Binary Encoding Research Articles

Digital information encrypted in an image using binary encoding

This paper proposes a new type of encoding methods to encrypt hidden (covert) information in host images. The encrypted information can be plot, fax, word, or network data, and it must be encoded with binary codes. All the pixels in an encoded (overt) image modulated from a host image are classified into three groups. The first group of pixels is called identification codes, used to judge whether the overt image is encoded by a method proposed in this paper or not. The second group of pixels is called type codes, used to judge the encoding type. The third group of pixels is called information codes, used to decode the encoded information. Applying the proposed encoding methods is quite convenient, and host images are not needed for decoding. Decoding covert information from overt images is rather difficult for un-authorized persons, whereas it is very easy for designers or authorized persons. Therefore, the proposed methods are very useful.

Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs

BackgroundAs one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins.ResultsA new protein bioinformatics tool, CKSAAP_OGlySite, was developed to predict mucin-type O-glycosylation serine/threonine (S/T) sites in mammalian proteins. Using the composition of k-spaced amino acid pairs (CKSAAP) based encoding scheme, the proposed method was trained and tested in a new and stringent O-glycosylation dataset with the assistance of Support Vector Machine (SVM). When the ratio of O-glycosylation to non-glycosylation sites in training datasets was set as 1:1, 10-fold cross-validation tests showed that the proposed method yielded a high accuracy of 83.1% and 81.4% in predicting O-glycosylated S and T sites, respectively. Based on the same datasets, CKSAAP_OGlySite resulted in a higher accuracy than the conventional binary encoding based method (about +5.0%). When trained and tested in 1:5 datasets, the CKSAAP encoding showed a more significant improvement than the binary encoding. We also merged the training datasets of S and T sites and integrated the prediction of S and T sites into one single predictor (i.e. S+T predictor). Either in 1:1 or 1:5 datasets, the performance of this S+T predictor was always slightly better than those predictors where S and T sites were independently predicted, suggesting that the molecular recognition of O-glycosylated S/T sites seems to be similar and the increase of the S+T predictor's accuracy may be a result of expanded training datasets. Moreover, CKSAAP_OGlySite was also shown to have better performance when benchmarked against two existing predictors.ConclusionBecause of CKSAAP encoding's ability of reflecting characteristics of the sequences surrounding mucin-type O-glycosylation sites, CKSAAP_ OGlySite has been proved more powerful than the conventional binary encoding based method. This suggests that it can be used as a competitive mucin-type O-glycosylation site predictor to the biological community. CKSAAP_OGlySite is now available at .

Computing strong metric dimension of some special classes of graphs by genetic algorithms

In this paper we consider the NP-hard problem of determining the strong metric dimension of graphs. The problem is solved by a genetic algorithm that uses binary encoding and standard genetic operators adapted to the problem. This represents the first attempt to solve this problem heuristically. We report experimental results for the two special classes of ORLIB test instances: crew scheduling and graph coloring.

Computing the metric dimension of graphs by genetic algorithms

In this paper we consider the NP-hard problem of determining the metric dimension of graphs. We propose a genetic algorithm (GA) that uses the binary encoding and the standard genetic operators adapted to the problem. The feasibility is enforced by repairing the individuals. The overall performance of the GA implementation is improved by a caching technique. Since the metric dimension problem up to now has been considered only theoretically, standard test instances for this problem do not exist. For that reason, we present the results of the computational experience on several sets of test instances for other NP-hard problems: pseudo boolean, crew scheduling and graph coloring. Testing on instances with up to 1534 nodes shows that GA relatively quickly obtains approximate solutions. For smaller instances, GA solutions are compared with CPLEX results. We have also modified our implementation to handle theoretically challenging large-scale classes of hypercubes and Hamming graphs. In this case the presented approach reaches optimal or best known solutions for hypercubes up to 131072 nodes and Hamming graphs up to 4913 nodes.

Genetic algorithms for solving the discrete ordered median problem

In this paper we present two new heuristic approaches to solve the Discrete Ordered Median Problem (DOMP). Described heuristic methods, named HGA1 and HGA2 are based on a hybrid of genetic algorithms (GA) and a generalization of the well-known Fast Interchange heuristic (GFI). In order to investigate the effect of encoding on GA performance, two different encoding schemes are implemented: binary encoding in HGA1, and integer representation in HGA2. If binary encoding is used (HGA1), new genetic operators that keep the feasibility of individuals are proposed. Integer representation keeps the individuals feasible by default, so HGA2 uses slightly modified standard genetic operators. In both methods, caching GA technique was integrated with the GFI heuristic to improve computational performance. The algorithms are tested on standard ORLIB p-median instances with up to 900 nodes. The obtained results are also compared with the results of existing methods for solving DOMP in order to assess their merits.

