* Repository author: Tommaso Zanotti
* email: tommaso.zanotti@unimore.it or francescomaria.puglisi@unimore.it
* Version v1.0

This repository includes MATLAB files and datasets related to the IEEE IIRW 2023 conference proceeding:
T. Zanotti et al., "Reliability Analysis of Random Telegraph Noisebased True Random Number Generators," 2023 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 2023, pp. 1-6, doi: 10.1109/IIRW59383.2023.10477697

The repository includes:

• The data of the bitmaps reported in Fig. 4, i.e., the results of the simulation of the ideal RTN-based TRNG circuit for different reseeding strategies.
  To load and plot the data use the "plot_bitmaps.mat" file.
• The result of the circuit simulations considering the EvolvingRTN from the HfO2 device shown in Fig. 7, for two Rgain values. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_11n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_4_8n.csv" (higher Rgain)
• The result of the circuit simulations considering the temporary RTN from the SiO2 device shown in Fig. 8. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_SiO2_1c_300s_Vth_180m_Noise_Ibias_1.5n.csv" (ref. Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.575n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.425n.csv" (higher Rgain)

This is a JSON format file generated by a random number generator in python. The range is 0 to 1000, and numbers are float number.This data will be used by a python script for further transformation.

Market Overview:

The global quantum random number generator RNG sales market is expected to grow at a CAGR of 7.5% during the forecast period from 2022 to 2030. The growth of the quantum RNG market can be attributed to the increasing demand for secure communication and data security. In addition, the growing adoption of Quantum Cryptography in various applications. However, a lack of awareness about quantum cryptography among end users may restrain the growth of this market during the forecast period.

Product Definition:

Quantum Random Number Generator Sales is the process of selling quantum random number generators. These are devices that generate random numbers using the principles of quantum mechanics. They are used for security applications, such as generating cryptographic keys, and in other settings where true randomness is important.

PCIe Type:

PCIe is a high-speed I/O interconnect standard for external cards. It is used in computers, servers, storage devices, and other electronic devices. PCIe provides better performance over PCI and also uses less power; making it an ideal choice for high-end systems that require more than basic functions such as graphics adapters.

USB Type:

USB Type is a specification for a type of connector used on portable devices, such as personal computers. USB Connectors are typically rectangular with a protrusion in one corner that fits into the corresponding receptacle on the device. It has three major interfaces, namely USB Mass Storage (MS), Universal Serial Bus (USB) Power Delivery, and USB Host Control.

Application Insights:

Quantum communication is expected to be the fastest-growing application segment over the forecast period. Quantum communication offers enhanced security and privacy as compared to classical communication systems due to characteristics of quantum mechanics such as uncertainty principle, non-locality, and entanglement. Traditional Information Security is expected to be the second-fastest growing application segment over the forecast period. Traditional Information Security applications use classical security methods such as passwords, firewalls, and intrusion detection systems to protect information.

Cryptography is expected to be the third-fastest growing application segment over the forecast period. Cryptography uses mathematical algorithms to secure data and communication. The betting industry is expected to be the fourth-fastest growing application segment over the forecast period. The betting industry uses cryptography for security purposes such as preventing fraud and ensuring fairness in gambling transactions. Other is expected to be the slowest growing application segment over the forecast period. Other includes applications that are not classified into any other category

Regional Analysis:

North America dominated the global market in terms of revenue share in 2019. The region is expected to continue its dominance over the forecast period owing to the high demand for secure and private communication channels among enterprises and government agencies. Moreover, the growing adoption of PCIe-type RNGs by several key companies for their critical applications is also likely to drive the regional growth over the forecast period. Europe is expected to witness modest growth over the forecast period owing to the increasing demand for quantum-safe cryptography and other applications in the region. The Asia Pacific is expected to grow at a faster pace than other regions due to the growing adoption of blockchain technology and increased investment in RNGs by key companies in this region. The Middle East & Africa is expected to account for a small share of the global market over the forecast period, as there are limited opportunities for quantum-safe cryptography and other key applications in this region.

