A buffer planning algorithm for chip-level floorplanning

This paper studies the buffer planning problem for interconnect-centric floorplanning for nanometer technologies. The dead-spaces are the spaces left unused within a placement that are not held by any circuit block. In this paper, we proposed a buffer planning algorithm based on dead space redistribution to make good use of dead-spaces for buffer insertion. Associated with circuit blocks under topological representations, the dead space can be redistributed by moving freely some circuit blocks within their rooms in the placement. The total area and the topology of the placement keep unchanged while doing the dead space redistribution. The number of nets satisfying the delay constraint can be increased by redistributing the dead space all over the placement, which has been demonstrated by the experimental results. The increment of the number of nets that meet delay constraint is 9% on an average.     

Deterministic VLSI block placement algorithm using Less Flexibility First principle

In this paper,a simple while effective deterministic algorithm for solving the VLSI block placement problem is proposed considering the packing area and interconnect wiring simultaneously.The algorithm is based on a principle inspired by observations of ancient professionals in solving their similar problems.Using the so-called Less Flexibility First principle,it is tried to pack blocks with the least packing flexibility on its shape and interconnect requirement to the empty space with the least packing flexibility in a greedy manner.Experimental results demonstrate that the algorithm,though simple,is quite effective in solving the problem.The same philosophy could also be used in designing efficient heuristics for other hard problems,such as placement with preplaced modules,placement with L/T shape modules,etc.     

Application of particle swarm optimization algorithm in bellow optimum design

In this paper, Particle Swarm Optimization Algorithm （PSOA） is used in problem of the bellow optimum design with constraints, in which the design variables are discrete. To implement bellow optimum design by PSOA, an augmented objective function is constructed based on penalty function and a new updating scheme of penalty parameter s is proposed. A new Discrete PSOA （DPSOA） is proposed. The mathematic model of bellow optimum design is established. Through numerical examples of bellow design, comparing the results of examples by proposed DPSOA with the theory solutions by Net method, it shows that the particle swarm optimization algorithm can be applied to the bellow optimum design successfully and satisfactory results by DPSOA are obtained, which is discrete optimal solution in the feasible domain.     

Pipe-assembly approach for ships using modified NSGA-Ⅱ algorithm

Pipe-routing for ship is formulated as searching for the near-optimal pipe paths while meeting certain objectives in an environment scattered with obstacles. Due to the complex construction in layout space, the great number of pipelines, numerous and diverse design constraints and large amount of obstacles, finding the optimum route of ship pipes is a complicated and time-consuming process. A modified NSGA-Ⅱ algorithm based approach is proposed to find the near-optimal solution to solve the problem. By simplified equipment models, the layout space is firstly divided into three dimensional(3D) grids to build its mathematical model. In the modified NSGA-Ⅱ algorithm, the concept of auxiliary point is introduced to improve the search range of maze algorithm(MA) as well as to guarantee the diversity of chromosomes in initial population. Then the fix-length coding mechanism is proposed, Fuzzy set theory is also adopted to select the optimal solution in Pareto solutions. Finally, the effectiveness and efficiency of the proposed approach is demonstrated by the contrast test and simulation. The merit of the proposed algorithm lies in that it can provide more appropriate solutions for the designers while subject certain constrains.     

Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory

This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters(λ and ξ), but the relation of them is not linear. So it is hard to find a best combination of λ and ξ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.     

Axis-coupled trajectory generation for chains of integrators through smoothing splines

Integrator based model is used to describe a wide range of systems in robotics. In this paper, we present an axis-coupled trajectory generation algorithm for chains of integrators with an arbitrary order. Special notice has been given to problems with pre-existing nominal plans, which are common in robotic applications. It also handles various type of constraints that can be satisfied on an entire time interval, including non-convex ones which can be transformed into a series of convex constraints through time segmentation. The proposed approach results in a linearly constrained quadratic programming problem, which can be solved effectively with off-the-shelf solvers. A closed-form solution is achievable with only the boundary constraints considered. Finally, the proposed method is tested in real experiments using quadrotors which represent high-order integrator systems.     

