Bit String Research Articles

Principal Component Analysis applied directly to Sequence Matrix

Sequence data is now widely used to observe relationships among organisms. However, understanding structure of the qualitative data is challenging. Conventionally, the relationships are analysed using a dendrogram that estimates a tree shape. This approach has difficulty in verifying the appropriateness of the tree shape; rather, horizontal gene transfers and mating can make the shape of the relationship as networks. As a connection-free approach, principal component analysis (PCA) is used to summarize the distance matrix, which records distances between each combination of samples. However, this approach is limited regarding the treatment of information of sequence motifs; distances caused by different motifs are mixed up. This hides clues to figure out how the samples are different. As any bases may change independently, a sequence is multivariate data essentially. Hence, differences among samples and bases that contribute to the difference should be observed coincidentally. To archive this, the sequence matrix is transferred to boolean vector and directly analysed by using PCA. The effects are confirmed in diversity of Asiatic lion and human as well as environmental DNA. Resolution of samples and robustness of calculation is improved. Relationship of a direction of difference and causative nucleotides has become obvious at a glance.

Sensor Selection for TDOA-Based Localization in Wireless Sensor Networks With Non-Line-of-Sight Condition

This paper investigates the selection of a subset of sensors for time difference of arrival (TDOA) localization under the non-line-of-sight (NLOS) condition in wireless sensor networks (WSN). Specifically, we aim to optimize the sensor activation for the sake of minimizing the localization error when considering NLOS condition subject to energy constraints. In contrast to existing sensor selection strategies, two independent Boolean selection vectors are utilized to determine the reference sensor and other sensors simultaneously in TDOA localization. Upon presenting expressions of the Cramer-Rao lower bound (CRLB) under three different scenarios, including: 1) line-of-sight (LOS), 2) Prior statistics unknown NLOS (PSU-NLOS), 3) Prior statistics known NLOS (PSK-NLOS), the optimization problems for sensor selection are formulated to minimize the CRLB based on two independent Boolean selection vectors. Analytical scheme is developed by solving the tractable semidefinite program (SDP) problems which are converted from the original nonconvex problem. Furthermore, two low-complexity heuristic algorithms, namely best option filling (BOF) algorithm and iterative swapping greedy (ISG) algorithm, are proposed for the sake of practical implementation. Simulation results validate that the localization accuracy for sensors selected by the SDP with randomization algorithm and the ISG algorithm achieves the exhaustive search method. Additionally, these two algorithms are stable under several random sensor network geometries.

Binary MDS Array Codes With Optimal Repair

Consider a binary maximum distance separable (MDS) array code composed of an $m\times (k+r)$ array of bits with $k$ information columns and $r$ parity columns, such that any $k$ out of $k+r$ columns suffice to reconstruct the $k$ information columns. Our goal is to provide {\em optimal repair access} for binary MDS array codes, meaning that the bandwidth triggered to repair any single failed information or parity column is minimized. In this paper, we propose a generic transformation framework for binary MDS array codes, using EVENODD codes as a motivating example, to support optimal repair access for $k+1\le d \le k+r-1$, where $d$ denotes the number of non-failed columns that are connected for repair; note that when $d<k+r-1$, some of the chosen $d$ columns in repairing a failed column are specific. In addition, we show how our transformation framework applies to an example of binary MDS array codes with asymptotically optimal repair access of any single information column and enables asymptotically or exactly optimal repair access for any column. Furthermore, we present a new transformation for EVENODD codes with two parity columns such that the existing efficient repair property of any information column is preserved and the repair access of parity column is optimal.

