Search Speed Research Articles

The analogy of matchline sensing techniques for content addressable memory (CAM)

Performance of a memory depends on the storage stability, yield and sensing speed. Differential input and the latching time of sense amplifiers are considered as primary performance factors in static random access memory. In a content addressable memory (CAM), the sensing is carried out through the matchline (ML) and the time for evaluation is the key to decide the search speed. The density of CAM is on a rise to accommodate a higher amount of information which increases the power dissipation associated with it. Issues such as the logical threshold variation and lower noise margin between match and mismatch are critical in the operation of a CAM. A good ML sensing technique can reduce the ML power with enhanced evaluation speed. This work provides an analogy of various ML sensing techniques based on their pre-charging, evaluation and performance improvement strategies. Estimation on the power dissipation and evaluation time are made and in-depth analysis on their power-speed-overhead trade-off are carried on 64-bit CAM macros.

Multipopulation harmony search algorithm for the detection of high-order SNP interactions.

Recently, multiobjective swarm intelligence optimization (SIO) algorithms have attracted considerable attention as disease model-free methods for detecting high-order single nucleotide polymorphism (SNP) interactions. However, a strict Pareto optimal set may filter out some of the SNP combinations associated with disease status. Furthermore, the lack of heuristic factors for finding SNP interactions and the preference for discrimination approaches to disease models are considerable challenges for SIO. We compared MP-HS-DHSI with four state-of-the-art SIO algorithms for detecting high-order SNP interactions for 20 simulation disease models and a real dataset of age-related macular degeneration. The experimental results revealed that our proposed method can accelerate the search speed efficiently and enhance the discrimination ability of diverse epistasis models. https://github.com/shouhengtuo/MP-HS-DHSI. Supplementary data are available at Bioinformatics online.

An Improved Underwater Electric Field-Based Target Localization Combining Subspace Scanning Algorithm And Meta-EP PSO Algorithm

In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of the electric field-based underwater target locating problem. The meta-EP PSO method is used to select M elite particles by the q-tournament selection method, which could effectively reduce the computational complexity of the three-dimensional underwater target localization. Moreover, the proposed meta-EP PSO optimization algorithm can avoid subspace scanning trapping into local minima. We also analyze the positioning performance of the uniform circular and cross-shaped electrodes arrays by using the subspace scanning algorithm combined with meta–EP PSO. According to the simulation, the calculation amount of the proposed algorithm is greatly reduced. Moreover, the positioning accuracy is effectively improved without changing the positioning accuracy and search speed.

Improved voltage scanning method for maximum power point under partial shadow conditions

This paper proposes a novel maximum power point tracking (MPPT) algorithm with global search. The algorithm combines global voltage sweep and incremental conductance method, and introduces a Judging factor as a criterion for global voltage scanning. The Judging factor is compared with the threshold and the scan step size is selected based on the result. Take a large step when far from the peak. When approaches the peak, uses the incremental conductance method to find the local extrema and store it, then continues scanning. When the output power is close to zero, the scan is complete, Comparing all local extrema points then the global maximum power point will be found by this algorithm. Matlab simulation results show that the algorithm can find the global maximum and the search speed is fast.

Research on quorum sensing particle swarm optimization based on chaos

particle swarm optimization (PSO) is a kind of swarm optimization algorithm with fast search speed, high efficiency and suitable for practical optimization problems. Once it was put forward, it has attracted extensive attention of scholars in various fields. A quorum sensing particle swarm optimization (CPSOQS) algorithm based on chaos is proposed to solve the shortcomings of traditional PSO algorithms, such as poor handling of discrete optimization problems and the loss of searching ability due to the fast particle velocity decline in the later stage. Firstly, the search ability of the algorithm is improved by generating chaotic sequences and mapping them to the solution space of the definition domain; Secondly, the quorum sensing mechanism of bacteria is introduced into PSO. Chaos search was used twice in the algorithm, which improved the global search ability. CEC 2005 benchmark is used to test the performance of the algorithm. The experimental results show that among the 14 selected test functions, 11 test functions have better experimental results than the comparison algorithm.