Growth Factors:

• Increasing demand for secure data transmission and storage.
• A growing number of cyber-attacks and data breaches.
• The proliferation of IoT devices and big data analytics.
• Development of quantum computing technology.
• Rising awareness about the benefits of using quantum random number generators.

Report Scope

Excel sheets in order: The sheet entitled “Hens Original Data” contains the results of an experiment conducted to study the response of laying hens during initial phase of egg production subjected to different intakes of dietary threonine. The sheet entitled “Simulated data & fitting values” contains the 10 simulated data sets that were generated using a standard procedure of random number generator. The predicted values obtained by the new three-parameter and conventional four-parameter logistic models were also appeared in this sheet. (XLSX)

The dataset contains all gathered data from the experiment from Wednesday, March 16, 2022 11:58:41.929 AM UTC+0 (1647431921929) until Sunday, April 3, 2022 1:08:35.353 PM UTC+0 (1648991315353). The experiment was executed during physical presence within the Arctic Circle in Tromsø, Norway 69° 40' 53.117'' N 18° 58' 36.027'' E at 35m elevation above sea level. The dataset was gathered with a prototype [1] based on the CREDO android application [2]. The main research is to use Ultra High Energy Cosmic Rays (UHECR) as an entropy source for a Random Bit Generator (RBG).

The associated publication will probably have the title "Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator"

In order to reproduce the results the SQLite3 database "mrng_arctic_experiment_2022.db" is needed. To get the visual representations of the detections use "image_decoding_and_codesnippets.py" to generate the cleaned (414 detections / ~15MB) or the uncleaned (5567 detections / ~195 MB) dataset. The compressed folder "raw_data_incl_space_weather.7z" contains all raw data as gathered with the MRNG prototype, unprocessed, uncleaned, and unmerged.

[1] https://github.com/StefanKutschera/mrng-prototype, visited on 27.03.2023

[2] https://github.com/credo-science/credo-detector-android, visited on 27.03.2023

The Hardware Random Number Generator (HRNG) market is experiencing robust growth, driven by increasing demand across diverse sectors. The expanding applications of quantum computing, cryptography, and gaming, coupled with the need for highly secure and unpredictable random numbers in various industries, are key factors fueling this expansion. While precise market sizing data is unavailable, based on the provided study period (2019-2033), a base year of 2025, and a forecast period of 2025-2033, we can infer significant market potential. Assuming a conservative Compound Annual Growth Rate (CAGR) of 15% (a reasonable estimate given the technological advancements and increasing demand), and a 2025 market value in the hundreds of millions (a plausible figure considering the significant investments in related technologies), the market is projected to reach several billion dollars by 2033. The segmentation reveals that circuit-based HRNGs currently hold a larger market share compared to physical source-based systems, but the latter are anticipated to witness accelerated growth due to advancements in their technology and associated benefits in terms of security and unpredictability. Geographically, North America and Europe are currently leading the market, with substantial growth expected from Asia-Pacific regions driven by increased technological adoption and government initiatives. However, regulatory hurdles and the complexity of integrating HRNGs into existing systems pose challenges to widespread adoption. The competitive landscape features both established players like Synopsys and Intel, alongside specialized companies like ID Quantique and Quside. This indicates a market ripe for both innovation and consolidation. Furthermore, the emergence of new applications and advancements in quantum-resistant cryptography will further stimulate market growth. The continued focus on data security and privacy across various sectors will ensure persistent demand for reliable and high-quality HRNGs. The market is expected to witness further segmentation and specialization in the coming years as different applications require unique HRNG characteristics in terms of speed, entropy quality, and physical security. This is likely to lead to a more diversified market with a range of solutions catering to varied niche applications.

