Design and Evaluation of Booth-Encoded Multipliers in Redundant Binary Representation

2017 ◽  
pp. 113-147
Author(s):  
Yajuan He ◽  
Jiaxing Yang ◽  
Chip-Hong Chang
Keyword(s):  
Binary Representation

scholarly journals MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0178090 ◽  
Author(s):  
Mingzhe Su ◽  
Yan Ma ◽  
Xiangfen Zhang ◽  
Yan Wang ◽  
Yuping Zhang
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Binary Representation ◽  
Invariant Feature

Efficient Conversion Technique from Redundant Binary to NonRedundant Binary Representation

2017 ◽  
Vol 26 (09) ◽  
pp. 1750135 ◽  
Author(s):  
Ranjan Kumar Barik ◽  
Manoranjan Pradhan ◽  
Rutuparna Panda
Keyword(s):  
Final Stage ◽  
Hardware Description Language ◽  
Binary Representation ◽  
Description Language ◽  
Power Product ◽  
Efficient Conversion ◽  
Look Ahead ◽  
Hardware Description ◽  
Sign Extension ◽  
Cadence Tool

Redundant Binary (RB) to Two’s Complement (TC) converter offers nonredundant representation. However, the sign bit of TC representation has to be handled using nonstandard hardware blocks. The concept of Inverted encoding of negative weighted bits (IEN) eliminates the need of sign extension and offers design only using predefined hardware blocks. NonRedundant Binary (NRB) representation refers to both conventional and IEN representations. The NRB representation is also useful considering problem related to shifting in Carry Save (CS) representation of a RB number. In this paper, we have proposed two new conversion circuits for RB to NRB representation. The proposed circuits of the RB to NRB converter are coded in Verilog Hardware Description language (HDL) and synthesized using the Encounter(R) RTL Compiler RC13.10 v13.10-s006_1 of Cadence tool considering ASIC platform. Considering 64 bits’ operand, the delay power product performances of proposed one-bit and two-bit computations offer improvement of almost 29.9% and 47%, respectively as compared to Carry-Look-Ahead (CLA). The proposed one-bit converter is also applied in the final stage of the Modified Redundant Binary Adder (MRBA). The 32-bit MRBA offers a delay improvement of 7.87% replacing conventional converter with proposed one-bit converter in same FPGA 4vfx12sf363-12 device.

scholarly journals Using machine learning and big data to explore the drug resistance landscape in HIV

2021 ◽  
Author(s):  
Luc Blassel ◽  
Anna Tostevin ◽  
Christian Julian Villabona-Arenas ◽  
Martine Peeters ◽  
Stephane Hue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Prediction Accuracy ◽  
Sequence Data ◽  
Transcriptase Inhibitor ◽  
Binary Representation ◽  
Resistance Mutations ◽  
Genetic Barrier ◽  
The Uk

Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55; 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs. When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, we did not find any significant signal of epistasis, beyond the standard resistance scheme associating major DRMs to auxiliary mutations.

scholarly journals On the 2-adic order of Stirling numbers of the second kind and their differences

2009 ◽  
Vol DMTCS Proceedings vol. AK,... (Proceedings) ◽  
Author(s):  
Tamás Lengyel
Keyword(s):  
Positive Integer ◽  
Stirling Numbers ◽  
Bell Polynomials ◽  
Binary Representation ◽  
Exact Order ◽  
Special Cases ◽  
International Audience ◽  
The Difference ◽  
Divisibility Properties ◽  
Positive Integers

International audience Let $n$ and $k$ be positive integers, $d(k)$ and $\nu_2(k)$ denote the number of ones in the binary representation of $k$ and the highest power of two dividing $k$, respectively. De Wannemacker recently proved for the Stirling numbers of the second kind that $\nu_2(S(2^n,k))=d(k)-1, 1\leq k \leq 2^n$. Here we prove that $\nu_2(S(c2^n,k))=d(k)-1, 1\leq k \leq 2^n$, for any positive integer $c$. We improve and extend this statement in some special cases. For the difference, we obtain lower bounds on $\nu_2(S(c2^{n+1}+u,k)-S(c2^n+u,k))$ for any nonnegative integer $u$, make a conjecture on the exact order and, for $u=0$, prove part of it when $k \leq 6$, or $k \geq 5$ and $d(k) \leq 2$. The proofs rely on congruential identities for power series and polynomials related to the Stirling numbers and Bell polynomials, and some divisibility properties.

Minimal Size of Counters for (Real-Time) Multicounter Automata

2021 ◽  
Vol 181 (2-3) ◽  
pp. 99-127
Author(s):  
Viliam Geffert ◽  
Zuzana Bednárová
Keyword(s):  
Finite Number ◽  
Real Time ◽  
The Other ◽  
Binary Representation ◽  
Minimal Size ◽  
Nondeterministic Automaton ◽  
Other Hand ◽  
Weak Space ◽  
Fixed Constant

We show that, for automata using a finite number of counters, the minimal space that is required for accepting a nonregular language is (log n)ɛ. This is required for weak space bounds on the size of their counters, for real-time and one-way, and for nondeterministic and alternating versions of these automata. The same holds for two-way automata, independent of whether they work with strong or weak space bounds, and of whether they are deterministic, nondeterministic, or alternating. (Here ɛ denotes an arbitrarily small—but fixed—constant; the “space” refers to the values stored in the counters, rather than to the lengths of their binary representation.) On the other hand, we show that the minimal space required for accepting a nonregular language is nɛ for multicounter automata with strong space bounds, both for real-time and one-way versions, independent of whether they are deterministic, nondeterministic, or alternating, and also for real-time and one-way deterministic multicounter automata with weak space bounds. All these bounds are optimal both for unary and general nonregular languages. However, for automata equipped with only one counter, it was known that one-way nondeterministic automata cannot recognize any unary nonregular languages at all, even if the size of the counter is not restricted, while, with weak space bound log n, we present a real-time nondeterministic automaton recognizing a binary nonregular language here.

Supporting Binding-Sites Discovery via Iterative Database Processing

2013 ◽  
Vol 2 (3) ◽  
pp. 19-41
Author(s):  
Ran Tel-Nir ◽  
Roy Gelbard ◽  
Israel Spiegler
Keyword(s):  
Information Extraction ◽  
Binding Site ◽  
Iterative Algorithm ◽  
Spatial Data ◽  
Binding Sites ◽  
Heuristic Algorithms ◽  
Protein Structures ◽  
Three Dimensional ◽  
Extraction Process ◽  
Binary Representation

Recognition of binding sites common to a set of protein structures is important for applications such as drug design. Common methods of binding-sites are based on heuristic algorithms that use summarized spatial data and superimposition techniques. However, computational operations generally do not store intermediate data for further calculation and information extraction. The current study presents an alternative approach to binding calculation by introducing a binary representation scheme for three dimensional molecule data and a fast iterative algorithm which obviates the need to calculate and resolve spatial transformations in the binding site extraction process. This is achieved by using relational database indexing methods and an efficient iterative model. This general-purpose iterative algorithm was tested for binding small molecules. The results show that the method can be applied efficiently for binding site extraction, and bio-information extraction. This binary representation improves performance by reducing processing time by 31% compared to typical representations.

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