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Encoding the shape of a binary tree is a basic step in a number of algorithms in integrated circuit design, automated theorem proving, and game playing. We propose cost-optimal parallel algorithms to solve the binary tree encoding/decoding problem. Specifically, we encode the relevant shape information of an n-node binary tree in a 2n bitstring. Conversely, given an arbitrary 2n bitstring we reconstruct the shape of the corresponding binary tree, if such a tree exists. All our algorithms run in O(log n) time using O(n/log n) processors in the EREW-PRAM model of computation.

Inter symbol interference (ISI) caused by dispersion in optical fibers is the major limiting factor on the achievable data rate or transmission distance in high-speed fiber-optic interconnects¹. Compared with optical-domain and other electrical-domain dispersion compensation methods, duobinary coding technology is used due to its higher spectral efficiency. In this paper, a monolithically integrated duobinary optical transmitter has be designed and fabricated in standard 0.18 μm CMOS technology. This transmitter consists of a duobinary encoder and a VCSEL driver. The master-slave D flip-flop structure is used to generate the wideband one bit delay which is important for the realization of duobinary signal. An open drain structure is used in VCSEL driver design to provide the higher current output in low supply voltage condition. This duobinary transmitter can operate up to 5 Gb/s with 20 mA current output. The current consumption is only 50 mA under 1.8 V supply voltage.

Data processing with multiple domains is an important concept in any platform; it deals with multimedia and textual information. Where textual data processing focuses on a structured or unstructured way of data processing which computes in less time with no compression over the data, multimedia data are processing deals with a processing requirement algorithm where compression is needed. This involve processing of video and their frames and compression in short forms such that the fast processing of storage as well as the access can be performed. There are different ways of performing compression, such as fractal compression, wavelet transform, compressive sensing, contractive transformation and other ways. One way of performing such a compression is working with the high frequency component of multimedia data. One of the most recent topics is fractal transformation which follows the block symmetry and archives high compression ratio. Yet, there are limitations such as working with speed and its cost while performing proper encoding and decoding using fractal compression. Swarm optimization and other related algorithms make it usable along with fractal compression function. In this paper, we review multiple algorithms in the field of fractal-based video compression and swarm intelligence for problems of optimization.

In contemporary superscalar microprocessors, issue queue is a considerable energy dissipating component due its complex scheduling logic. In addition to the energy dissipated for scheduling activities, read and write lines of the issue queue entries are also high energy consuming pieces of the issue queue. When these lines are used for reading and writing unnecessary information bits, such as the immediate operand part of an instruction that does not use the immediate field or the insignificant higher order bits of an immediate operand that are in fact not needed, significant amount of energy is wasted. In this paper, we propose two techniques to reduce the energy dissipation of the issue queue by exploiting the immediate operand files of the stored instructions: firstly by storing immediate operands in separate immediate operand files rather than storing them inside the issue queue entries and secondly by issue queue partitioning based on widths of immediate operands of instructions. We present our performance results and energy savings using a cycle accurate simulator and testing the design with SPEC2K benchmarks and 90 nm CMOS (UMC) technology.

The need of effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a reduced uncertainty-based hybrid evolutionary algorithm (RUBHEA) to solve dynamic shortest path routing problem (DSPRP) effectively and rapidly. Genetic algorithm (GA) and particle swarm optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks.

By applying nonlinear dynamics to the dense storage of information, we demonstrate how a single nonlinear dynamical element can store M items, where M is variable and can be large. This provides the capability for naturally storing data in different bases or in different alphabets and can be used to implement multilevel logic. Further we show how this method of storing information can serve as a preprocessing tool for (exact or inexact) pattern matching searches. Since our scheme involves just a single procedural step, it is naturally set up for parallel implementation and can be realized with hardware currently employed for chaos-based computing architectures.

Recent advances in solving constraint satisfaction problems (CSPs) and heuristic search have made it possible to solve classical planning problems significantly faster. There is an increasing amount of work on extending these advances to solving planning problems in more expressive languages. These problems and languages contain metric time, quantifiers and resource quantities. SAT-based approaches are very effective at optimal planning. In this paper we report on SAT-based temporal planner T-SATPLAN. One key challenge in the development of planners casting planning as SAT or CSP is the identification of constraints which are satisfied if and only if there is a plan of k steps. In this paper we show how such a SAT encoding can be synthesized for temporal planning. As part of this, we generalize explanatory frame axioms for two states of fluents (true, false) to three states of fluents (true, false and undefined). We show how this SAT encoding can be simplified. We discuss two additional SAT encodings of temporal planning. The encoding schemes make it easier to exploit progress in SAT and CSP solving to solve temporal planning problems. We also report on an experimental evaluation of T-SATPLAN using one such encoding scheme. The evaluation shows that significantly large SAT encodings of temporal planning problems can be solved extremely fast.

Many complex networks have scale-free and small-world effects. In this paper, a family of evolving networks is constructed modeled by a non-symmetric self-similar planar fractal, using the encoding method in fractal geometry. Based on the self-similar structure, we study the degree distribution, clustering coefficient and average path length of our evolving network to verify their scale-free and small-world characteristics.