Rack Random Number Generator Market Outlook

The global Rack Random Number Generator market size was estimated at USD 1.2 billion in 2023 and is projected to reach USD 2.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 8.5% during the forecast period. This growth is driven by the increasing demand for secure and reliable random number generation across various applications such as gaming, cryptography, and statistical sampling. The rise in cyber-attacks and the need for enhanced security protocols in digital transactions are significant growth factors propelling the market forward.

One of the primary growth drivers for the Rack Random Number Generator market is the increasing complexity of cyber-attacks and the resultant need for robust security measures. Random number generators (RNGs) play a crucial role in encryption and cryptographic applications, ensuring the security and integrity of data transmissions. The growing adoption of digital banking and online financial services has heightened the demand for RNGs to safeguard sensitive information against unauthorized access and fraud, thus fueling market growth.

Another significant factor contributing to the market's expansion is the rising popularity of online gaming and gambling platforms. RNGs are integral to ensuring fair play and unpredictability in gaming outcomes, which helps maintain player trust and platform credibility. As the gaming industry continues to evolve with advancements in technology and increased internet penetration, the demand for reliable and high-performance RNGs is expected to surge, further driving market growth.

Furthermore, the increasing use of RNGs in statistical sampling and simulations across various industries, including healthcare, finance, and research, is propelling the market. RNGs are essential tools for generating random samples and simulating complex models, which are critical for data analysis, risk assessment, and decision-making processes. The growing reliance on data-driven insights and the need for accurate, randomized data in scientific studies are key drivers boosting the adoption of RNGs in these applications.

Regionally, North America is expected to dominate the Rack Random Number Generator market during the forecast period. The region's strong presence of major technology companies, high adoption rate of advanced security solutions, and significant investments in research and development activities are contributing to market growth. Additionally, Asia Pacific is anticipated to witness substantial growth due to the increasing digitalization, expanding IT infrastructure, and rising awareness about data security among businesses and consumers.

Type Analysis

The Rack Random Number Generator market is segmented by type into Hardware RNG and Software RNG. Hardware RNGs are physical devices that generate random numbers using inherent physical processes, such as electronic noise. These RNGs are considered highly secure and reliable, making them ideal for critical applications in cryptography and secure communications. The demand for hardware RNGs is expected to grow steadily as organizations seek to enhance their security frameworks and protect sensitive data from potential cyber threats.

Software RNGs, on the other hand, generate random numbers through algorithmic processes. While they are less expensive and easier to implement compared to hardware RNGs, they are generally considered less secure due to their deterministic nature. However, advancements in software RNG algorithms have significantly improved their security and unpredictability, making them suitable for various applications, including gaming and statistical sampling. The flexibility and cost-effectiveness of software RNGs are key factors driving their adoption in the market.

In terms of market share, hardware RNGs are expected to hold a larger share due to their superior security features and widespread use in critical applications. However, the software RNG segment is anticipated to grow at a faster pace during the forecast period, driven by continuous advancements in software technologies and increasing demand for cost-effective RNG solutions. Both segments are essential to meet the diverse needs of different industries and applications, offering a range of options for businesses to choose from based on their specific requirements.

The integration of hardware and software RNGs into hybrid systems is also gaining traction in the market. These systems combine the strengths of both types, offering

According to Cognitive Market Research, the global Quantum Random Number Generator RNG market size is USD 555.9 million in 2024. It will expand at a compound annual growth rate (CAGR) of 72.60% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 222.36 million in 2024 and will grow at a compound annual growth rate (CAGR) of 70.8% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 166.77 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 127.86 million in 2024 and will grow at a compound annual growth rate (CAGR) of 74.6% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 27.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 72.0% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 11.12 million in 2024 and will grow at a compound annual growth rate (CAGR) of 72.3% from 2024 to 2031.
Cloud held the dominant segment in the Quantum Random Number Generator RNG market in 2024.