An adaptive integral separated proportional–integral controller based strategy for particle swarm optimization

Particle swarm optimization algorithm (PSO), which updates the particle by the linear summation of the particle’s past momentum and current search direction, has demonstrated its power in many optimization applications. However, few researches have focused on the overshoot problem of PSO caused by past momentum, which may result in an oscillation search and slow convergence speed on complex optimization problems. Based on the connection between the PSO optimization process and the PID controller based control system, we first analyze the effect of the momentum in PSO, then we find that the oscillation problem relates to the gathering of the momentum term and the current search direction. Inspired by the conditional integration in automatic control, we propose an adaptive search direction learning approach for PSO, namely ISPSO (Integral Separated PI controller-based PSO). The ISPSO separates momentum term adaptively when the current search direction is consistent with historical momentum direction, and then the IS strategy will guide particles to fly to better and steady directions by eliminating the integral gathering of historical momentum. We select seven main-stream approaches as control group, and conduct experiments on benchmark CEC2013 test suite. The experimental results of ISPSO, providing the faster steady global convergence and higher solution accuracy, show a significant improvement on PSO algorithm. Compared with the results of the seven methods, the performance of ISPSO is also promising in general, especially for unimodal and composition functions. Furthermore, our results validate the generalization and effectiveness of IS strategy by the designed experiments of PSO variants with and without IS strategy, which also indicates that the IS strategy can be applied to PSO variants with any topological structures.

Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration.

Real-time detection of energy consumption of IoT network nodes based on artificial intelligence

Low power loss networks and wireless sensor networks are important components of the Internet of Things. With the development and popularization of artificial intelligence, the network application of wireless sensors is gradually scaled up and industrialized. Aiming at the problems of high energy consumption of the routing strategy, the algorithm protocol is easy to fall into the local optimum and the uneven energy consumption of network nodes. This paper proposes a regional energy balance routing algorithm based on intelligent chaotic ant colony. Based on the intelligent chaotic ant colony algorithm, combined with the remaining energy factors of wireless sensor network nodes, this paper propose a neighbor selection strategy. In order to enhance the ant search ability and speed up the algorithm convergence, an adaptive perturbation strategy and algorithm termination conditions are proposed respectively to find the global optimal solution and avoid falling into the local optimal solution. Simulation results show that the routing planning method in this paper has obvious advantages in terms of network energy consumption and network delay, node equilibrium energy distribution, network life cycle and other performance. Compared with similar algorithms, the average energy consumption has been saved by 7.3%.

TopoFilter: a MATLAB package for mechanistic model identification in systems biology

BackgroundTo develop mechanistic dynamic models in systems biology, one often needs to identify all (or minimal) representations of the biological processes that are consistent with experimental data, out of a potentially large set of hypothetical mechanisms. However, a simple enumeration of all alternatives becomes quickly intractable when the number of model parameters grows. Selecting appropriate dynamic models out of a large ensemble of models, taking the uncertainty in our biological knowledge and in the experimental data into account, is therefore a key current problem in systems biology.ResultsThe TopoFilter package addresses this problem in a heuristic and automated fashion by implementing the previously described topological filtering method for Bayesian model selection. It includes a core heuristic for searching the space of submodels of a parametrized model, coupled with a sampling-based exploration of the parameter space. Recent developments of the method allow to balance exhaustiveness and speed of the model space search, to efficiently re-sample parameters, to parallelize the search, and to use custom scoring functions. We use a theoretical example to motivate these features and then demonstrate TopoFilter’s applicability for a yeast signaling network with more than 250’000 possible model structures.ConclusionsTopoFilter is a flexible software framework that makes Bayesian model selection and reduction efficient and scalable to network models of a complexity that represents contemporary problems in, for example, cell signaling. TopoFilter is open-source, available under the GPL-3.0 license at https://gitlab.com/csb.ethz/TopoFilter. It includes installation instructions, a quickstart guide, a description of all package options, and multiple examples.

