Fractional design leadership for mobile apps and web platforms. Embedded in your team, guiding strategy and execution from concept to launch.

Simulation code for "Comments on ''Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization''". https://arxiv.org/pdf/1802.09252.pdf

Simulation of "Waveform design for communicating radar systems using Fractional Fourier Transform" paper

In the case of static PV modeling (single, double, and three diode models), the load variation and switching operation of the inverter and DC/DC converter stages are not considered. Therefore, another type of PV model named integer order dynamic PV model (IOM) has been introduced, which is the most efficient and accurate model to handle the static models' aforementioned drawbacks. That is why the dynamic model is the preferable one for the design of the grid-connected PV systems. Recently, the theory of fractional calculus has been employed to reinforce the efficiency and flexibility of IOM. As a result, the fractional-order dynamic PV model (FOM) has been introduced as the latest trend in tackling the PV models' dynamic behavior. The accuracy of the dynamic PV models is mainly influenced by obtaining their parameters under different operating conditions. The manufacturers usually undefine the models’ parameters. Therefore, it is crucial to identify these parameters accurately with minimum execution time using the experimental load current–time (I-T) curve [1]-[2]-[3]. [1]AbdelAty AM, Radwan AG, Elwakil AS, Psychalinos C. Transient and steady-state response of a fractional-order dynamic PV model under different loads. J Circ Syst Comput 2018;27(02):1850023. https://doi.org/10.1142/s0218126618500238 [2] Yousri, D., Allam, D., Eteiba, M.B. and Suganthan, P.N., 2019. Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants. Energy conversion and management, 182, pp.546-563. [3] Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters March 2021 Energy Conversion and Management ( In proofing). Note: To implement the code for optimizing the fractional order model. The user should click on fomcon-1.21b right click and select add to the path ( then select folders and subfolders) to let all the inside files are readable. Then use main to implement the optimization process

StereoSanction MultiFX - VST3 Multi-effect based on fractional delay line algorithm. It can achieve popular audio effects such as delay, vibrato, chorus, flanger, ring modulator, tremolo, overdrive, distortion and fuzz. GUI designed to be welcoming for every audio enthousiast, without the clutter of modern apps serving similar purpose.

This repository contains all Scilab programs required to design a Fractional Order PID controller.

A Python CLI implementation of the factorial design that uses split plots (fractional). It implements both the 2-level full factorial design, and the 2-level fractional factorial design of experiments.

ERC404 is an experimental, mixed ERC20 / ERC721 implementation with native liquidity and fractionalization. While these two standards are not designed to be mixed, this implementation strives to do so in as robust a manner as possible while minimizing tradeoffs.

Optimal Experimental Design for Fractional Polynomials models

Repository designed to showcase the results of the paper "Fractional Fourier Sound Synthesis" presented at the ICMC 2025 in Boston, USA.

Sensei Seek: Fractional Executive Marketplace Sensei Seek is a sophisticated marketplace platform designed to connect high-growth startups with elite, experienced executives for fractional, interim, or advisory roles. It bridges the gap between innovative companies needing strategic guidance and seasoned leaders seeking flexible, high-impact work.

Covered: • Introduction to algorithmic thinking and design • Analysis of algorithms (proof techniques, asymptotic notation) • Divide & conquer: recursive design techniques and solving recurrences • Greedy algorithms: Coin changing, Interval scheduling, Fractional knapsack, Huffman codes • Dynamic programming: Top-down approach, Bottom-up enumeration, Optimal substructure, Optimal coin changing, Minimum cost path in grid, Multi-stage graphs, Unbounded knapsack, 0/1 knapsack, Edit distance, Longest common subsequence, Dynamic time warping • Computational geometry: Convex hulls (properties, Gift-wrap algorithm, Graham-scan algorithm), Plane-sweep algorithms (closest pair, line intersections), Range search methods (kD trees, Quadtrees) • Graphs: Topological ordering, Minimum spanning trees, Single-source and All-pair shortest paths • Backtracking: combinatorial search and generation • String matching: Rabin-Karp algorithm, Knuth-Morris-Pratt algorithm, Boyer-Moore algorithm.

