Input Symbols Research Articles

A clocked finite state machine built from DNA

We implement a finite state machine by representing state, transition rules and input symbols with DNA components. Transitions between states are triggered by a clock signal which allows synchronized, parallel operation of two (or more) state machines. The state machine can be re-programmed by changing the input symbols.

Transmission Control for Fast Recovery of Rateless Codes

Luby Transform (LT) codes are more important in communication applications due to the characteristics of fast encoding/decoding process, and low complexity. However, LT codes are optimal only when the number of input symbols is close to infinity. Some researches modified the degree distribution of LT codes to make LT codes suitable for short symbol length. In this article, we propose an optimal coding algorithm to recover all of the encoded symbols for LT codes quickly. The proposed algorithm observes the coding status of each client and increases the coding performance by changing the transmission sequence of low-degree and high-degree encoding packets. Simulation results show that the resulting decoding overhead of our algorithm are lower than the traditional LT codes, and our algorithm is very appropriate to server various clients in the broadcasting channel environment.

A Practical Optimization Framework for the Degree Distribution in LT Codes

SUMMARY LT codes are the first practical rateless codes whose reception overhead totally depends on the degree distribution adopted. The capability of LT codes with a particular degree distribution named robust soliton has been theoretically analyzed; it asymptotically approaches the optimum when the message length approaches infinity. However, real applications making use of LT codes have finite number of input symbols. It is quite important to refine degree distributions because there are distributions whose performance can exceed that of the robust soliton distribution for short message length. In this work, a practical framework that employs evolutionary algorithms is proposed to search for better degree distributions. Our experiments empirically prove that the proposed framework is robust and can customize degree distributions for LT codes with different message length. The decoding error probabilities of the distributions found in the experiments compare well with those of robust soliton distributions. The significant improvement of LT codes with the optimized degree distributions is demonstrated in the paper.

Blind channel estimation for cyclic prefix-free orthogonal frequency-division multiplexing systems with particular input symbols

Subspace algorithms are commonly used for blind channel estimation in orthogonal frequency-division multiplexing (OFDM) systems as they can establish the relation between the channel impulse response and singular vectors of the noise subspace. However, they are not workable under the conventional cyclic prefix-free OFDM signal model as the corresponding necessary condition of subspace algorithms is not satisfied. To solve this problem, this study proposes two subspace-based blind channel estimation approaches using the real and periodic symbols of time-domain transmitted OFDM blocks. From the simulation results, we can obtain the proposed approaches outperform conventional ones in terms of normalised mean-squared error and bit error rate under time-invariant channel environments.

Iterative Message Passing for Pilot-Assisted Multiuser Detection in MC-CDMA Systems

Application of the belief propagation (BP) algorithm is proposed for pilot-assisted reception of multi-carrier code division multiple access systems where the input symbols are binary phase shift keying symbols. The proposed method utilizes a Bayesian network graph that includes channel parameters as explicit nodes representing random variables. Channel parameters in both frequency and time domains are included in the graph. The messages on the graph are obtained by making large system assumptions for the user symbols and Gaussian assumptions for the messages related to the channel parameters. Density evolution analysis is performed whose results match well with the simulation results. For the simulated scenario it is found that BP with the proposed graph outperforms channel estimation followed by graph based detection, and non-graph based iterative channel estimation.