Applying ROBDDs for Logical Circuit Delay Testing

Increasing frequency of functioning and decreasing transistor sizes in high performance logical circuits may result in illegal capacities, inductivities, resistances, and so on that generate decreasing estimated circuit frequency. These defects cannot be detected by physical methods. The main way of solving the problem is based on delay testing of logical circuits within the path delay fault (PDF) model. In this paper, facilities of enhancing PDF test sequence quality based on application of Reduced Ordered Binary Decision Diagrams (ROBDDs) that compactly represent all test pairs of neighbor test patterns for the circuit path are studied. Test patterns (Boolean vectors) are neighbor if they differ by only one component. It is established that using of these ROBDDs cut the lengths of test sequences by more than 1/3 in comparison with traditional scan test sequences simultaneously enhancing test sequence quality. In particular, we derive test sequences for robust testable PDFs of sequential circuits decreasing their power consumption and peak power values.

Multilayer Neural Network Technique for Parsing the Natural Language Sentences

In this article is presented an approach for parsing natural language sentences using neural networks. The pre-processing technique is applied to code the sentences into string of bits and after the training process is started, is formed into patterns available in the form of coded information. The multilayer feed forward networks are used here for training to classify the words into appropriate syntactical categories. The classified words represent the parsed information of the given sentences. The main function of the network is to assign the respective syntactical categories to each word of a sentence with a minimal error rate. The comparison between the two popular neural network approaches i.e. feed forward neural network and radial basis neural network is presented to analyze performance for the new and unknown sentences.

Sum Codes with Fixed Values of Multiplicities for Detectable Unidirectional and Asymmetrical Errors for Technical Diagnostics of Discrete Systems

We introduce the concept of sum codes with fixed values of the multiplicities of unidirectional and asymmetrical errors in data vectors. We show that such codes can be constructed on the basis of weighing one of the data vector's bits by a natural number w = 2 and then calculating the total weight of the data vector modulo the Berger code (M = 2[log2(m+1)]). We establish the basic characteristics of the new class of sum codes. Compared with the Berger code, the proposed codes have the advantage of detecting symmetrical errors while maintaining the property of detecting any unidirectional and asymmetrical errors up to fixed multiplicities. Such codes can be effectively used in the construction of concurrent error-detection systems for combinational logic devices and, especially, in the construction of systems with the detection of all single faults in the controlled device.

Error correction in quantum cryptography based on artificial neural networks

Intensive work on quantum computing has increased interest in quantum cryptography in recent years. Although this technique is characterized by a very high level of security, there are still challenges that limit the widespread use of quantum key distribution. One of the most important problems remains secure and effective mechanisms for the key distillation process. This article presents a new idea for a key reconciliation method in quantum cryptography. This proposal assumes the use of mutual synchronization of artificial neural networks to correct errors occurring during transmission in the quantum channel. Users can build neural networks based on their own string of bits. The typical value of the quantum bit error rate does not exceed a few percent; therefore, the strings are similar and also users’ neural networks are very similar at the beginning of the learning process. It has been shown that the synchronization process in the new solution is much faster than in the analogous scenario used in neural cryptography. This feature significantly increases the level of security because a potential eavesdropper cannot effectively synchronize their own artificial neural networks in order to obtain information about the key. Therefore, the key reconciliation based on the new idea can be a secure and efficient solution.

On some properties of vector functions of Boolean algebra

Abstract The functional system of Boolean vector functions with naturally defined superposition operation is studied. Sufficient conditions for membership of a number of important vector functions in closed classes are given.

Local Synchronization of Interconnected Boolean Networks With Stochastic Disturbances.

This paper is concerned with the local synchronization problem for the interconnected Boolean networks (BNs) without and with stochastic disturbances. For the case without stochastic disturbances, first, the limit set and the transient period of the interconnected BNs are discussed by resorting to the properties of the reachable set for the global initial states set. Second, in terms of logical submatrices of a certain Boolean vector, a compact algebraic expression is presented for the limit set of the given initial states set. Based on it, several necessary and sufficient conditions are derived assuring the local synchronization of the interconnected BNs. Subsequently, an efficient algorithm is developed to calculate the largest domain of attraction. As for the interconnected BNs with stochastic disturbances, first, mutually independent two-valued random logical variables are introduced to describe the stochastic disturbances. Then, the corresponding local synchronization criteria are also established, and the algorithm to calculate the largest domain of attraction is designed. Finally, numerical examples are employed to illustrate the effectiveness of the obtained results/ algorithms.