Market Dynamics of Quantum Random Number Generator RNG Market

Key Drivers for Quantum Random Number Generator RNG Market

Increasing need for random numbers in cryptography or compute applications

The QRNG is an ideal random key generator since it generates entropy using intrinsic quantum physics properties. Nowadays, applications demand a huge number of keys and randomization to achieve total security. It could include key vaults, games, IoT devices, AI/ML, blockchains, simulations, and vital infrastructure. QRNG is the source of these applications in which trust in randomness is prevalent. Furthermore, it is utilized in encryption for a wide range of applications, including cryptography, numerical simulation, gambling, and game design.

Growing adoption of quantum computing

The increasing use of quantum computing is boosting the market for Quantum Random Number Generators (RNG) as it creates a need for improved random number generation capabilities. The accurate abilities of quantum computing enable RNGs to produce truly random numbers, essential for secure communication and encryption. Advancements in quantum computing will lead to a higher demand for dependable RNGs, driving market expansion to meet the changing requirements of cybersecurity and data encryption.

Restraint Factor for the Quantum Random Number Generator RNG Market

High initial investment

A significant initial investment hinders the Quantum Random Number Generator (RNG) Market, creating a barrier for new entrants and small companies looking to invest in RNG generation. The significant initial costs involved in the research, development, and deployment of quantum RNG solutions may discourage potential entrants from joining the market. This limitation impedes the growth of the market by limiting innovation and competition, potentially hindering progress in the era of RNG and constraining the market's growth

Impact of Covid-19 on the Quantum Random Number Generator RNG Market

The effect of COVID-19 on the Quantum Random Number Generator RNG Market was merged. Although the pandemic initially caused disruptions in supply chains and slowed down certain trends, the increased focus on cybersecurity and data protection during remote work and digital interactions enhanced the need for secure communication solutions such as quantum RNGs. With a focus on safeguarding information, both organizations and governments fueled growth in the Quantum RNG market despite pandemic-related obstacles. Introduction of the Quantum Random Number Generator RNG Market

The Quantum Random Number Generator (QRNG) is a highly sophisticated engineering innovation that combines the power of complex deep-tech technologies like semiconductors, optoelectronics, high-precision electronics, and quantum physics to achieve the highest level of randomness possible. QRNG has shown to be a critical enabling technology for quantum-level security in mobile devices, data centres, and medical implants. They provide consumers with a significant enhancement over ordinary random number generators (RNGs), which have been utilized for years in a variety of business applications. Several factors, including th...

For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is “fooled” into using a genome, supplied by a genetic algorithm, in place of the output of its random number generator. The method is demonstrated with generative heuristics in the domains of 3D design and communications network design. It is also used in novel approaches to genetic programming. 16th European Conference, EuroGP 2013, Vienna, Austria, April 3-5, 2013 Author has checked copyright

Analysis of sensor-based data processed for Random Number Generator seeding.

Artifacts for the paper titled Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?.

This artifact repository contains 9 compressed folders, as follows:

Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system).

Details about the generation of our datasets

1. Synthetic datasets

We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:

1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. From this DAG, time series data for each node is generated using a vector auto-regression (VAR) model. A fault is injected into a node by altering the noise term in the VAR model for two timestamps.

2. RCD data generator [25] uses the pyAgrum package [3] to generate a random DAG based on a given number of nodes, subsequently generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its conditional probability distribution.

3. CausIL data generator [15] generates causal graphs and time series data that simulate the behavior of microservice systems. It first constructs a DAG of services and metrics based on domain knowledge, then generates metric data for each node of the DAG using regressors trained on real metrics data. Unlike the CIRCA and RCD data generators, the CausIL data generator does not have the capability to inject faults.

To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. `syn_rcd`, `syn_circa`) are used to evaluate causal discovery methods, while the faulty datasets (e.g. `rca_rcd`, `rca_circa`) are used to assess RCA methods.

2. Data collected from benchmark microservice systems

We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.

