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IMPROVED ULTRASONIC SENSITIVITY USING PSEUDO-RANDOM binary-code PHASE-MODULATED SIGNALS

ULTRASONIC IMAGING 1979年第3期1卷 255-264页

作者： CHAPELON, JY CATHIGNOL, D FOURCADE, C CLIN INST NATL SANTE & RECH MED CTR ETUD & TECHNOL APPL18 AVE DOYEN LEPINEF-69500 BRONFRANCE

Random signal systems are used in radars, but very seldom in medical and materials ultrasonic applications. Yet, compared with conventional techniques which use the same peak power, pseudo-random signal correlation techniques can easily improve the signal-to-noise ratio by a thousand or more times without sacrificing spatial resolution. To overcome the limitations imposed by the high ratio of peak power to average power, we developed a technique by which the echo energy is integrated with respect to time. As in radar systems, we used a sinusoidal signal which was phase-modulated by the pseudo-random binary code. This approach simplified our apparatus compared to those techniques using noise signals.

关键词： binary code high sensitivity pulse-echo pseudo-random noise ultrasonics

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Functionality Recognition on binary code with Neural Representation Learning 21

Functionality Recognition on Binary Code with Neural Represe...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Zhenzhou Tian Jinrui Li Peng Xue Jie Tian Hengchao Mao Yaqian Huang Xi'an University of Posts and Telecommunications School of Computer Science and Technology China

ISBN: (纸本)9781450384087

The functionality recognition of binary code has important application value in malware analysis, software forensics, binary code similarity analysis and other applications. Most of the existing methods are based on source code or machine learning strategies to carry out program similarity analysis, and this similarity analysis is also applied to a pair of programs, there are limitations in detection accuracy and quantity. Inspired by the recent great success of neural networks and representation learning in various program analysis tasks, We propose NPFI to analyze the binary code of the program and identify its functionality from the perspective of assembly instruction sequence. To evaluate the performance of NPFI, we built a large dataset consisting of 39,000 programs from six different categories collected from Google code Jam. A large number of experiments show that the accuracy of NPFI in binary code function recognition can reach 95.8%, which is much better than the existing methods.

关键词： binary code Neural network Program functionality identification

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Salable Image Search with Reliable binary code 14

Salable Image Search with Reliable Binary Code

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Proceedings of the 22nd ACM international conference on Multimedia

作者： Guangxin Ren Junjie Cai Shipeng Li Nenghai Yu Qi Tian University of Science and Technology of China Hefei China University of Texas at San Antonio San Antonio TX USA Microsoft Research Asia Beijing China

ISBN: (纸本)9781450330633

In many existing image retrieval algorithms, Bag-of-Words (BoW) model has been widely adopted for image representation. To achieve accurate indexing and efficient retrieval, local features such as the SIFT descriptor are extracted and quantized to visual words. One of the most popular quantization scheme is scalar quantization, which generates binary signature with an empirical threshold value. However, such binarization strategy inevitably suffers from the quantization loss induced by each quantized bit and impairs the effectiveness of search performance. In this paper, we investigate the reliability of each bit in scalar quantization and propose a novel reliable binary SIFT feature. We move one step ahead to incorporate the reliability in both index word expansion and feature similarity. Our proposed approach not only accelerates the search speed by narrowing search space, but also improves the retrieval accuracy by alleviating the impact of unreliable quantized bits. Experimental results demonstrate that the proposed approach achieves significant improvement in retrieval efficiency and accuracy.

关键词： binary code image search

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Numerical Analysis of All-Optical binary to Gray code Converter Using Silicon Microring Resonator

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OPTICAL MEMORY AND NEURAL NETWORKS 2024年第2期33卷 193-204页

作者： Hossain, Manjur Mondal, Kalimuddin Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai 602105 India

Present manuscript designs and analyzes numerically all-optical binary-to-gray code (BTGC) converter utilizing silicon microring resonator. A waveguide-based silicon microring resonator has been employed to achieve optical switching under low-power conditions using the two-photon absorption effect. Gray code (GC) is a binary numerical system in which two consecutive codes distinguished by only one bit. The GC is critical in optics communication because it prevents spurious output from optical switches and facilitates error correction in optical communications. MATLAB is used to design and analyze the architecture at almost 260 Gbps operational speed. The faster response times and compact design of the demonstrated circuits make them especially useful for optical communication systems. Performance indicating factors evaluated from MATLAB results and analyzed. Design parameters that are optimized have been chosen in order to construct the model practically.

关键词： microring resonator (MRR) all-optical switch binary code gray code code conversion

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Cross-Language binary-Source code Matching with Intermediate Representations 29

Cross-Language Binary-Source Code Matching with Intermediate...

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29th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Gui, Yi Wan, Yao Zhang, Hongyu Huang, Huifang Sui, Yulei Xu, Guandong Shao, Zhiyuan Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & TechnolCluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan Peoples R China Univ Newcastle Newcastle NSW Australia Huazhong Univ Sci & Technol Sch Math & Stat Wuhan Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781665437868

binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks.