웨이블렛 변환과 HMM을 이용한 고유공간 기반 얼굴인식에 관한 연구

본 논문은 Wavelet 변환을 이용한 실시간 얼굴 영역 검출을 제안하였으며, 계산의 효율성과 검출 성능을 동시에 만족시키는 강인한 검출 알고리즘을 제안하였다. 검출된 얼굴 영상은 주성분 분석을 통해 저차원 얼굴 심볼로 구성하여 얼굴을 인식한다. 제안된 방법은 기존의 기하학적인 특징 기반 방법이나 외관기반 방법의 비해 많은 계산 량이 요구 되지 않고 최소한의 정보를 사용하고도 높은 인식률을 유지 할 수 있기에 실시간 시스템 구축에 매우 적합하다. 또한 얼굴 인식 시 발생하는 잘못된 인식이나 인식 오차를 줄이기 위해 고유 공간상에 투영된 모델 특징 값을 군집화 알고리즘을 통해 특정한 기호로 구성하여 은닉마르코프 모델의 입력 기호로 사용하였다. 이렇게 함으로써 임의의 입력 얼굴은 확률 값이 가장 높은 해당 얼굴 모델로 인식하게 된다. 실험 결과 기존의 방식인 Euclidean과 Mahananobis방법 보다 제안한 방법이 잘못된 매칭이나 매칭 실패에서 우수한 인식 성능을 보였다. This paper proposed the real time face area detection using Wavelet transform and the strong detection algorithm that satisfies the efficiency of computation and detection performance at the same time was proposed. The detected face image recognizes the face by configuring the low-dimensional face symbol through the principal component analysis. The proposed method is well suited for real-time system construction because it doesn't require a lot of computation compared to the existing geometric feature-based method or appearance-based method and it can maintain high recognition rate using the minimum amount of information. In addition, in order to reduce the wrong recognition or recognition error occurred during face recognition, the input symbol of Hidden Markov Model is used by configuring the feature values projected to the unique space as a certain symbol through clustering algorithm. By doing so, any input face will be recognized as a face model that has the highest probability. As a result of experiment, when comparing the existing method Euclidean and Mahananobis, the proposed method showed superior recognition performance in incorrect matching or matching error.

Near real-time suffix tree construction via the fringe marked ancestor problem

We contribute a further step towards the plausible real-time construction of suffix trees by presenting an on-line algorithm that spends only O(loglogn) time processing each input symbol and takes O(nloglogn) time in total, where n is the length of the input text. Our results improve on a previously published algorithm that takes O(logn) time per symbol and O(nlogn) time in total. The improvements are obtained by adapting [email protected]?s suffix tree construction algorithm to use a new data structure for the fringe marked ancestor problem, a special case of the nearest marked ancestor problem, which may be of independent interest.

Crypto System based Authentication using CSTA in Grid

Grid computing, as a distributed computing model, stands for the new kind of systems that pools heterogeneous computational resources, such as computers, storage space, sensors, application software, and experiment data. When a user wants to request some computing and data resources, the grid can seamlessly, transparently and dynamically supply them. The security issue has become an important concern of grid computing. To prevent the grid resources from being illegally visited, the strong mutual authentication is needed for user and server. In recent periods, many password-based user authentication schemes are proposed for solving the authentication issue. However, most of them are not ideal for grid computing, since they do not provide the strong mutual authentication. It is proposed to introduce an efficient user Cyclic Shift Transposition Algorithm (CSTA) scheme and a model for secure routing mechanism of sharing messages between source and destination in grid computing by adding information about sender and preserves the message content, which prevents not only known attacks but also maintains the integrity of data. This efficient method is implemented over text data. This model dynamically reorders the frequency of the input symbols according to the coupled cyclic shift system.All text is coded during initialization. The output from the engine is in the form of variable size words and the individual bit output corresponding to the inserted symbols cannot be determined thus highly confidential.

三值光学计算机一种限制输入一步式MSD加法器

On the basis of the research in MSD adder of Ternary Optical Computer (TOC), further work is carried out in one-step MSD adder with restricted input symbols. In this paper, the principle of general one-step MSD adder is introduced briefly. The core part of the principle can be reduced to the new conceptions of mid-bit transform, mid-bit transform table (MTT) and mid-bit transform unit (MTU). By restricting the input symbols, a simplified mid-bit transform table is obtained. Through the deep analysis to the simplified MTT, the subtransforms V and U as well as the main transform of the mid-bit transform are obtained, and the corresponding optical graphs are designed. Based on these, the structure of one-step MSD adder with restricted input symbols is proposed and its structure is designed. The software simulation and experiment to the mid-bit transform and the one-step adder show that the designed one-step MSD adder is effective. The adder will be one of the basic components of the ternary optical computer.