Code

The code to reproduce the experimental results in the paper is available at https://github.com/phamquiluan/RCAEval.

References

As in our paper.

Card Random Number Generator Market Outlook

The global card random number generator market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 3.8 billion by 2032, expanding at a compound annual growth rate (CAGR) of 11.2% during the forecast period. This growth is driven by the increasing demand for secure and fair gaming experiences, as well as the rising need for robust security mechanisms in financial transactions. The rapid digitalization and expansion of online gaming platforms further fuel the market's growth, offering numerous opportunities for advancements in random number generation technology.

One of the primary growth factors for the card random number generator market is the booming online gaming industry. As gaming platforms strive to provide fair and transparent gaming environments, the demand for sophisticated random number generators is surging. These generators ensure that card shuffling and other game mechanics are unpredictable and free from tampering, enhancing user trust and engagement. Additionally, advancements in cryptographic techniques have expanded the application of random number generators in secure online transactions, protecting user data and financial information from cyber threats.

The financial sector also plays a significant role in propelling the growth of the card random number generator market. Financial institutions rely on random number generators for various applications, including secure encryption, authentication processes, and transaction verification. As the frequency and sophistication of cyber-attacks increase, the need for advanced security solutions becomes more critical. Random number generators provide an essential layer of security, ensuring that sensitive information remains protected against fraudulent activities and unauthorized access.

Technological advancements, particularly in quantum computing, are another crucial driver of market growth. The development of quantum random number generators (QRNGs) promises unprecedented levels of randomness and security, making them highly attractive for use in critical applications such as cryptography, research simulations, and secure communications. These cutting-edge technologies are expected to revolutionize the random number generation landscape, paving the way for more reliable and tamper-proof systems across various industries.

When examining the regional outlook, North America is poised to dominate the card random number generator market, owing to its strong presence of leading technology companies and robust online gaming industry. The region's advanced technological infrastructure and high adoption rate of digital solutions further contribute to its market leadership. Asia Pacific is anticipated to showcase significant growth during the forecast period, driven by the expanding online gaming market, rising internet penetration, and increasing investments in cybersecurity. Europe is also expected to experience steady growth, supported by stringent regulatory requirements for data protection and secure digital transactions.

Type Analysis

The card random number generator market can be segmented by type into hardware random number generators (RNGs) and software RNGs. Hardware RNGs generate random numbers based on physical processes, such as electronic noise, which are inherently unpredictable. This type of RNG is favored for applications requiring high levels of security and integrity, such as cryptographic applications and secure communications. The increasing recognition of hardware RNGs' superior security features is driving their adoption in sectors like finance, where data protection is paramount.

Software RNGs, on the other hand, use algorithms to produce random numbers. While generally easier to implement and more cost-effective than hardware RNGs, software RNGs can be less secure due to their deterministic nature—they can potentially be predicted if the algorithm or seed value is compromised. Despite this, software RNGs are widely used in applications where high security is not as critical, such as gaming and lotteries. Their flexibility and ease of integration make them a popular choice for online gaming platforms and simulation applications.

The competition between hardware and software RNGs in the market is intense, as each type has its distinct advantages and applications. Innovations in both categories are continuously emerging, with hardware RNGs incorporating quantum technology to enhance randomness and security, while software RNGs are improving their algorithms to reduce

In the quantum Monte Carlo (QMC) method, the pseudo-random number generator (PRNG) plays a crucial role in determining the computation time. However, the hidden structure of the PRNG may lead to serious issues such as the breakdown of the Markov process. Here, we systematically analyze the performance of different PRNGs on the widely used QMC method known as the stochastic series expansion (SSE) algorithm. To quantitatively compare them, we intro- duce a quantity called QMC efficiency that can effectively reflect the efficiency of the algorithms. After testing several representative observables of the Heisenberg model in one and two dimensions, we recommend the linear congruential generator as the best choice of PRNG. Our work not only helps improve the performance of the SSE method but also sheds light on the other Markov-chain-based numerical algorithms.