The paper elaborates on the encoding and decoding of numerical and nonnumerical data. Proposed are general criteria leading to the distortion-free interfacing mechanisms that help transform information between the systems (or modelling environments) operating at different levels of information granularity. Distinguished are three basic categories of information: numerical, interval-valued, and linguistic (fuzzy). As all of them are dealt with here, the paper subsumes the current studies concentrated exclusively on representing fuzzy sets through their numerical representatives (prototypes). The algorithmic framework in which the distortion-free interfacing is completed is realized through neural networks. Each category of information is treated separately and gives rise to its own specialized architecture of the neural network. Similarly, these networks require carefully designed training sets that fully capture the specificity of the reconstruction problem. Several carefully selected numerical examples are aimed at the illustration of the key ideas.

To better analyze human posture in ice sports, a method based on deep learning has been established. In this method, the input gate, forget gate and output gate are configured to make them more suitable for human pose analysis relying on the LSTM network framework. The input data of LSTM are encoded using the sequence of human skeleton and joint angles in ice skating. During the experiment, abstract models of human bones and joints were provided, with continuous jumping movements in figure skating as the analysis object. LSTM provided detailed analysis results. The experimental results show that the accuracy of deep learning continues to improve in analyzing human posture during ice sports with the number of iterations increasing.

CSS codes are a subfamily of stabilizer codes especially appropriate for fault-tolerant quantum computations. A very simple method is proposed to encode a general qudit when a Calderbank–Shor–Steane quantum code, defined over a q-ary alphabet, is used.

Inter symbol interference (ISI) caused by dispersion in optical fibers is the major limiting factor on the achievable data rate or transmission distance in high-speed fiber-optic interconnects¹. Compared with optical-domain and other electrical-domain dispersion compensation methods, duobinary coding technology is used due to its higher spectral efficiency. In this paper, a monolithically integrated duobinary optical transmitter has be designed and fabricated in standard 0.18 μm CMOS technology. This transmitter consists of a duobinary encoder and a VCSEL driver. The master-slave D flip-flop structure is used to generate the wideband one bit delay which is important for the realization of duobinary signal. An open drain structure is used in VCSEL driver design to provide the higher current output in low supply voltage condition. This duobinary transmitter can operate up to 5 Gb/s with 20 mA current output. The current consumption is only 50 mA under 1.8 V supply voltage.

Photonic crystals containing multiple rugate structure are prepared by applying a composite waveform summed three computer-generated pseudo-sinusoidal current waveforms. They exhibit three sharp photonic band gaps in the optical reflectivity spectrum. Three types of rugate PSi prepared in this study. Typically etched PSi, oxidized PSi, and hydrosilylated PSi. The rugate PSi films have been removed from the silicon substrate by applying a lift-off current and are then made into particles by ultrasono-method in an organic solution. The sensing experiments using these particles for organic solvents such as methanol and hexane have been achieved. Condensing of organic vapors in the pores increases the refractive indices of entire particle which results a red shift in the photonic peaks.

In this paper, inspired by the idea of Non-symmetry and Anti-packing Model (NAM) representation method, we proposed an improved algorithm for the gray image representation by using the Line Non-symmetry and Anti-packing Model (LNAM), which is called the LNAM image representation method. Also, we present a LNAM representation algorithm of gray images, Storage structure and data analysis. By comparing our proposed LNAM method with the conventional linear quadtree (LQT) method, the experimental results presented in this paper show that the former can significantly reduce the lower bit rate and the number of storing node than the latter whereas remaining the satisfactory image quality.

There has evolved a conventional approach to analysis of the effect of brain dysfunction, induced by anatomical damage or chemical disruption, on learning and memory. The results have indicated that brain dysfunction impairs some instances of learning and memory, but not all. Given verification that the sensory detection of events to be learned is not impaired, the conventional conclusion has been that brain dysfunction can disrupt the operation of some systems of learning and memory but leave others quite intact.

The clear and often dramatic dissociation between how the CNS is impaired and what aspects of learning and memory are impaired has had an appropriately forceful impact that has shaped this conventional analysis. Yet this analysis, and the general case for separate memory systems, is weakened by an often-overlooked consequence of brain dysfunction: the enhancement of learning and memory that sometimes occurs. Some instances of this enhancement are dismissible as method-specific artifacts that are relatively uninteresting, such as lesion-induced increases in activity imposed on learning that is especially indicated by high levels of activity. Yet the number and variety of enhancement examples argue for their serious consideration and for the reality of enhanced memory function as one consequence of brain dysfunction.

One could conceivably deal with this in terms of a multiple-memory-systems approach, but new and untested assumptions would be necessary. A much simpler form of explanation is possible. Perhaps it is not so much entire memory systems that are affected by brain damage, but subprocesses of the one (or perhaps more) memory system(s). We have in mind subprocesses such as orienting, attention, encoding and retrieval, with present emphasis on changes in encoding that lead to corresponding changes in what is learned and remembered. How might the array of memory changes induced by brain dysfunction be accounted for in these terms? Considerations of recent studies of the ontogeny of learning and memory provide a guide.

Similar to the case of brain dysfunction and memory (although by no means directly analogous), animals and people with a relatively immature brain during the early phases of ontogeny typically have been less effective in learning and memory than are those in later stages of ontogeny. This, too, has been explained by some theorists in terms of the differential availability of certain memory systems; the notion is that early in ontogeny, the critical memory systems needed for effective learning and memory have not yet developed. Like the case of general memory dysfunction, however, this interpretation is complicated by a variety of instances of more effective learning early in ontogeny than later on. An alternative interpretation focusing on ontogenetic changes in encoding and stimulus selection has proven useful. Perhaps it would be equally useful to consider similarly the encoding changes that result from general brain dysfunction.