On deriving test suites for nondeterministic finite state machines with time-outs

In the paper, the non-separability relation for finite state machines with time-outs is studied. A specific feature of such machines is integer-valued delays, or time-outs, which determine how long the finite state machine will stay in one or another state if there are no input actions. If the time-out is over and no input symbol has been applied, then the TFSM state is changed according to the transition under time delay. In the paper, an algorithm for constructing a separating sequence for such finite state machines is presented. Here, the separating sequence is a timed input sequence for which sets of input sequences of the TFSM do not intersect; hence, it is sufficient to apply the separating sequence once in order to distinguish the TFSMs by their output reactions. This algorithm underlies the algorithm for construction of test suites with respect to non-separability relation in the case where the fault domain is specified by means of a mutation machine. Test suite derivation with respect to non-separability relation by way of "TFSM to FSM" transformation is discussed.

On Computational Power of Partially Blind Automata

On Computational Power of Partially Blind Automata In this paper we deal with 1-way multihead finite automata, in which the symbol under only one head (called read head) controls its move and other heads cannot distinguish the input symbols, they can only distinguish the end-marker from the other input symbols and they are called the blind head. We call such automaton a partially blind multihead automaton. We prove that partially blind k + 1-head finite automata are more powerful than such k-head finite automata. We show also that nondeterministic partially blind k-head finite automata languages are not closed under iteration and intersection for any k ≥ 2, and moreover, deterministic partially blind k head finite automata languages are not closed under intersection, union, complementation and reversal for any k ≥ 2. Finally we prove that deterministic partially blind k-head finite automata with endmarker are more powerful that such automata without endmarker for each k ≥ 4.

이진 삼차 재귀 신경망과 유전자 알고리즘을 이용한 문맥-자유 문법의 추론

이 논문은 이진 삼차 재귀 신경망(Binary Third-order Recurrent Neural Networks: BTRNN)에 유전자 알고리즘을 적용하여 문맥-자유 문법을 추론하는 방법을 제안한다. BTRNN은 각 입력심볼에 대응되는 재귀 신경망들의 다층적 구조이고 외부의 스택과 결합된다. BTRNN의 매개변수들은 모두 이진수로 표현되며 상태 전이와 동시에 스택의 한 동작이 실행된다. 염색체로 표현된 BTRNN들에 유전자 알고리즘을 적용하여 긍정과 부정의 입력 패턴들의 문맥-자유 문법을 추론하는 최적의 BTRNN를 얻는다. 이 방법은 기존의 신경망 이용방법보다 적은 학습량과 적은 학습회수로 작거나 같은 상태 수를 갖는 BTRNN을 추론한다. 또한 문법 표현의 염색체 이용방법보다 parsing과정에서 결정적인 상태전이와 스택동작이 실행되므로 입력 패턴에 대한 인식처리 시간복잡도가 우수하다. 문맥-자유 문법의 비단말 심볼의 개수 p, 단말 심볼의 개수 q, 그리고 길이가 k인 문자열이 입력이 될 때, BTRNN의 최대 상태수가 m이라고 하면, BTRNN의 인식처리 병렬처리 시간은 O(k)이고 순차처리 시간은 O(km)이다. We present the method to infer Context-Free Grammars by applying genetic algorithm to the Binary Third-order Recurrent Neural Networks(BTRNN). BTRNN is a multiple-layered architecture of recurrent neural networks, each of which is corresponding to an input symbol, and is combined with external stack. All parameters of BTRNN are represented as binary numbers and each state transition is performed with any stack operation simultaneously. We apply Genetic Algorithm to BTRNN chromosomes and obtain the optimal BTRNN inferring context-free grammar of positive and negative input patterns. This proposed method infers BTRNN, which includes the number of its states equal to or less than those of existing methods of Discrete Recurrent Neural Networks, with less examples and less learning trials. Also BTRNN is superior to the recent method of chromosomes representing grammars at recognition time complexity because of performing deterministic state transitions and stack operations at parsing process. If the number of non-terminals is p, the number of terminals q, the length of an input string k, and the max number of BTRNN states m, the parallel processing time is O(k) and the sequential processing time is O(km).