Towards Large-Scale Object Instance Search: A Multi-Block N-Ary Trie

Object instance search is a challenging task with a wide range of applications, but the fast search with high accuracy has not been well solved yet. In this paper, we investigate the object instance search from a new perspective in terms of joint precision and computational cost optimization, and propose a novel index structure i.e., Multi-Block N-ary Trie (MBNT) to accelerate the exact r-neighbor search in the Hamming space. Comprehensive studies are first carried out to reveal the performance of exact and approximate nearest neighbor (NN) algorithms for object instance search. An interesting finding that the exact search is more promising for very compact binary codes (e.g., 64-bit and 128-bit) is analyzed. Along this vein, we introduce a Trie structure, i.e., MBNT, which is specifically designed for improving the exact NN search performance in the context of large-scale object instance search. To index the binary codes, a subset of continuous bits of a binary string, denoted as a block, is regarded as an atomic indexing element. As such, the problem of lookup misses can be addressed. Theoretical analyses are also provided to show that our MBNT scheme can incur less computational cost than other hash table-based methods. Extensive experimental results on the 100M dataset have demonstrated that our method achieves faster search speed while maintaining the promising search precision towards large-scale object instance search.

Modified Atom Search Optimization Based on Immunologic Mechanism and Reinforcement Learning

Atom search optimization algorithm has good searching ability and has been successfully applied to calculate hydrogeological parameters and groundwater dispersion coefficient. Since the atom search optimization algorithm is only based on the atom force motion model in molecular dynamics, it has some shortcomings such as slow search speed and low precision during the later stage of iteration. A modified atom search optimization based on the immunologic mechanism and reinforcement learning is proposed to overcome the abovementioned shortcomings in this paper. The proposed algorithm introduces a vaccine operator to better utilize the dominant position in the current atom population so that the speed, accuracy, and domain search ability of the atom search optimization algorithm can be strengthened. The reinforcement learning operator is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability. The test results of 21 benchmark functions confirm that the performance of the proposed algorithm is superior to seven contrast algorithms in search accuracy, convergence speed, and robustness. The proposed algorithm is used to optimize the permutation flow shop scheduling problem. The experimental results indicate that the proposed algorithm can achieve better optimization results than the seven comparative algorithms, so the proposed algorithm has good practical application value.

A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis

The aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack optimization algorithm based on the quantum theory. In the new method, wolves are coded by Bloch spherical coordinates of quantum bits, updated by quantum revolving gates, and selectively adaptively mutated when performing poorly. Because of the three-coordinate characteristics of the sphere, the number of global optimum solutions is greatly expanded and ultimately can be searched with a higher probability. Selective mutation enhances the robustness of the algorithm and improves the search speed. Furthermore, because the size of each dimension of Bloch spherical coordinates is always [−1, 1], the variables transformed by solution space must satisfy the constraints of the aperture of arrays and the minimum distance between adjacent elements, which effectively avoids infallible solutions in the process of updating and mutating the position of the wolf group, reduces the judgment steps, and improves the efficiency of optimization. The validity and robustness of the proposed method are verified by the simulation of two typical examples, and the optimization efficiency of the proposed method is higher than the existing methods.

An Artificial Neural Network Assisted Optimization System for Analog Design Space Exploration

This article presents a new analog circuit optimization system for automated sizing of analog integrated circuits. It consists of a genetic algorithm (GA)-based global optimization engine and an artificial neural network (ANN)-based local optimization engine. The key new idea is to use parallel computation to train ANN models for design space neighborhoods thus the local minimum search (LMS) can have a much faster search speed. For the GA-based global optimization, circuit performances are calculated by parallel SPICE simulations. For the LMS, circuit performance data are derived from ANN model predictions instead of SPICE simulations. Since the most time for an ANN-based LMS is spent on SPICE calls which can be run in parallel, the LMS process can also exploit the multiple core configuration of a modern computational server in addition to the GA global search. The fully parallelized optimization system is deployed to design a two-stage rail-to-rail operational amplifier and a fifth-order active-RC Chebyshev complex band-pass filter. The experimental results show that the proposed method provides about four times speed enhancement and comparable results compared with traditional approaches employing the same parallel global optimization but sequential SPICE calls during LMS.