Saddle Point Search Algorithm for the Problem of Site Protection Level Assignment Based on Search of Simplices

This paper deals with a continuous zero-sum game with constraints on resources between a defender allocating resources for protection of sites and an attacker choosing sites for attack. The problem is formulated so that each player would have to solve its own linear program with a fixed solution of the other player. We show that in this case the saddle point is located on the faces of simplices defining feasible solutions. We propose an algorithm of saddle point search based on search of the simplices' faces on hyperplanes of equal dimension. Each possible face is defined using a boolean vector defining states of variables and problem constraints. The search of faces is reduced to the search of feasible boolean vectors. In order to reduce computational complexity of the search we formulate the rules for removing patently unfeasible faces. Each point of a face belonging to an (m--1)-dimensional hyperplane is defined using m points of the hyperplane. We created an algorithm for generating these points. Two systems of linear equations must be solved in order to find the saddle point if it located on the faces of simplices belonging to hyperplanes of equal dimension. We created a generic algorithm of saddle point search on the faces located on hyperplanes of equal dimension. We present an example of solving a problem and the results of computational experiments

BER analysis of concatenated levels of encoding in GFDM system using labview

&lt;p&gt;All the devices are interconnected each other in digital form, for different applications the input data is encoded for error correcting and detecting purpose. The paper describes the transmission of QAM signals with two level encoded stages, i.e. convolutional and hamming coded GFDM system with 256-point IFFT at transmitter and FFT at the receiver using LABVIEW software. GFDM is a non-orthogonal, digital multicarrier transmission scheme which digitally implements the classical filter bank approach. GFDM transmits a block of frame composed by M time slots with K subcarriers. The higher order QAM is used because of transmitting more data but is less reliable when compared to lower order QAM. Based on GFDM specifications for the IEEE 802.11, latest 5G physical layer standards, the coding is provided by ½ rate encoder at the input side, and Maximum Likelihood decoder at the receiver side is used. The standard convolution code (7, [171, 133]), is used as encoder for the GFDM system. The GFDM complex values are displayed in the front panel, along with FFT and power spectrum is plotted for GFDM signal. The array of input bits and output bits are shown with green colour LED’s. The van de Beek algorithm is used at the receiver for maximum likelihood detection acts as convolutional decoder of GFDM signal. Next the signal is subjected to remove cyclic prefix and zero padding and applied to channel estimation algorithm. The un-equalized data and equalized data graph is shown in the front panel, before and after channel estimation VI. With BER VI available in the LABVIEW the data is normalized and its response is plotted with respect to SNR. BER values for different levels of encoders have shown in table for SNR values. This paper concludes the 32.91% improvement in BER for two levels of concatenated codes.Thus the GFDM signal outperforms the OFDM signal interms of BER for series levels of coding using labVIEW software.&lt;/p&gt;

High capacity reversible data hiding with interpolation and adaptive embedding.

A new Interpolation based Reversible Data Hiding (IRDH) scheme is reported in this paper. For different applications of an IRDH scheme to the digital image, video, multimedia, big-data and biological data, the embedding capacity requirement usually varies. Disregarding this important consideration, existing IRDH schemes do not offer a better embedding rate-distortion performance for varying size payloads. To attain this varying capacity requirement with our proposed adaptive embedding, we formulate a capacity control parameter and propose to utilize it to determine a minimum set of embeddable bits in a pixel. Additionally, we use a logical (or bit-wise) correlation between the embeddable pixel and estimated versions of an embedded pixel. Thereby, while a higher range between an upper and lower limit of the embedding capacity is maintained, a given capacity requirement within that limit is also attained with a better-embedded image quality. Computational modeling of all new processes of the scheme is presented, and performance of the scheme is evaluated with a set of popular test-images. Experimental results of our proposed scheme compared to the prominent IRDH schemes have recorded a significantly better-embedding rate-distortion performance.