An Improved Full Rate Full Diversity QOSTBC with Linear Decoding in MIMO Systems

In this letter, we propose an improved Quasi-orthogonal space-time block code (QOSTBC) with full rate, full diversity, linear decoding and better peak-to-average power ratio (PAPR). Constellation rotation is used to the input symbol vector to ensure full diversity, and then the information symbol vector is interleaved in coordinates and pre-grouped by using a Given rotation matrix. The performance is evaluated by numerical experiments. The PAPR of our proposed QOSTBC is lower than that of CI-QOSTBC in Khan and Rajan (IEEE Trans Inf Theory 52(5):2062---2091, 2006). Meanwhile, the Bit-error-rate versus Signal-to-noise-ratio of our proposed QOSTBC is better than those of OSTBC (Tarokh et al. in IEEE Trans Inf Theory 45(5):1456---1467, 1999) QOSTBC (Jafarkhani in IEEE Trans Wirel Commun 49(1):1---4, 2001), G-QOSTBC (Park et al. in IEEE Commun Lett 12(12):868---870, 2008), slightly better that of the CI-QOSTBC, and as same as that of the recently proposed Minimum Decoding Complexity QOSTBCs (MDC-QOSTBC) in Yuen et al.(IEEE Trans Wirel Commun 4(5):2089---2098, 2005), Wang and Xia (IEEE Trans Inf Theory 55(3):1104---1130, 2009). Compared with MDC-QOSTBC, the proposed QOSTBC has simpler code construct and lower decoding complexity.

Multimodal Language Specification for Human Adaptive Mechatronics

Designing and building automated systems with which people can interact naturally is one of the emerging objective of Mechatronics. In this perspective multimodality and adaptivity represent focal issues, enabling users to communicate more freely and naturally with automated systems. One of the basic problem of multimodal interaction is the fusion process. Current approaches to fusion are mainly two: the former implements the multimodal fusion at dialogue management level, whereas the latter at grammar level. In this paper, we propose a multimodal attribute grammar, that provides constructions both for representing input symbols from different modalities and for modeling semantic and temporal features of multimodal input symbols, enabling the specification of multimodal languages. Moreover, an application of the proposed approach in the context of a multimodal language specification to control a driver assistance system, as robots using different integrated interaction modalities, is given.

The Degree of Consideration-Based Mechanism of Thought and Its Application to Artificial Creatures for Behavior Selection

To make artificial creatures deliberately interact with their environment like living creatures, a behavior selection method mimicking living creatures thought mechanism is needed. For this purpose, there has been research based on probabilistic knowledge links between input (assumed fact) and target (behavior) symbols for reasoning. However, real intelligent creatures including human beings select a behavior based on the multi-criteria decision making process using the degree of consideration (DoC) for input symbols, i.e. will and context symbols, in their memory. In this paper, the DoC-based mechanism of thought (DoC-MoT) is proposed and applied to the behavior selection of artificial creatures. The knowledge links between input and behavior symbols are represented by the partial evaluation values of behaviors over each input symbol, and the degrees of consideration for input symbols are represented by the fuzzy measures. The proposed method selects a behavior through global evaluation by the fuzzy integral, as a multicriteria decision making process, of knowledge link strengths with respect to the fuzzy measure values. The effectiveness of the proposed behavior selection method is demonstrated by experiments carried out with a synthetic character Rity in the 3D virtual environment. The results show that the artificial creatures with various characteristics can be successfully created by the proposed DoC-MoT. Moreover, training the created artificial creatures to modify their characteristics was more efficient in the DoC-MoT than the probability-based mechanism of thought (P-MoT), both in terms of the number of parameters to be set and the amount of time consumed.