Comparative Analysis of Parameter Extractor for Low-Power Precomputation Based Content Addressable Memory

Content Addressable Memory (CAM) is used in high speed searching applications and also in data compression. Recently in the network computing era, fast lookup tables are required for address resolution in network switches and routers such as LAN bridges/switches, ATM switches, and layer-3 switches. High search speed is obtained by parallel comparison. But power consumption is high in case of parallel comparison. In precomputation based CAM (PB-CAM) the comparison operation is divided into two stages to reduce the massive comparison operations in data searches. Precomputation is the way of comparing the extracted parameter from the input data in the first stage. The second stage comparison is done between the matched output of the first stage and the input data. In this paper, a Remainder Function parameter extractor is proposed as a precomputation technique to enhance the performance of low power PB-CAM. The experimental results obtained using 90 nm CMOS technology in the Cadence virtuoso show that the proposed approach achieves an average of 91% in power reduction.

Bibliometric analysis of patent infringement retrieval model based on self-organizing map neural network algorithm

PurposeThe purpose of this paper is to quickly retrieve the same or similar patents in a large patent database.Design/methodology/approachThe research is carried out through the analysis of the issue of patent examination, the type of patent infringement search and theories related to patent infringement determination and text mining.FindingsThe results show that the model improves the speed of patent search. It can quickly, accurately and comprehensively retrieve the same or equivalent patents as the imported patent claims.Research limitations/implicationsThe patent infringement detection mainly focuses on the measurement of patent similarity in the implementation method. It is not mature, and there is still much room for improvement in research.Practical implicationsThe model improves the efficiency of patent infringement detection, increases the accuracy of detection and protects the interests of patent stakeholders.Originality/valueThis study has great significance for improving the efficiency of patent examiners.

Fast Query-by-example Speech Search using Attention-based Deep Binary Embeddings

State-of-the-art query-by-example (QbE) speech search approaches usually use recurrent neural network (RNN) based acoustic word embeddings (AWEs) to represent variable-length speech segments with fixed-dimensional vectors, and thus simple cosine distances can be measured over the embedded vectors of both the spoken query and the search content. In this paper, we aim to improve search accuracy and speed for the AWE-based QbE approach in low-resource scenario. First, multi-head self-attentive mechanism is introduced for learning a sequence of attention weights for all time steps of RNN outputs while attending to different positions of a speech segment. Second, as the real-valued AWEs suffer from substantial computation in similarity measure, a hashing layer is adopted for learning deep binary embeddings, and thus binary pattern matching can be directly used for fast QbE speech search. The proposed approach of self-attentive deep hashing network is effectively trained with three specifically-designed objectives: a penalization term, a triplet loss, and a quantization loss. Experiments show that our approach improves the relative search speed by 8 times and mean average precision (MAP) by 18.9%, as compared with the previous best real-valued embedding approach.

Supervised Matrix Factorization Hashing With Quantitative Loss for Image-Text Search

Image-text hashing approaches have been widely applied in large-scale similarity search applications due to their efficiency in both search speed and storage efficiency. Most recent supervised hashing approaches learn a hash function by constructing a pairwise similarity matrix or directly learning the hash function and hash code (i.e.,1 or -1) procedure based on class labels. However, the former suffers from high training complexity and storage cost, and the latter ignores the semantic correlation of the original data, both of which prevent discriminative hash codes. To this end, we propose a novel discrete hashing algorithm called supervised matrix factorization hashing with quantitative loss (SMFH-QL). The proposed SMFH-QL first generates hash codes via the class label, avoiding the construction of a pairwise similarity; then, matrix factorization is used to design hash codes from original image-text data, thereby eliminating the impact of class labels and reducing the quantization error. Moreover, we introduce a quantitative loss function term to learn hash codes by incorporating class labels and the original data information, facilitating learning a similarity-preserving hash function in image-text search. Extensive experiments show that SMFH-QL outperforms several existing hashing methods on three representative datasets.