Fast evaluation of Bounded Slice-line Grid

Bounded Slice-line Grid (BSG) is an elegant representation of block placement, because it is very intuitionistic and has the advantage of handling various placement constraints. However, BSG has attracted little attention because its evaluation is very time-consuming. This paper proposes a simple algorithm independent of the BSG size to evaluate the BSG representation in O(nloglogn) time, where n is the number of blocks. In the algorithm, the BSG-rooms are assigned with integral coordinates firstly, and then a linear sorting algorithm is applied on the BSG-rooms where blocks are assigned to compute two block sequences, from which the block placement can be obtained in O(n log logn) time. As a consequence, the evaluation of the BSG is completed in O(nloglogn) time, where n is the number of blocks. The proposed algorithm is much faster than the previous graph-based O(n^2) algorithm. The experimental results demonstrate the efficiency of the algorithm.     

Web Classification Based on Latent Semantic Indexing

A new web document automatic classification algorithm based on Latent Semantic Indexing （LSIWAC）, is proposed in this paper. LSMAC uses the LSI based on Singular Value Decomposition （SVD） to compress the document vector space to lower dimensional space. Using the optimal clustering, LSIWAC can cluster part of web documents Then, LSIWAC uses the optimal discriminate transform to get feature vector from every clustering＇s discriminate features. Finally, it uses the conception classification algorithm to classify the rest documents. LSIWAC solves the high dimension problem and improves the precision of web classification.     

Second Attribute Algorithm Based on Tree Expression

One view of finding a personalized solution of reduct in an information system is grounded on the viewpoint that attribute order can serve as a kind of semantic representation of user requirements. Thus the problem of finding personalized solutions can be transformed into computing the reduct on an attribute order. The second attribute theorem describes the relationship between the set of attribute orders and the set of reducts, and can be used to transform the problem of searching solutions to meet user requirements into the problem of modifying reduct based on a given attribute order. An algorithm is implied based on the second attribute theorem, with computation on the discernibility matrix. Its time complexity is O（n^2 × m） （n is the number of the objects and m the number of the attributes of an information system）. This paper presents another effective second attribute algorithm for facilitating the use of the second attribute theorem, with computation on the tree expression of an information system. The time complexity of the new algorithm is linear in n. This algorithm is proved to be equivalent to the algorithm on the discernibility matrix.     

Universal triple I fuzzy reasoning algorithm of function model based on quotient space

Aiming at the deficiencies of analysis capacity from different levels and fuzzy treating method in product function modeling of conceptual design,the theory of quotient space and universal triple I fuzzy reasoning method are introduced,and then the function modeling algorithm based on the universal triple I fuzzy reasoning method is proposed.Firstly,the product function granular model based on the quotient space theory is built,with its function granular representation and computing rules defined at the same time.Secondly,in order to quickly achieve function granular model from function requirement,the function modeling method based on universal triple I fuzzy reasoning is put forward.Within the fuzzy reasoning of universal triple I method,the small-distance-activating method is proposed as the kernel of fuzzy reasoning;how to change function requirements to fuzzy ones,fuzzy computing methods,and strategy of fuzzy reasoning are respectively investigated as well;the function modeling algorithm based on the universal triple I fuzzy reasoning method is achieved.Lastly,the validity of the function granular model and function modeling algorithm is validated.Through our method,the reasonable function granular model can be quickly achieved from function requirements,and the fuzzy character of conceptual design can be well handled,which greatly improves conceptual design.     

Leakage current estimation of CMOS circuit with stack effect

Leakage current of CMOS circuit increases dramatically with the technology scaling down and has become a critical issue of high performance system. Subthreshold, gate and reverse biased junction band-to-band tunneling (BTBT) leakages are considered three main determinants of total leakage current. Up to now, how to accurately estimate leakage current of large-scale circuits within endurable time remains unsolved, even though accurate leakage models have been widely discussed. In this paper, the authors first dip into the stack effect of CMOS technology and propose a new simple gate-level leakage current model. Then, a table-lookup based total leakage current simulator is built up according to the model. To validate the simulator, accurate leakage current is simulated at circuit level using popular simulator HSPICE for comparison. Some further studies such as maximum leakage current estimation, minimum leakage current generation and a high-level average leakage current macromodel are introduced in detail. Experiments on ISCAS85 and ISCAS89 benchmarks demonstrate that the two proposed leakage current estimation methods are very accurate and efficient.     