How to share data with a statistician This is a guide for anyone who needs to share data with a statistician or data scientist. The target audiences I have in mind are: Collaborators who need statisticians or data scientists to analyze data for them Students or postdocs in various disciplines looking for consulting advice Junior statistics students whose job it is to collate/clean/wrangle data sets The goals of this guide are to provide some instruction on the best way to share data to avoid the most common pitfalls and sources of delay in the transition from data collection to data analysis. The Leek group works with a large number of collaborators and the number one source of variation in the speed to results is the status of the data when they arrive at the Leek group. Based on my conversations with other statisticians this is true nearly universally. My strong feeling is that statisticians should be able to handle the data in whatever state they arrive. It is important to see the raw data, understand the steps in the processing pipeline, and be able to incorporate hidden sources of variability in one's data analysis. On the other hand, for many data types, the processing steps are well documented and standardized. So the work of converting the data from raw form to directly analyzable form can be performed before calling on a statistician. This can dramatically speed the turnaround time, since the statistician doesn't have to work through all the pre-processing steps first. What you should deliver to the statistician To facilitate the most efficient and timely analysis this is the information you should pass to a statistician: The raw data. A tidy data set A code book describing each variable and its values in the tidy data set. An explicit and exact recipe you used to go from 1 -> 2,3 Let's look at each part of the data package you will transfer. The raw data It is critical that you include the rawest form of the data that you have access to. This ensures that data provenance can be maintained throughout the workflow. Here are some examples of the raw form of data: The strange binary file your measurement machine spits out The unformatted Excel file with 10 worksheets the company you contracted with sent you The complicated JSON data you got from scraping the Twitter API The hand-entered numbers you collected looking through a microscope You know the raw data are in the right format if you: Ran no software on the data Did not modify any of the data values You did not remove any data from the data set You did not summarize the data in any way If you made any modifications of the raw data it is not the raw form of the data. Reporting modified data as raw data is a very common way to slow down the analysis process, since the analyst will often have to do a forensic study of your data to figure out why the raw data looks weird. (Also imagine what would happen if new data arrived?) The tidy data set The general principles of tidy data are laid out by Hadley Wickham in this paper and this video. While both the paper and the video describe tidy data using R, the principles are more generally applicable: Each variable you measure should be in one column Each different observation of that variable should be in a different row There should be one table for each "kind" of variable If you have multiple tables, they should include a column in the table that allows them to be joined or merged While these are the hard and fast rules, there are a number of other things that will make your data set much easier to handle. First is to include a row at the top of each data table/spreadsheet that contains full row names. So if you measured age at diagnosis for patients, you would head that column with the name AgeAtDiagnosis instead of something like ADx or another abbreviation that may be hard for another person to understand. Here is an example of how this would work from genomics. Suppose that for 20 people you have collected gene expression measurements with RNA-sequencing. You have also collected demographic and clinical information about the patients including their age, treatment, and diagnosis. You would have one table/spreadsheet that contains the clinical/demographic information. It would have four columns (patient id, age, treatment, diagnosis) and 21 rows (a row with variable names, then one row for every patient). You would also have one spreadsheet for the summarized genomic data. Usually this type of data is summarized at the level of the number of counts per exon. Suppose you have 100,000 exons, then you would have a table/spreadsheet that had 21 rows (a row for gene names, and one row for each patient) and 100,001 columns (one row for patient ids and one row for each data type). If you are sharing your data with the collaborator in Excel, the tidy data should be in one Excel file per table. They should not have multiple worksheets, no macros should be applied to the data, and no columns/cells should be highlighted. Alternatively share the data in a CSV or TAB-delimited text file. (Beware however that reading CSV files into Excel can sometimes lead to non-reproducible handling of date and time variables.) The code book For almost any data set, the measurements you calculate will need to be described in more detail than you can or should sneak into the spreadsheet. The code book contains this information. At minimum it should contain: Information about the variables (including units!) in the data set not contained in the tidy data Information about the summary choices you made Information about the experimental study design you used In our genomics example, the analyst would want to know what the unit of measurement for each clinical/demographic variable is (age in years, treatment by name/dose, level of diagnosis and how heterogeneous). They would also want to know how