Software TLB Management Method Based on Balanced Binary Tree

Abstract Purpose: with the development of the computer system, there is a higher request to reduce the failure times of Translation Look—aside Buffer and relieve the failure influence, normal solution is deal with TLB failure by software or hardware, it find the page table and then implement the index operation to locate the page want. Method: In order to satisfy the needs of software management method for mapping speed from virtual address to physical address and to expand the size of TLB, our team design a software management method based on balanced binary search tree. Page management is implemented in software management, TLB is managed by operating system based on abstract model, and MMU (memory management unit) is no longer used Unit, our team build a balanced binary tree to search TLB. Result: Binary search tree has the advantage of pruning in the interval search problem under the balanced state. Therefore, searching TLB by this method is conducive to the expansion of TLB capacity, making space for cache and other performance improvement designs on chip and reducing costs. However, the search speed is reduced and some time is sacrificed to free up CPU space. Conclusion: This method can free up CPU design space, also can expand more size of TLB and reduce the cost, its optimization algorithm is worthy of further research.

A Novel Category Group Index Mechanism for Efficient Ranked Search of Encrypted Cloud Data

Nowadays, searchable encryption technology has become the focus of research. Owing to the enormous growth in data storage capacity arising from individual users, business organizations, enterprises and government agencies, it will take these data owners a great deal of time to construct and update indexes in the field of encrypted search in untrusted cloud environment. This has become an urgent problem to be solved. In this paper, based on multiple keywords ranked search, a novel category group index mechanism is proposed. By classifying documents, we create group vectors in each index which can not only transform a high-dimensional secret key into several low-dimensional keys to accelerate the process of encrypting indexes but also improve the flexibility of adding, modifying or deleting documents. When updating indexes, only the group vectors corresponding to the changed category keyword sets need to be updated. During the retrieval process of the proposed mechanism, a novel “targeted search” method is designed. With this method, instead of calculating the whole products, cloud server only needs to calculate some inner products of group vectors corresponding to query keywords in the trapdoor and each index to improve the search speed and efficiency. Extensive theoretical analysis and experiment results demonstrate that the method is more feasible and more effective than the compared schemes.

Application research of ant colony cellular optimization algorithm in population evacuation path planning

With the improvement of people's living standards, large-scaled public activities have increased considerably, and the emergency probability has increased greatly. When an emergency occurs, the emergency evacuation can effectively reduce casualties and economic losses. Therefore, how to quickly evacuate crowd is a current research hotspot in this field. The path planning of emergency evacuation is one of the effective ways to implement the crowd evacuation. Aiming at the problem of path planning for emergency evacuation and taking the grid map as the background, the ant colony cellular optimization (ACCO) algorithm is proposed as the path planning algorithm based on the cellular automata theory and ant colony algorithm. Firstly, in order to solve the problem of inconsistent time steps in the quadrilateral grid map, the grid map based on hexagonal cell is established and the ACCO algorithm is developed based on the hexagonal grid map. And the method of solving grid coordinate is given. Then, in order to improve the convergence speed and search ability of the ACCO algorithm, the static field is used to optimize the heuristic function, and the segment update rule is used to optimize the pheromone update method. Finally, the parameters of ACCO algorithm are optimized through the particle swarm optimization (PSO) algorithm. The method of designing the fitness evaluation function is proposed, and the optimal combination of parameters of the ACCO algorithm is implemented according to the fitness function. In order to verify the scientificity and effectiveness of the algorithm proposed in this research and also to systematically verify the optimization strategy, in this research the exhibition hall on the B-deck of a large cruise ship is used as the engineering background, and the traditional algorithm and the ACCO algorithm are adopted to perform the simulations. The simulation results show that compared with the traditional quadrilateral grid, the hexagonal grid proposed in this research unifies the simulation time step and can be used as the division method of the simulation environment. At the same time, the ACCO algorithm can effectively perform the evacuation path planning, and the optimization strategy proposed in this research not only acceletates the search speed, but also increases the solution space and improves the search ability, which can effectively avoid falling into the local optimal solution.