Speech Detection in Non-Stationary Noise Based on the 1/f Process

In this paper,an effective and robust active speech detection method is proposed based on the 1/f process technique for signals under non-stationary noisy environments.The Gaussian 1/f process ,a mathematical model for statistically self-similar radom processes based on fractals,is selected to model the speech and the background noise.An optimal Bayesian two-class classifier is developed to discriminate them by their 1/f wavelet coefficients with Karhunen-Loeve-type properties.Multiple templates are trained for the speech signal,and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise.In our experiments,a 10-minute long speech with different types of noises ranging from 20dB to 5dB is tested using this new detection method.A high performance with over 90% detection accuracy is achieved when average SNR is about 10dB.     

Spontaneous Speech Parsing in Travel Information Inquiring and Booking Systems

Grammar-based parsing is a prevalent method for natural language understanding(NLU)and has been introduced into dialogue systems for spoken language processing (SLP).A robust parsing scheme is proposed in this paper to overcome the notorious phenomena,such as garbage,ellipsis,word disordering,fragment ,and ill-form,which frequently occur in splien utterances,Keyword categories are used as terminal symbols,and the definition of grammar is extended by introducing three new rule types,by-passing,up-messing and overcrossing,in addition to the general rules called up-tying in this paper,and the use of semantic items simplifies the semantics extraction.The corresponding parser marionette,which is essentially a partial chart parser,is enhanced to parse the semantic grammar.The robust parsing scheme integrating the above methods has been adopted in an air traveling information service system called EasyFlight,and has achieved a high performance when used for parsing spontaneous speeches.     

A new algebraic modelling approach to distributed problem-solving in MAS

This paper is devtoed to a new algebraic modelling approach to distributed problem-solving in multi-agent systems(MAS),which is featured by a unified framework for describing and treating social behaviors,social dynamics and social intelligence.A coneptual architecture of algebraic modelling is presented.The algebraic modelling of typical social be-haviors,social situation and social dynamics is discussed in the context of distributed problem-solving in MAS .The comparison and simulation on distributed task allocations and resource assignments in MAS show more advantages of the algebraic approach than other conventional methods.     

Learning-based tracking of complex non-rigid motion

This paper describes a novel method for tracking complex non-rigid motions by learning the intrinsic object structure. The approach builds on and extends the studies on non-linear dimensionality reduction for object representation, object dynamics modeling and particle filter style tracking. First, the dimensionality reduction and density estimation algorithm is derived for unsupervised learning of object intrinsic representation, and the obtained non-rigid part of object state reduces even to 2-3 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle filter style tracker. It is shown that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle filter style tracker more robust and reliable.Extensive experiments are done on the tracking of challenging non-rigid motions such as fish twisting with selfocclusion, large inter-frame lip motion and facial expressions with global head rotation. Quantitative results are given to make comparisons between the newly proposed tracker and the existing tracker. The proposed method also has the potential to solve other type of tracking problems.     

A new polynomial algorithm for a paralle identical scheduling problem

A precedence order is defined based on the release dates of jobs' direct successors. Using the defined precedence order and Heap Sort, a new polynomial algorithm is provided which aims to solve the parallel scheduling problem P|p = 1, r ,outtree|∑C . The new algorithm is shown to be more compact and easier to implement.     