关键词： Cross-language clone detection intermediate representation binary code code matching deep learning

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Supervised binary Hash code Learning With Jensen Shannon Divergence

Supervised Binary Hash Code Learning With Jensen Shannon Div...

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IEEE International Conference on Computer Vision (ICCV)

作者： Fan, Lixin Nokia Res Ctr Cambridge England

ISBN: (纸本)9781479928392

This paper proposes to learn binary hash codes within a statistical learning framework, in which an upper bound of the probability of Bayes decision errors is derived for different forms of hash functions and a rigorous proof of the convergence of the upper bound is presented. Consequently, minimizing such an upper bound leads to consistent performance improvements of existing hash code learning algorithms, regardless of whether original algorithms are unsupervised or supervised. This paper also illustrates a fast hash coding method that exploits simple binary tests to achieve orders of magnitude improvement in coding speed as compared to projection based methods.

关键词： Jensen Shannon Divergence approximate nearest neighbor search binary code indexing matching randomized tree hash code binary code Nearest neighbor query Upper bound Indexing binary Neuroretinitis performance boost Statistical learning

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Design and Simulation of Optical Ex-OR Gate and binary to Gray code Converter using Semiconductor Optical Amplifier

Design and Simulation of Optical Ex-OR Gate and Binary to Gr...

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International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES)

作者： Singh, Babita Singh, Rakesh Kumar Gangwar, Som Pal RR Singh Inst Management & Technol Dept Elect & Commun Engn Amethi 227405 UP India Kamla Nehru Inst Technol Dept Elect Engn Sultanpur 228118 UP India

ISBN: (纸本)9781509021185

The advanced signal processing in next generation optical networks demands all optical digital devices as the key element which provides the benefit in terms of power, speed and cost. In this paper, execution of optical binary to gray code converter with the aid of Ex-OR gate is proposed. The mechanism of co-propagating MZI configuration which holds SOA symmetrically in both arms is opted to design Ex-OR gate. The two nonlinearity property XGM and XPM of SOA is exploited to obtain the Ex-OR gate logic output. The extinction ratio obtained is 44.679 dB in Ex-OR gate and 46.206 dB in binary to gray code converter at 10 GB/s.

关键词： Co-propagating Mach-Zehnder Interferometer Semiconductor Optical Amplifier Gray code binary code Digital Encoder

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BinDeep: binary to Source code Matching Using Deep Learning 20

BinDeep: Binary to Source Code Matching Using Deep Learning

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20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Alrabaee, Saed Choo, Kim-Kwang Raymond Qbea'h, Mohammad Khasawneh, Mahmoud United Arab Emirates Univ UAEU Informat Syst & Secur Al Ain U Arab Emirates Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX USA Al Ain Univ Comp Engn Dept Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665416580

Mapping a binary function taken from a compiled binary to the same function in the original source code has many security applications, such as discovering reused free open source code in malware binaries. To facilitate malware analysis, we present BINDEEP, a framework that learns the semantic relationships among binary functions based on assembly code. It also learns semantic information about the source functions in order to carry out function matching. We demonstrate how BINDEEP can be applied to fingerprint the origin of functions in malware binaries, and then benchmark its performance against that of five competing systems (i.e., RESOURCE, the binary Analysis Tool (BAT), BinPro, Statistical Machine Translation (SMT), and FOSSIL). The findings show that BINDEEP is more robust and achieves significant improvement over these existing systems when confronted with changes introduced by code transformation methods or the use of different compilers and optimization levels. Furthermore, BINDEEP is able to discover source packages in malware binaries, such as Zeus and Citadel, that match those listed in existing security reports.

关键词： binary code machine learning malicious code

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All-optical binary to Gray code converter and Gray code to binary converter with proper use of nonlinear material

All-optical binary to Gray code converter and Gray code to b...

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International Conference on Optics and Photonics (ICOP)

作者： Pahari, Nirmalya Sripat Singh Coll Jiaganj Murshidabad India

All-optical systems for binary to Gray code conversion and Gray code to binary conversion are proposed where the input and output binary digits are expressed as the presence (=1) or the absence (=0) of a light signal.... 详细信息

ISBN: (纸本)9781628418644

关键词： code conversion binary code Gray code Optical Nonlinear Material Optical Switch

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Efficient Exhaustive Search for binary Complementary code Sets

Efficient Exhaustive Search for Binary Complementary Code Se...

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47th Annual Conference on Information Sciences and Systems (CISS)

作者： Coxson, Gregory E. Russo, Jon C. Naval Res Lab Washington DC USA

ISBN: (纸本)9781467352390;9781467352376

binary complementary code sets offer a possibility that single binary codes cannot -zero aperiodic autocorrelation sidelobe levels. These code sets can be viewed as the columns of so-called complementary code matrices, or CCMs. This matrix formulation is particularly useful in gaining the insight needed for developing an efficient exhaustive search for complementary code sets. An exhaustive search approach is described, designed to find all sets of K complementary binary codes of length N, for specified N and K. Results for several cases are examined.

关键词： complementary code set Hadamard matrix autocorrelation sidelobes binary code exhaustive search

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