you picked the exons you used for summarizing the genomic data (UCSC/Ensembl, etc.). They would also want to know any other information about how you did the data collection/study design. For example, are these the first 20 patients that walked into the clinic? Are they 20 highly selected patients by some characteristic like age? Are they randomized to treatments? A common format for this document is a Word file. There should be a section called "Study design" that has a thorough description of how you collected the data. There is a section called "Code book" that describes each variable and its units. How to code variables When you put variables into a spreadsheet there are several main categories you will run into depending on their data type: Continuous Ordinal Categorical Missing Censored Continuous variables are anything measured on a quantitative scale that could be any fractional number. An example would be something like weight measured in kg. Ordinal data are data that have a fixed, small (< 100) number of levels but are ordered. This could be for example survey responses where the choices are: poor, fair, good. Categorical data are data where there are multiple categories, but they aren't ordered. One example would be sex: male or female. This coding is attractive because it is self-documenting. Missing data are data that are unobserved and you don't know the mechanism. You should code missing values as NA. Censored data are data where you know the missingness mechanism on some level. Common examples are a measurement being below a detection limit or a patient being lost to follow-up. They should also be coded as NA when you don't have the data. But you should also add a new column to your tidy data called, "VariableNameCensored" which should have values of TRUE if censored and FALSE if not. In the code book you should explain why those values are missing. It is absolutely critical to report to the analyst if there is a reason you know about that some of the data are missing. You should also not impute/make up/ throw away missing observations. In general, try to avoid coding categorical or ordinal variables as numbers. When you enter the value for sex in the tidy data, it should be "male" or "female". The ordinal values in the data set should be "poor", "fair", and "good" not 1, 2 ,3. This will avoid potential mixups about which direction effects go and will help identify coding errors. Always encode every piece of information about your observations using text. For example, if you are storing data in Excel and use a form of colored text or cell background formatting to indicate information about an observation ("red variable entries were observed in experiment 1.") then this information will not be exported (and will be lost!) when the data is exported as raw text. Every piece of data should be encoded as actual text that can be exported. The instruction list/script You may have heard this before, but reproducibility is a big deal in computational science. That means, when you submit your paper, the reviewers and the rest of the world should be able to exactly replicate the analyses from raw data all the way to final results. If you are trying to be efficient, you will likely perform some summarization/data analysis steps before the data can be considered tidy. The ideal thing for you to do when performing summarization is to create a computer script (in R, Python, or something else) that takes the raw data as input and produces the tidy data you are sharing as output. You can try running your script a couple of times and see if the code produces the same output. In many cases, the person who collected the data has incentive to make it tidy for a statistician to speed the process of collaboration. They may not know how to code in a scripting language. In that case, what you should provide the statistician is something called pseudocode. It should look something like: Step 1 - take the raw file, run version 3.1.2 of summarize software with parameters a=1, b=2, c=3 Step 2 - run the software separately for each sample Step 3 - take column three of outputfile.out for each sample and that is the corresponding row in the output data set You should also include information about which system (Mac/Windows/Linux) you used the software on and whether you tried it more than once to confirm it gave the same results. Ideally, you will run this by a fellow student/labmate to confirm that they can obtain the same output file you did. What you should expect from the analyst When you turn over a properly tidied data set it dramatically decreases the workload on the statistician. So hopefully they will get back to you much sooner. But most careful statisticians will check your recipe, ask questions about steps you performed, and try to confirm that they can obtain the same tidy data that you did with, at minimum, spot checks. You should then expect from the statistician: An analysis script that performs each of the analyses (not just instructions) The exact computer code they used to run the analysis All output files/figures they generated. This is the information you will use in the supplement to establish reproducibility and precision of your results. Each of the steps in the analysis should be clearly explained and you should ask questions when you don't understand what the analyst did. It is the responsibility of both the statistician and the scientist to understand the statistical analysis. You may not be able to perform the exact analyses without the statistician's code, but you should be able to explain why the statistician performed each step to a labmate/your principal investigator. Contributors Jeff Leek - Wrote the initial version. L. Collado-Torres - Fixed typos, added links. Nick Reich - Added tips on storing data as text. Nick Horton - Minor wording suggestions.