Robustness Analysis of the IFN-ν Induced JAK-STAT Signaling Pathway

Here, the issue of robustness analysis of cell JAK-STAT signal transduction networks is addressed. This is investigated upon a mathematical model of IFN-γ induced JAK-STAT signaling pathway by applying robustness analysis which is based on a broad range of simultaneous and systematical parameters variation. The effects of the variations of the initial signal proteins' concentrations on the output of this system are also studied. The study demonstrates that the JAK-STAT signaling pathway is robust with respect to its "signal time" and "signal duration", but sensitive with respect to its "signal amplitude". These analysis results can point to experimental designs that can further test how the pathway activity can be perturbed.     

An efficient method for geo-referenced video mosaicing for environmental monitoring

Environmental monitoring applications require seamless registration of optical data into large area mosaics that are geographically referenced to the world frame. Using frame-by-frame image registration alone, we can obtain seamless mosaics, but it will not exhibit geographical accuracy due to frame-to-frame error accumulation. On the other hand, the 3D geo-data from GPS, a laser profiler, an INS system provides a globally correct track of the motion without error propagation. However, the inherent (absolute) errors in the instrumentation are large for seamless mosaicing. The paper describes an effective two-track method for combining two different sources of data to achieve a seamless and geo-referenced mosaic, without 3D reconstruction or complex global registration. Experiments with real airborne video images show that the proposed algorithms are practical in important environmental applications. Zhigang Zhu received his B.E., M.E. and Ph.D. degrees, all in computer science from Tsinghua University, Beijing, in 1988, 1991 and 1997, respectively. He is currently an associate professor in the Department of Computer Science, the City College of the City University of New York. Previously, he was an associate professor at Tsinghua University, and a senior research fellow at the University of Massachusetts, Amherst. His research interests include 3D computer vision, HCI, virtual/augmented reality, video representation, and various applications in education, environment, robotics, surveillance and transportation. He has published over 90 technical papers in the related fields. He is a member of IEEE and ACM. Edward M. Riseman received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as assistant professor in 1969, has been a professor since 1978, and served as chairman of the department from 1981 to 1985. Professor Riseman has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 200 publications. He has co-directed the Computer Vision Laboratory since its inception in 1975. Professor Riseman has been on the editorial boards of Computer Vision and Image Understanding (CVIU) from 1992 to 1997 and of the International Journal of Computer Vision (IJCV) from 1987 to the present. He is a senior member of IEEE, and a fellow of AAAI. Allen R. Hanson received his B.S. degree from Clarkson College of Technology in 1964 and his M.S. and Ph.D. degrees in electrical engineering from Cornell University in 1966 and 1969, respectively. He joined the Computer Science Department at UMass-Amherst as an associate professor in 1981, and has been a professor there since 1989. Professor Hanson has conducted research in computer vision, artificial intelligence, learning, and pattern recognition, and has more than 150 publications. He is co-director of the Computer Vision Laboratory at UMass-Amherst, and has been on the editorial boards of the following journals: Computer Vision, Graphics and Image Processing 1983–1990, Computer Vision, Graphics, and Image Processing—Image Understanding 1991–1994, and Computer Vision and Image Understanding 1995–present. Howard Schultz received a M.S. degree in physics from UCLA in 1974 and a Ph.D. in physical oceanography from the University of Michigan in 1982. Currently, he is a senior research fellow with the Computer Science Department at the University of Massachusetts, Amherst. His research interests include quantitative methods for image understanding and remote sensing. The current focus of his research activities are on developing automatic techniques for generating complex, 3D models from sequences of images. This research has found application in a variety of programs including real-time terrain modeling and video aided navigation. He is a member of the IEEE, the American Geophysical Union, and the American Society of Photogrammetry and Remote Sensing.     

A further note on overshoot estimation in pole placements

Recently, it is proved in the literature that for a given controllable pair （A, B） with A ∈R^n×n, B ∈R^n×m, and any λ ≥ 1, a gain matrix K can be designed so that ‖e^（A＋BK）t‖ ≤Mλ^Le^-λt, where M and L are constants independent of λ. Here, we show that M and L can be chosen much smaller than that proposed above. As a consequence, the estimation on overshoot of a transition matrix can be bounded more precisely. This can be regarded as a complement to the existing result.