daniel009988
From traditional software, over high-performance custom FPGA to to cutting-edge physics & bio inspired solutions
Q.Solutions
daniel009988's Stars
dynexcoin/Dynex
Dynex is the world’s only accessible neuromorphic quantum computing cloud for solving real-world problems, at scale.
dynexcoin/Dynex-Neuromorphic-Chip
Dynex has also developed a proprietary circuit design, the Dynex Neuromorphic Chip, that complements the Dynex ecosystem and turns any modern G into a neuromorphic computing chip by simulating its equations of motion. This implementation proofs the mathematical model.
dynexcoin/Dynex-Whitepaper
With the end of Moore’s law approaching and Dennard scaling ending, the computing community is increasingly looking at new technologies to enable continued performance improvements. A neuromorphic computer is a nonvon Neumann computer whose structure and function are inspired by biology and physics. Today, such systems can be built and operated using existing technology, even at scale, and are capable of outperforming current quantum computers.
daniel009988/AI-Analyst
Open Source implementation of an online analyst tool to research companies with competence in the area of Artificial Intelligence. Currently contains 11,469 public AI stocks, AI-ETF's and PE/VC backed AI companies with a focus on analytics of the core strength around AI technology. The system retrieves and collates data from multiple online sources and provides a comprehensive overview about the skills, expertise, finantial health, fitness and other parameters. Features include a compilation of all public listed companies which are specialised in or create real product value with Artificial Intelligence, a comprehensive compilation of private companies in the area of Artificial Intelligence and an overview of available Exchange Traded Funds (ETFs) with an investment focus in Artificial Intelligence companies. Funding rounds from major Venture Capital and Private Equity funds are matched with private companies in the database.
daniel009988/AI-Academy
Open Source implementation of an online academy to introduce Artificial Intelligence to the public. The online certificate program "Introduction to Artificial Intelligence" is free and will take you approximately 60 hours (6 weeks) to complete. It will shed light on various aspects of AI, including definitions, neural networks, machine learning and societal implications. No programming, but some basic math is required. Some exercises also require you to draw with a pen and paper to find the answer to the question. You will receive a certificate after successfuly completing the course.
namdi/JFNK-integrator
This contains code for a group of numerical integrators for time-dependent differential equations. They include the backward (implicit) Euler method, Spectral Deferred Corrections (SDC) method, and a Jacobian-Free Newton Krylov (JFNK) method designed to accelerate the convergence of SDC for stiff systems.
RGNC/pcudasat
GPU simulator of a family of recognizer P systems with active membranes solving SAT in linear time
maxtuno/HESS-SAT-COMPLETE
The original HESS (Hyper Exponential Space Sorting) is a polynomial black-box optimization algorithm, that work very well with any NP-Complete, or NP-Hard problem, at 2021 thanks to suggestions of Daniel Mattes, work like a complete algorithm.
maxtuno/HESS-SHA256
A sha256 sequential inverter with HESS algorithm.
daniel009988/AES-FPGA-Core
Verilog implementation of the symmetric block cipher AES (Advanced Encryption Standard) as specified in NIST FIPS 197. This implementation supports 128 and 256 bit keys.
daniel009988/HESS-HAMPATH-COMPLETE
The original HESS (Hyper Exponential Space Sorting) is a polynomial black-box optimization algorithm, that work very well with any NP-Complete, or NP-Hard problem, at 2021 thanks to suggestions of Daniel Mattes, work like a complete algorithm.
daniel009988/HESS-SAT-COMPLETE
The original HESS (Hyper Exponential Space Sorting) is a polynomial black-box optimization algorithm, that work very well with any NP-Complete, or NP-Hard problem, at 2021 thanks to suggestions of Daniel Mattes, work like a complete algorithm.
daniel009988/HESS-SHA256
A sha256 sequential inverter with HESS algorithm.
daniel009988/UPSimulator
A univeral P system simulator for membrane computing researchers.
dmeoli/AlphaZeroSAT
TensorFlow implementation of "From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero"
evelyn6666/Comparison_between_two_ways_to_solve_MIN_VERTEX_COVER
Vertex cover of a graph is a set of vertexes such that each edges of the graph is incident to at least one vertex of the set. In this project, we use three different algorithms (CNF-SAT-VC, APPROXVC-1, and APPROX-VC-2) that solving the vertex cover problem. The algorithm CNF-SAT-VC create a polynomial reduction of the decision version of VERTEX COVER to CNF-SAT. Then we add other two additional ways to solve MIN-VERTEX-COVER, APPROX-VC-1 and APPROXVC-2 respectively. APPR1 just picks a vertex of highest degree and add it to the vertex cover, meanwhile, throw away all edges incident on that vertex and repeat till no edges remain. APPR2 is an algorithm which pick an edge randomly and delete all edges which attach to the previous picked one until no edges left.