All‐Optical Operation Cycle on Molecular Bits with 250‐GHz Clock‐Rate Based on Photochromic Fulgides

AbstractThe prototype operation of an ultrafast write–readout–erase–readout cycle of an all‐optical system based on photochromic indolylfulgides is demonstrated. In the employed dye the molecular structure is switched using light of different wavelengths between two thermally stable states to allow binary encoding of information. Non‐destructive readout of the bit states using infrared light completes the scheme of an all‐optical memory. For ultrafast operation femtosecond light pulses are applied and it is demonstrated that two consecutive write/erase processes separated by less than 4 ps still allow the defined readout of the bit state. The short time between the write/erase and readout actions demonstrates that an all‐optical data storage system based on indolylfulgides may be operated at memory clock rates that exceed 250 GHz.

Photoamplification and Multiple Tag Release in a Linear Peptide‐Based Array of Dithiane Adducts

Binary encoding and amplification can be achieved in linear polypeptide-based scaffolds of photoactive dithiane adducts. QA masked sensitizer is tethered to lysine and incorporated in a peptide chain terminated with a sensitizer (see picture). When illuminated, the sensitizer induces fragmentation, thus unmasking more sensitizer and resulting in a linear domino photoamplification effect.

Introducing the Microsoft Vista event log file format

Several operating systems provide a central logging service which collects event messages from the kernel and applications, filters them and writes them into log files. Since more than a decade such a system service exists in Microsoft Windows NT. Its file format is well understood and supported by forensic software. Microsoft Vista introduces an event logging service which entirely got newly designed. This confronts forensic examiners and software authors with unfamiliar system behavior and a new, widely undocumented file format. This article describes the history of Windows system loggers, what has been changed over time and for what reason. It compares Vista log files in their native binary form and in a textual form. Based on the results, this paper for the first time publicly describes the key-elements of the new log file format and the proprietary binary encoding of XML. It discusses the problems that may arise during daily work. Finally it proposes a procedure for how to recover information from log fragments. During a criminal investigation this procedure was successfully applied to recover information from a corrupted event log.

Integrating visible, near‐infrared and short wave infrared hyperspectral and multispectral thermal imagery for geologic mapping: simulated data

Hyperspectral and thermal infrared (TIR) multispectral remote sensing have great potential for surface geological mapping. This paper investigates the potential impact of combining these data on the comparative accuracy of different classification methods. A series of simulated datasets based on the characteristics of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER) sensors was created from surface reflectance and emissivity data derived from library spectra of 16 common minerals and rocks occurring in Cuprite, Nevada. System noise, illumination effects, the presence of vegetation, and spectral mixing were added to create the simulated data. Five commonly used classification algorithms, minimum distance, maximum likelihood classification, binary encoding, spectral angle mapper (SAM) and spectral feature fitting (SFF), were applied to all datasets. All the classification methods, excluding binary encoding, achieved nominal to significant improvement in overall accuracy when applied to the combined datasets in comparison to using only the AVIRIS dataset. Furthermore, certain classification methods of the combined datasets show a marked increase in individual rock or mineral class accuracies. Limestone, silicified and muscovite, for instance, show an improvement of almost 30% or greater in either producer's or user's accuracy using the combined datasets with SAM. SFF provides a great improvement in accuracy for limestone, quartz and muscovite. In terms of overall comparative accuracy for the individual and the combined datasets, maximum likelihood classification shows the best performance. For the simulated AVIRIS data, SFF was generally superior to SAM, although the accuracy of SAM applied to the combined datasets was slightly better than that of SFF. SAM applied to the combined datasets increases classification accuracy for some minerals and rocks which do not exhibit distinct absorption feature in the TIR region, while for SFF, only the accuracy of minerals and rocks with characteristic absorption features in the TIR region is improved.