All-Optical RZ-to-NRZ Conversion of Advanced Modulated Signals

A generic scheme for return-to-zero (RZ) to nonreturn-to-zero (NRZ) format conversion of optical signals is analyzed. It relies on a simple delay interferometer with frequency periodicity twice as high as the input symbol rate and a subsequent optical band-pass filter. Simulation results at 40 Gbaud indicate the compatibility of the technique with a variety of advanced modulation formats. RZ-to-NRZ conversion of 40 Gb/s differential phase shift keying signals is experimentally demonstrated with 1.5 dB power penalty compared to the back-to-back measurement.

Gaussian matrix-product states for coding in bosonic communication channels

The communication capacity of Gaussian bosonic channels with memory has recently attracted much interest. Here, we investigate a method to prepare the multimode entangled input symbol states for encoding classical information into these channels. In particular, we study the usefulness of a Gaussian matrix product state (GMPS) as an input symbol state, which can be sequentially generated although it remains heavily entangled for an arbitrary number of modes. We show that the GMPS can achieve more than 99.9% of the Gaussian capacity for Gaussian bosonic memory channels with a Markovian or non-Markovian correlated noise model in a large range of noise correlation strengths. Furthermore, we present a noise class for which the GMPS is the exact optimal input symbol state of the corresponding channel. Since GMPS are ground states of particular quadratic Hamiltonians, our results suggest a possible link between the theory of quantum communication channels and quantum many-body physics.

Tight bound on the length of distinguishing sequences for non-observable nondeterministic Finite-State Machines with a polynomial number of inputs and outputs

In this paper we show that the upper bound 2n−2 on the length of input sequences that distinguish two sets of states is tight for a non-observable NFSM with n states and a polynomial number of inputs and outputs. For each n⩾2, there exists a non-observable NFSM M with n states, a single input symbol, and n output symbols such that there are two sets of states in M which are not distinguishable by each input sequence of length 2n−3 but can be distinguished by an input sequence of length 2n−2.

A Programmable Transducer Self-Assembled from DNA.

A transducer consists of an input/output alphabet, a finite set of states, and a transition function. From an input symbol applied to a given state, the transition function determines the next state, and an output symbol. Using DNA, we have constructed a transducer that divides a number by 3. The input consists of a series of individually addressable 2-state DNA nanomechanical devices that control the orientations of a group of flat 6-helix DNA motifs; these motifs have edge domains tailed in sticky ends corresponding to the numbers 0 and 1. Three-domain DNA molecules (TX tiles) act as computational tiles that correspond to the transitions that the transducer can undergo. The output domain of these TX tiles contains sticky ends that also correspond to 0 or 1. Two different DNA tiles can chelate these output domains: A 5 nm gold nanoparticle is attached to the chelating tile that binds to 0-domains and a 10 nm gold nanoparticle is attached to the chelating tile that binds to 1-domains. The answer to the division is represented by the series of gold nanoparticles, which can be interpreted as a binary number. The answers of the computation are read out by examination of the transducer complexes under a transmission electron microscope. The start or end points of the output sequence can be indicated by the presence of a 15 nm gold nanoparticle. This work demonstrates two previously unreported features integrated in a single framework: [1] a system that combines DNA algorithmic self-assembly with DNA nanomechanical devices that control that input, and [2] the arrangement of non-DNA species, here metallic nanoparticles, through DNA algorithmic self-assembly. The nanomechanical devices are controlled by single-stranded DNA strands, allowing multiple input sequences to be applied to the rest of the system, thus guiding the algorithmic self-assembly to a variety of outputs.

에지 방향성 히스토그램과 HMM을 이용한 제스처 인식에 관한 연구

In this paper, the algorithm that recognizes the gesture by configuring the feature information obtained through edge orientation histogram and principal component analysis as low dimensional gesture symbol was described. Since the proposed method doesn`t require a lot of computations compared to the existing geometric feature based method or appearance based methods and it can maintain high recognition rate by using the minimum information, it is very well suited for real-time system establishment. In addition, to reduce incorrect recognition or recognition errors that occur during gesture recognition, the model feature values projected in the gesture space is configured as a particular status symbol through clustering algorithm to be used as input symbol of hidden Markov models. By doing so, any input gesture will be recognized as the corresponding gesture model with highest probability.