scholarly journals Reduced Complexity Detection Schemes for Golden Code Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 139140-139149
Author(s):  
Hongjun Xu ◽  
Narushan Pillay
Keyword(s):  
Reduced Complexity ◽  
Golden Code ◽  
Detection Schemes

Medicinal Chemistry Database GDBMedChem

2019 ◽  
Author(s):  
Mahendra Awale ◽  
Finton Sirockin ◽  
Nikolaus Stiefl ◽  
Jean-Louis Reymond
Keyword(s):  
Small Molecules ◽  
Natural Product ◽  
Medicinal Chemistry ◽  
3D Visualization ◽  
Molecular Size ◽  
Similarity Searching ◽  
Complex Molecules ◽  
Synthetic Accessibility ◽  
Simple Chemical ◽  
Reduced Complexity

<div>The generated database GDB17 enumerates 166.4 billion possible molecules up to 17 atoms of C, N, O, S and halogens following simple chemical stability and synthetic feasibility rules, however medicinal chemistry criteria are not taken into account. Here we applied rules inspired by medicinal chemistry to exclude problematic functional groups and complex molecules from GDB17, and sampled the resulting subset evenly across molecular size, stereochemistry and polarity to form GDBMedChem as a compact collection of 10 million small molecules.</div><div><br></div><div>This collection has reduced complexity and better synthetic accessibility than the entire GDB17 but retains higher sp 3 - carbon fraction and natural product likeness scores compared to known drugs. GDBMedChem molecules are more diverse and very different from known molecules in terms of substructures and represent an unprecedented source of diversity for drug design. GDBMedChem is available for 3D-visualization, similarity searching and for download at http://gdb.unibe.ch.</div>

DESIGN FLOW OF CONCURRENT ERROR DETECTION SCHEMES FOR COMBINATIONAL CIRCUITS

2018 ◽  
Vol 65 ◽  
pp. 92-98
Author(s):  
A. L. Stempkovskiy ◽  
◽  
D. V. Telpukhov ◽  
A. I. Demeneva ◽  
T. D. Zhukova ◽  
...  
Keyword(s):  
Error Detection ◽  
Design Flow ◽  
Concurrent Error Detection ◽  
Combinational Circuits ◽  
Detection Schemes

ARFIS: Adaptive-Receiver-Based Fuzzy Inference System for Diffusion- Based Molecular Communications

2020 ◽  
Vol 16 (2) ◽  
pp. 280-289
Author(s):  
Ghalib H. Alshammri ◽  
Walid K. M. Ahmed ◽  
Victor B. Lawrence
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection Threshold ◽  
Learning Rule ◽  
Optimal Number ◽  
Molecular Communication ◽  
Inference System ◽  
Detection Scheme ◽  
Adaptive Receiver ◽  
Detection Schemes

Background: The architecture and sequential learning rule-based underlying ARFIS (adaptive-receiver-based fuzzy inference system) are proposed to estimate and predict the adaptive threshold-based detection scheme for diffusion-based molecular communication (DMC). Method: The proposed system forwards an estimate of the received bits based on the current molecular cumulative concentration, which is derived using sequential training-based principle with weight and bias and an input-output mapping based on both human knowledge in the form of fuzzy IFTHEN rules. The ARFIS architecture is employed to model nonlinear molecular communication to predict the received bits over time series. Result: This procedure is suitable for binary On-OFF-Keying (Book signaling), where the receiver bio-nanomachine (Rx Bio-NM) adapts the 1/0-bit detection threshold based on all previous received molecular cumulative concentrations to alleviate the inter-symbol interference (ISI) problem and reception noise. Conclusion: Theoretical and simulation results show the improvement in diffusion-based molecular throughput and the optimal number of molecules in transmission. Furthermore, the performance evaluation in various noisy channel sources shows promising improvement in the un-coded bit error rate (BER) compared with other threshold-based detection schemes in the literature.

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