MiCA: A Web-Based Tool for the Analysis of Microbial Communities Based on Terminal-Restriction Fragment Length Polymorphisms of 16S and 18S rRNA Genes

A web-based resource, Microbial Community Analysis (MiCA), has been developed to facilitate studies on microbial community ecology that use analyses of terminal-restriction fragment length polymorphisms (T-RFLP) of 16S and 18S rRNA genes. MiCA provides an intuitive web interface to access two specialized programs and a specially formatted database of 16S ribosomal RNA sequences. The first program performs virtual polymerase chain reaction (PCR) amplification of rRNA genes and restriction of the amplicons using primer sequences and restriction enzymes chosen by the user. This program, in silico PCR and Restriction (ISPaR), uses a binary encoding of DNA sequences to rapidly scan large numbers of sequences in databases searching for primer annealing and restriction sites while permitting the user to specify the number of mismatches in primer sequences. ISPaR supports multiple digests with up to three enzymes. The number of base pairs between the 5' and 3' primers and the proximal restriction sites can be reported, printed, or exported in various formats. The second program, APLAUS, infers a plausible community structure(s) based on T-RFLP data supplied by a user. APLAUS estimates the relative abundances of populations and reports a listing of phylotypes that are consistent with the empirical data. MiCA is accessible at http://mica.ibest.uidaho.edu/.

Comparison of Different Heuristic Optimization Methods for Near-Field Antenna Measurements

A comparison between different modern heuristic optimization methods applied to antenna far-field radiation pattern reconstruction from planar near-field data is presented in this paper. The antenna under test is represented by means of equivalent magnetic currents (EMC) whose components are optimized using several heuristic algorithms such as simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO), as well as a traditional local optimization method, the Nelder Mead downhill simplex algorithm. Several schemes for GA (classical real-valued and binary encoding, and their hybrid versions) and PSO (global or local topologies with synchronous or asynchronous updates of the swarm) have been considered in the analysis and the pros and cons of each one are reported and discussed. A study of the performance and limitations of each algorithm using as a canonical problem a small size antenna aperture, along with results of near-field to far-field (NF-FF) transformation are also included

Low-power state encoding for partitioned FSMs with mixed synchronous/asynchronous state memory

Partitioned finite state machine (FSM) architectures in general enable low-power implementations and it has been shown that for these architectures, state memory based on both synchronous and asynchronous storage elements gives lower power consumption compared to their fully synchronous counterparts. In this paper we present state encoding techniques for a partitioned FSM architecture based on mixed synchronous/asynchronous state memory. The state memory, in this case, is composed of a synchronous local state memory and an asynchronous global state memory. The local state memory uses synchronous storage elements and is shared by all sub-FSMs. The global state memory operates asynchronously and is responsible for handling the interaction between sub-FSMs. Even though the partitioned FSM contains the asynchronous mechanism, its input/output behaviour is still cycle by cycle equivalent to the original monolithic synchronous FSM. In this paper, we discuss the low-power state encoding method for the implementation of partitioned FSM with mixed synchronous/asynchronous state memory. For the local state assignment a, what we call, state-bundling procedure is presented to enable states residing in different sub-FSMs to share the same state codes. Based on state-bundles, two state encoding techniques, in which one is the employment of binary encoding and the other is the further optimization for low power, are compared.

A novel splicing/decomposable binary encoding and its operators for genetic and evolutionary algorithms

In this paper, we introduce a new genetic representation — a splicing/decomposable (S/D) binary encoding, which was proposed based on some theoretical guidance and existing recommendations for designing efficient genetic representations. The S/D binary representation can be spliced and decomposed to describe potential solutions of the problem with different precisions by different number of uniform-salient building blocks (BBs). According to the characteristics of the S/D binary representation, genetic and evolutionary algorithms (GEAs) can be applied from the high scaled to the low scaled BBs sequentially to avoid a noise from the competing BBs and improve GEAs’ performance. Our theoretical and empirical investigations reveal that the S/D binary representation is more proper than other existing binary encodings for GEAs searching. Moreover, we define a new genotypic distance on the S/D binary space, which is equivalent to the Euclidean distance on the real-valued space during GEAs convergence. Based on the new genotypic distance, GEAs can reliably and predictably solve problems of bounded complexity and the methods depended on the Euclidean distance for solving different kinds of optimization problems can be directly used on the S/D binary space.

A simple storage scheme for strings achieving entropy bounds

We propose a storage scheme for a string S [ 1 , n ] , drawn from an alphabet Σ , that requires space close to the k -th order empirical entropy of S , and allows one to retrieve any substring of S of length ℓ in optimal O ( 1 + ℓ log | Σ | n ) time. This matches the best known bounds [R. González, G. Navarro, Statistical encoding of succinct data structures, in: Procs CPM, in: LNCS, vol. 4009, 2006, pp. 295–306; K. Sadakane, R. Grossi, Squeezing succinct data structures into entropy bounds, in: Procs ACM-SIAM SODA, 2006, pp. 1230–1239], via the use of binary encodings and tables only. We also apply our storage scheme to the Burrows–Wheeler Transform [M. Burrows, D. Wheeler, A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation, 1994], and achieve a space bound which depends on both the k -th order entropy of S and the k -th order entropy of its BW-transformed string bwt ( S ) .