The high-speed random number generator (HS RNG) chip market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, currently estimated at $500 million in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1.8 billion by 2033. Key application drivers include advancements in network security, where HS RNG chips are crucial for securing data transmission and encryption; the burgeoning field of scientific computing, particularly in simulations and high-performance computing; and the rapidly expanding gaming industry, demanding improved randomness for realistic game experiences. Financial terminals also leverage HS RNG chips for enhanced security and reliable transaction processing. The market is segmented by data rate (below 20 Mbps, 20-50 Mbps, 50-100 Mbps, and above 100 Mbps), with the higher data rate segments showing faster growth due to the increasing need for high-throughput cryptographic applications. Geographic growth is expected to be robust across North America and Asia Pacific regions, fuelled by significant investments in technological infrastructure and the presence of key players in these regions. However, challenges remain, including the high cost of advanced HS RNG chips and potential supply chain constraints. Despite these restraints, ongoing technological advancements and the increasing emphasis on data security across industries are expected to fuel market expansion. The development of more efficient and cost-effective HS RNG chips will be key in expanding market penetration to a wider range of applications. Competition within the market is intense, with established players such as ID Quantique, Qrange, and QuantumCTek competing with emerging companies. Strategic partnerships and mergers and acquisitions are anticipated to play a significant role in shaping the market landscape. Furthermore, government regulations mandating stronger cybersecurity measures in various sectors will act as a positive catalyst for market growth. The focus will increasingly shift towards chips that offer enhanced performance, improved energy efficiency, and better integration with existing security infrastructure.

Code and experiment results for a synthetic knowledge graph generator. The generator receives a set of rules, with an expected body support and support, and returns a knowledge graph that approximately matches the rules according to the body support and confidence. This code was developed during the Bachelor thesis by Gabriel Glaser, Generating Random Knowledge Graphs from Rules, University of Stuttgart, 2024. doi:10.18419/opus-15467.

Overview This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session. Database The database includes 1 recording of 72 seconds of synchronous PPG and ECG signals sampled at 250 Hz using a Medicom device, ABP-10 module (Medicom MTD Ltd., Russia). It was collected from a healthy subject during an induced relaxation by guided autogenic relaxation. For more information about the data collection, please refer to the following publication: https://pubmed.ncbi.nlm.nih.gov/30094756/ In addition, PPG signals corrupted by the noise generator at different levels are also included in the database. Realistic noise generator Motion Artifacts in PPG signals generally appear in the form of sudden spikes (in correspondence to the subject's movement) and slowly varying offsets (baseline wander) due to the changes in distance between the skin and the sensor after every sudden movement. For this reason, conventional noise generators — using random noise drawn from different distributions such as Gaussian or Poissonian — do not allow to properly evaluate the algorithm's performance, as they can only provide unrealistic noises compared to the one commonly found in PPG signals. To overcome this issue, we designed a more realistic synthetic noise generator that can simulate those two behaviors, enabling us to corrupt a reference signal with different noise levels. The details about noise generation are available in the reference paper. Data Files The reference PPG signal can be found in Datasets\GoodSignals\PPG and the simultaneously acquired ECG in Datasets\GoodSignals\ECG. The folder Datasets\NoisySignals contains 340 noisy PPG signals affected by different levels of noise. The names describe the intensity of the noise (evaluated in terms of the standard deviation of the random noise used as input for the noise generator, see reference paper). Five noisy signals are produced for every noise level by running the noise generator with five random seeds each (for noise generation). Name convention: ppg_stdx_y denotes the y-th noisy PPG signal produced using a noise with a standard deviation of x. Datasets\BPMs contains the ground truth for the heart-rate estimation computed in windows of 8s with an overlap of 2s. Code The folder Code contains the MATLAB scripts to generate the noisy files by generating the realistic noise with the function noiseGenerator. When referencing this material, please cite: Masinelli, G.; Dell'Agnola, F.; Valdés, A.A.; Atienza, D. SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices. Sensors 2021, 21, 2725. https://doi.org/10.3390/s21082725