Orthogonal Range Searching in Moderate Dimensions: k-d Trees and Range Trees Strike Back

We revisit the orthogonal range searching problem and the exact \(\ell _\infty \) nearest neighbor searching problem for a static set of n points when the dimension d is moderately large. We give the first data structure with near linear space that achieves truly sublinear query time when the dimension is any constant multiple of \(\log n\). Specifically, the preprocessing time and space are \(O(n^{1+\delta })\) for any constant \(\delta >0\), and the expected query time is \(n^{1-1/O(c\log c)}\) for \(d=c\log n\). The data structure is simple and is based on a new “augmented, randomized, lopsided” variant of k-d trees. It matches (in fact, slightly improves) the performance of previous combinatorial algorithms that work only in the case of offline queries [(Impagliazzo et al. in arXiv:1401.5512, 2014) and (Chan in Proceedings of the 26th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015)]. It leads to slightly faster combinatorial algorithms for all-pairs shortest paths in general real-weighted graphs and rectangular Boolean matrix multiplication. In the offline case, we show that the problem can be reduced to the Boolean orthogonal vectors problem and thus admits an \(n^{2-1/O(\log c)}\)-time non-combinatorial algorithm [Abboud et al. in Proceedings of the 26th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015]. This reduction is also simple and is based on range trees. Finally, we use a similar approach to obtain a small improvement to Indyk’s data structure (J Comput Syst Sci 63(4):627–638, 2001) for approximate\(\ell _\infty \) nearest neighbor search when \(d=c\log n\).

Implementation of RAM Based Neural Networks on Maze Mapping Algorithms for Wall Follower Robot

A wall follower robot will recognize its environment by knowing the position of the surrounding wall. The goal is that the robot has a good safe navigation system without damaging the walls. For navigation systems, robots use the Maze Mapping method with the left-hand rule, while to study the wall distance pattern is a RAM-based artificial neural network method. The neural network has 3 RAM nodes to process the received environmental patterns. The left sensor, the right sensor, and the front sensor have 8 bits of the input pattern, the pattern will be optimized into 4 bits stored in the RAM node, so that the computing process in the robot becomes faster and simpler [1][4]. RAM discriminator design has 3 RAM nodes. Each RAM node has 4 bit words (x = 4), with a total of 12 input vector bits (n = 12). Each RAM discriminator will receive 48 binary input patterns. The results of environmental polo testing, the wall follower robot can complete the labyrinth path which has 8 intersections and a dead end with a distance of 595 cm, then the next 4 intersections with a distance of 360 cm.

Implicit representation conjecture for semi-algebraic graphs

The implicit representation conjecture concerns hereditary families of graphs. Given a graph in such a family, we want to assign some string of bits to each vertex in such a way that we can recover the information about whether 2 vertices are connected or not using only the 2 strings of bits associated with those two vertices. We then want to minimize the length of this string. The conjecture states that if the family is hereditary and small enough (it only has 2O(nln(n)) graphs of size n), then O(ln(n)) bits per vertex should be sufficient. The trivial bounds on this problem are that: (1) some families require at least log2(n) bits per vertex ; (2) (n−1)∕2+log2(n) bits per vertex are sufficient for all families.In this paper, we will be talking about a special case of the implicit representation conjecture, where the family is semi-algebraic (which roughly means that the vertices are points in some euclidean space, and the edges are defined geometrically, or according to some polynomials). We will first prove that the ‘obvious’ way of storing the information, where we store an approximation of the coordinates of each vertex, does not work. Then we will come up with a way of storing the information that requires O(n1−ϵ) bits per vertex, where ϵ is some small constant depending only on the family. This is a slight improvement over the trivial bound, but is still a long way from proving the conjecture.