CMOS Design of a Fractional-N Phase Lcoked Loop in Cadence Virtuoso

Blender3D addon to help with woodworking design showing fractional units and object and element dimensions

Fractional Snow/Non-Snow Cover Mapping through Incorporation of Thermal Band in Snow Index Design

volutionary Strategy for fractional knapsack using MATLAB (orchestration) + SystemC (genetic operators); reproducible co-design with software-only baseline.

This repository contains smart contracts related to a Fractional NFT use case for real estate. It likely includes Solidity smart contracts designed to represent and manage fractional ownership of real estate properties using non-fungible tokens (NFTs).

A novel approach for the design of appropriate control policies and valid neural Lyapunov functions for Caputo-type nonlinear fractional-order systems.

Hyperparameter tuning of a Convolutional Neural Network (CNN) for CIFAR-10 image classification using fractional factorial Design of Experiments (DOE) and regression modeling.

Given competing hypotheses, partial evidence, safety constraints, and a run budget, computes the minimal combinatorial design (covering arrays, Latin squares, BIBDs, fractional factorials) that maximizes hypothesis discrimination.

This Java application is designed to parse and evaluate mathematical expressions. It utilizes a Lexer to tokenize input, a Parser to construct an abstract syntax tree (AST), and evaluates these expressions to return results in fractional form.

Fractional RWA Bonds is a blockchain-powered prototype designed to democratize access to low-risk government bond investments. By tokenizing bonds into small, tradable units, the platform allows users to invest using stablecoins, visualize yields, and receive automated interest payouts through smart contracts.

Flagship restoration framework fusing Meta-Learning with Fractional Calculus priors. Features a lightweight CNN for adaptive parameter prediction, Numba-accelerated iterative solver, and hybrid Quality/Speed execution modes. Designed for extreme noise conditions in scientific imaging (Thermal/Astro/Microscopy). GPL-3.0 licensed.

RealEquity is a mobile real estate investment app that lets users purchase, manage, and sell fractional property assets. It provides a streamlined portfolio dashboard, real-time performance tracking, and an intuitive, Wealthsimple-style interface designed to make property investing simple and accessible.

I’m Arindam Kar — a Fractional CTO with 23+ years in enterprise tech across India, UAE & Singapore. I design AI-first systems, modernize legacy infra, and deliver results for startups & institutions. From voter sentiment engines to private LLM stacks — I build what others only pitch.

Design and implementation of a multistage Digital Front End (DFE) for radio/ADC preprocessing: fractional polyphase decimator (9 MHz → 6 MHz), dual 2nd-order IIR notch filters (2.4 MHz and 5 MHz), and a configurable CIC decimator chain (decimation factors 1,2,4,8,16).

Vestify is a Go-based REST API that enables users to earn fractional shares of Indian stocks (NSE/BSE) as rewards for actions like referrals or onboarding. The system is designed with financial correctness and scalability in mind, using double-entry bookkeeping, idempotent APIs, and high-precision INR calculations.

SmartHousing is an innovative real estate tokenization platform designed to address Nigeria's significant housing deficit by leveraging blockchain technology. Our solution enables fractional ownership of real estate properties through the use of Real World Asset Tokenization, making it easier for low-income earners to invest in and own real estate.

The designed program can be employed to calculate the Indicated Power, Brake power, Fractional Power and the Mechanical Efficiency of an IC Engine by using the inputs provided by the user. By calculating the Mechanical Efficiency of different engines and comparing them, a proper, efficient and feasible number of cylinders, mean effective power, length of stroke, area of cylinder, diameter of the cylinder bore and Crank speed can be calculated. This program can be effectively used to determine the engine specifications before designing and can also calculate the Indicated Power, Brake power, Fractional Power and the Mechanical Efficiency of an existing IC Engine.