Sample size calculation per Cochrane review group; random review # generator (used to help pick reviews at random)

We computed the largest integer that can return to 1, which is 6000000 bits long (currently known is 128 bits).We proposed an algorithm that can compute extremely large integer by using logical computation instead of numerical computation.We discovered that ratio of Collatz dynamics goes to 1 with the growth of starting integer.We evaluated that dynamics for sufficient large integers is random.We proposed a random bit sequence generator by only using logical computation (or gates).

AI Random Face Generator Market Outlook

The global AI random face generator market size was valued at approximately USD 200 million in 2023 and is projected to reach around USD 1.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 22.5% during the forecast period. The rapid advancements in AI and deep learning technologies coupled with a growing need for synthetic data for various applications are the driving factors behind this exponential market growth.

One of the primary growth factors for the AI random face generator market is the increasing demand for anonymized data in research and security. In a world where privacy concerns are escalating, the ability to generate realistic yet non-identifiable faces helps organizations maintain the integrity of their data while ensuring individual privacy. In sectors like healthcare and social sciences, the use of AI-generated faces enables researchers to conduct studies without compromising the privacy of actual subjects, thus broadening the scope and depth of their research capabilities.

Another significant driver is the media and entertainment industry's adoption of these technologies. From creating digital characters in movies and video games to generating avatars for social media and virtual reality experiences, the versatility and realism offered by AI random face generators are transforming creative processes. The capability to produce an infinite number of unique faces without the need for casting or hiring models significantly reduces costs and time, thus increasing the efficiency of production pipelines.

Additionally, the marketing and advertising sectors are leveraging AI-generated faces to create personalized and highly engaging promotional content. The ability to use diverse and unique faces allows marketers to better represent their target demographics, thereby increasing the relatability and effectiveness of their campaigns. The flexibility offered by AI random face generators in rapidly producing new visual content is proving invaluable in an industry that thrives on innovation and novelty.

The rise of Ai Image Upscaler technology is another fascinating development in the realm of AI applications. This technology enhances the resolution of images, making them clearer and more detailed without losing quality. In industries such as media and entertainment, Ai Image Upscaler is revolutionizing the way visual content is produced and consumed. By improving image quality, it allows filmmakers and game developers to create more immersive and visually appealing experiences. Moreover, in marketing and advertising, the ability to upscale images ensures that promotional materials maintain high quality across various platforms and devices, thus enhancing the overall impact of campaigns.

From a regional perspective, North America dominates the market due to its robust technological infrastructure and high adoption rates of AI across various industries. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by significant investments in AI research and development, coupled with the burgeoning digital economies in countries like China and India. These regional dynamics are indicative of a market that is not only expanding but also diversifying in its applications and reach.

Component Analysis

The AI random face generator market can be segmented by component into software, hardware, and services. Software is the most dominant segment due to the intrinsic role it plays in the generation process. Advanced algorithms, neural networks, and deep learning frameworks form the backbone of these solutions, enabling the creation of highly realistic faces. Continuous innovations in software capabilities are pivotal, with companies investing heavily in research to enhance the realism, efficiency, and versatility of these tools.

The hardware segment is also crucial, as the complex computations required for generating realistic faces demand robust processing power. High-performance GPUs and specialized AI processors are essential for facilitating the real-time generation of faces, especially in applications like video games and virtual reality. The hardware segment is expected to grow steadily, driven by the increasing demand for sophisticated computatio

The Hardware Random Number Generator (HRNG) market is witnessing significant growth, driven by the increasing demand for secure data transmission and storage in various industries, including finance, healthcare, and cybersecurity. Unlike software-based solutions, HRNGs utilize physical processes to generate randomne