Quantized Spiking Neuron With A/D Conversion Functions

This brief presents a simple artificial spiking neuron and proposes its application to an A/D converter. Depending on the initial state, which is an analog input, the neuron can generate spike trains having various spike position patterns. Based on spike position modulation, the spike train can be symbolized by a digital output. As a result, the analog input can be encoded into the digital output. Adjusting a reconfigurable parameter, the neuron can realize various encodings such as binary and Gray encodings. This brief also proposes a simple reconfigurable implementation circuit and experimentally confirms typical A/D conversion functions

A note on the complexity of the problem of two-agent scheduling on a single machine

We consider a two-agent scheduling problem on a single machine, where the objective is to minimize the total completion time of the first agent with the restriction that the number of tardy jobs of the second agent cannot exceed a given number. It is reported in the literature that the complexity of this problem is still open. We show in this paper that this problem is NP-hard under high multiplicity encoding and can be solved in pseudo-polynomial time under binary encoding. When the first agent's objective is to minimize the total weighted completion time, we show that the problem is strongly NP-hard even when the number of tardy jobs of the second agent is restricted to be zero.

A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity

Unravelling the rules underlying protein-protein and protein-ligand interactions is a crucial step in understanding cell machinery. Peptide recognition modules (PRMs) are globular protein domains which focus their binding targets on short protein sequences and play a key role in the frame of protein-protein interactions. High-throughput techniques permit the whole proteome scanning of each domain, but they are characterized by a high incidence of false positives. In this context, there is a pressing need for the development of in silico experiments to validate experimental results and of computational tools for the inference of domain-peptide interactions. We focused on the SH3 domain family and developed a machine-learning approach for inferring interaction specificity. SH3 domains are well-studied PRMs which typically bind proline-rich short sequences characterized by the PxxP consensus. The binding information is known to be held in the conformation of the domain surface and in the short sequence of the peptide. Our method relies on interaction data from high-throughput techniques and benefits from the integration of sequence and structure data of the interacting partners. Here, we propose a novel encoding technique aimed at representing binding information on the basis of the domain-peptide contact residues in complexes of known structure. Remarkably, the new encoding requires few variables to represent an interaction, thus avoiding the 'curse of dimension'. Our results display an accuracy >90% in detecting new binders of known SH3 domains, thus outperforming neural models on standard binary encodings, profile methods and recent statistical predictors. The method, moreover, shows a generalization capability, inferring specificity of unknown SH3 domains displaying some degree of similarity with the known data.

Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study

Fuzzy ordered classifiers were used to assign fuzzy labels to river sites expressing their suitability as a habitat for a certain macroinvertebrate taxon, given up to three abiotic properties of the considered river site. The models were built using expert knowledge and evaluated on data collected in the Province of Overijssel in the Netherlands. Apart from a performance measure for crisp classifiers common in the aquatic ecology domain, the percentage of correctly classified instances (% CCI), two performance measures for fuzzy (ordered) classifiers are introduced in this paper: the percentage of correctly fuzzy classified instances (% CFCI) and the average deviation (AD). Furthermore, results of an interpretability-preserving genetic optimization of the linguistic terms, applying once binary encoding and once real encoding, are presented.

Optimizing chromatic aberration calibration using a novel genetic algorithm

Advances in digitalized image optics has increased the importance of chromatic aberration. The axial and lateral chromatic aberrations of an optical lens depends on the choice of optical glass. Based on statistics from glass companies worldwide, more than 300 optical glasses have been developed for commercial purposes. However, the complexity of optical systems makes it extremely difficult to obtain the right solution to eliminate small chromatic aberration. Even the damped least-squares technique, which is a ray-tracing-based method, is limited owing to its inability to identify an enhanced optical system configuration. Alternatively, this study instead attempts to eliminate even negligible axial and lateral colour aberration by using algorithms involving the theories of geometric optics in triplet lens, binary and real encoding, multiple dynamic crossover and random gene mutation techniques.

Cycle-logical treatment for "Cyclopathic" networks

This correspondence addresses the problem of finding the network encoding equations for error-free networks with multiple sources and sinks. Previous algorithms could not cope with cyclic networks. Networks that are cyclic in three different senses are considered in this correspondence, and two extensions of the polynomial time Linear Information Flow (LIF) algorithm are presented. The first algorithm will produce the network encoding equations for a network which can be cyclic, unless the actual flow paths form cycles. The second algorithm will work also when the flow paths form simple cycles. Finally an example of a third kind of cyclic network, where the previous algorithms will fail, is given. However, a binary encoding is provided also in this case.