Role of the structure of Boolean hypercubes when used as vectors in natural (Boolean) vector semispaces

Role of the structure of Boolean hypercubes when used as vectors in natural (Boolean) vector semispaces

Modeling Direct Transmission Diseases Using Parallel Bitstring Agent-Based Models

Agent-based models (ABMs) are gaining importance over traditional epidemiological modeling due to advances in computing technology and catalyzed by the need for detailed epidemiological analysis of emergent diseases. Unfortunately, the advantages of ABMs are realized at the cost of significantly large execution times and high memory consumption for large-scale simulations. To address the memory issue, we designed and implemented an ABM using an innovative feature: the bitstring approach, in which the attributes of each agent are represented by an array of bits instead using traditional data structures. To cope with the high computational demands, we developed a parallel version of model aiming multicore CPUs and GPUs architectures using Thrust parallel algorithms library. The results of our model were validated comparing them with data of a spread of Influenza A in the Cascavel City, South Brazil, occurred in 2009. The model presented good qualitative results and an excellent performance on GPUs. The application of bitstring technique is proved to be relevant in economy of memory, allowing to store the same attributes using 41% less memory space and improving the data copy time between CPU and GPU up to 52%.

Material distributive topology design of UWB antenna using parallel computation of improved BPSO with FDTD

AbstractIn this article, the material distributive topology-based design optimization of ultra-wide band (UWB) antenna is proposed by using improved binary particle swarm optimization (BPSO) with finite difference time domain (FDTD) method. In the improved BPSO implementation, the velocity of each particle is calculated based on complete set of bits of particle position vector. The V-shaped transfer function is employed to transform all real values of velocities to values in the interval [0,1]. The fitness function of all the particles in BPSO algorithm are computed parallely by using FDTD simulation. The usage of FDTD and the parallel computation helps in analyzing the broadband frequency characteristics of the antenna with a single simulation run. The return loss of the optimized UWB antenna obtained from FDTD, Computer Simulation Technology (CST) simulation and practical measurement are in good agreement and show good impedance matching.

Constant-Time Discrete Gaussian Sampling

Sampling from a discrete Gaussian distribution is an indispensable part of lattice-based cryptography. Several recent works have shown that the timing leakage from a non-constant-time implementation of the discrete Gaussian sampling algorithm could be exploited to recover the secret. In this paper, we propose a constant-time implementation of the Knuth-Yao random walk algorithm for performing constant-time discrete Gaussian sampling. Since the random walk is dictated by a set of input random bits, we can express the generated sample as a function of the input random bits. Hence, our constant-time implementation expresses the unique mapping of the input random-bits to the output sample-bits as a Boolean expression of the random-bits. We use bit-slicing to generate multiple samples in batches and thus increase the throughput of our constant-time sampling manifold. Our experiments on an Intel i7-Broadwell processor show that our method can be as much as 2.4 times faster than the constant-time implementation of cumulative distribution table based sampling and consumes exponentially less memory than the Knuth-Yao algorithm with shuffling for a similar level of security.

A Spectral-Efficient Self-Homodyne-Detected Microwave Photonic Link With an Extended Fiber-Reach

In this letter, a simple and spectral-efficient self-homodyne-detected microwave photonic link is proposed and experimentally demonstrated. At the transmitter, a phase-modulated optical signal and an intensity-modulated optical local oscillator signal are polarization-multiplexed and delivered in a single fiber over long distance. At the receiver, the two signals are coherently detected by a self-homodyne interferometer and then demodulated through simple digital signal processing. The proposed scheme is experimentally validated by 25-km fiber transmission of the 50-Msymbol/s 16-QAM microwave vector signals at 2 and 2.5 GHz. The transmission performance compared with the conventional homodyne detection scheme in terms of error vector magnitude and bit error rate is evaluated. The proposed scheme can be a potential solution to wireless access networks for both high spectral efficiency and low-cost implementation.