution files). The outcome decisions of the FPT algorithm (“satisfiable” or “unsatisfiable”), together with solution assignments (if applicable) and some basic information about its runs (numbers of patterns generated and considered in the search space, the running times) are stored in files named WSP_input_#S_#U_#C_a_b_c_d_FPTgen_soln.txt. This implementation of the FPT algorithm has check points for memory usage and elapsed running time. It stops the computational process when the running time of the FPT algorithm reaches one hour limit or the virtual memory consumption exceeds 64GB. Summary Excel tables for the computational experiments are included as well. The executable code of the FPT algorithm and the experimental data set are provided for non-commercial use only. When using this executable code or the data set, please cite the full-size paper above.