Linked List Data Structure Research Articles

Linked List Data Structure and Library Management System

Data Structure and Library Management System, focuses on the implementation of a linked list data structure for a library management, aiming to address the efficiency and functionality of managing library resources through the use of linked lists. The paper explores the advantages of using linked lists in the context of a library management system, discussing how this data structure can optimize operations such as adding or removing books from the library, tracking borrowing history, and managing book availability. By utilizing a linked list data structure, the library management system can efficiently organize and manipulate its resources, leading to improved search and retrieval times. The implementation of a linked list data structure in a library management system streamlines processes, enhances resource management, and ultimately improves the overall efficiency of the system.

An adaptive lattice boltzmann method based on nodal linked-list data structure

An adaptive mesh refinement (AMR) technique based on nodal linked-list data structure for lattice Boltzmann method (LBM) is proposed in this paper. It makes connections between nodal linked-list data structure which is hierarchical and LBM calculation to retain the simplicity of LBM. Based on uniform meshes, the adaptive algorithm refines the meshes by constructing the linked-list of nodes and levels for mesh levels refined. In the nodal linked-list data structure, the relationships for nodal neighbors and upper and lower level indexes of nodes are appointed to help implement the LBM process in the different mesh levels. To guarantee precise transmission of flow field information and numerical stability in the different mesh levels, the average interpolation for temporal and spatial space is used, which chimes in with the accuracy of LBM itself. To verify the present method, flows over square cylinder at Re=30 and circle cylinder at Re=200 are carried out.

Extrapolating linked list data structures to 3rd dimension

Extrapolating linked list data structures to 3rd dimension

An adaptive refined grid search strategy for assessing operational flexibility and application on refrigerant selection

AbstractA novel adaptive refined grid search strategy is developed for representative characterization of process feasible region boundaries and accurate estimation of its hypervolume. In particular, a linked list data structure adopted from the field of computer science is used to maintain the grid connectivity information. A uniform perturbation scheme is used to refine the search only near the boundaries. The volumetric flexibility index (FIV) is calculated directly from a summation of feasible hypercubes in the grid, without the need to apply shape reconstruction techniques. The proposed adaptive grid search strategy can capture complex region shapes with reduced sampling costs and without randomness, increasing reproducibility. Operational flexibility is optimized traditionally at a process scale. A case study on refrigerant selection is presented to demonstrate that the developed strategy can be combined within a computer‐aided molecular design framework for operational flexibility optimization at the molecular scale.

A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.

Efficient local locking for massively multithreaded in-memory hash-based operators

The join and group-by aggregation are two memory intensive operators that are affecting the performance of relational databases. Hashing is a common approach used to implement both operators. Recent paradigm shifts in multi-core processor architectures have reinvigorated research into how the join and group-by aggregation operators can leverage these advances. However, the poor spatial locality of the hashing approach has hindered performance on multi-core processor architectures which rely on using large cache hierarchies for latency mitigation. Multithreaded architectures can better cope with poor spatial locality by masking memory latency with many outstanding requests. Nevertheless, the number of parallel threads, even in the most advanced multithreaded processors, such as UltraSPARC, is not enough to fully cover the main memory access latency. In this paper, we explore the hardware re-configurability of FPGAs to enable deeper execution pipelines that maintain hundreds (instead of tens) of outstanding memory requests across four FPGAs-drastically increasing concurrency and throughput. We present two end-to-end in-memory accelerators for the join and group-by aggregation operators using FPGAs. Both accelerators use massive multithreading to mask long memory delays of traversing linked-list data structures, while concurrently managing hundreds of thread states across four FPGAs locally. We explore how content addressable memories can be intermixed within our multithreaded designs to act as a synchronizing cache, which enforces locks and merges jobs together before they are written to memory. Throughput results for our hash-join operator accelerator show a speedup between 2times and 3.4times over the best multi-core approaches with comparable memory bandwidths on uniform and skewed datasets. The accelerator for the hash-based group-by aggregation operator demonstrates that leveraging CAMs achieves average speedup of 3.3times with a best case of 9.4times in terms of throughput over CPU implementations across five types of data distributions.

Dislocation Dynamics Simulations of Dislocation-Particle Bypass Mechanisms

Effect of precipitation strengthening on metal is generally attributed to the dislocation interaction with the precipitate which acts as the barrier to the dislocation motion on the slip plane. In order to achieve better understanding of critical events of dislocation motion and evolution of dislocation microstructure, we have developed numerical simulation method of dislocation-dislocation and dislocation-particle interactions by means of discrete dislocation dynamics at mesoscopic scale. In this work, Green’s function method is utilized for the computation of the stress fields of dislocation and misfitting particle, and the interaction forces acting on the dislocation. We also proposed the efficient algorithm of the connectivity vector for the dislocation line elements, linked-list data structure, to deal with the flexible interaction of dislocation line elements. The geometrical effect of dislocation slip planes on the dislocation bypassing behaviors is tested by changing the relative height of dislocation slip plane against the center plane of spherical particle, where cross slip event is also taken into account for the dislocation motion. Simulation results show a wide variety of topological changes of dislocation during motion on the slip planes around the particle, which results from the stress field of the particle varied with the relative height between the dislocation slip plane and center plane of particle. The full analysis of the mechanisms of dislocation line bypassing misfitting particle has been explained in this study.

Distributed mining of high utility time interval sequential patterns using mapreduce approach

High Utility Sequential Pattern mining (HUSP) algorithms aim to find all the high utility sequences from a sequence database. Due to the large explosion of data, recently few distributed algorithms have been designed for mining HUSPs based on the MapReduce framework. However, the existing HUSP algorithms such as USpan, HUS-Span and BigHUSP are able to predict only the order of items, they do not predict the time between the items, that is, they do not include the time intervals between the successive items. But in a real-world scenario, time interval patterns provide more valuable information than conventional high utility sequential patterns. Therefore, we propose a distributed high utility time interval sequential pattern mining (DHUTISP) algorithm using the MapReduce approach that is suitable for big data. DHUTISP creates a novel time interval utility linked list data structure (TIUL) to efficiently calculate the utility of the resulting patterns. Moreover, two utility upper bounds, namely, remaining utility upper bound (RUUB) and co-occurrence utility upper bound (CUUB) are proposed to prune the unpromising candidates. We conducted various experiments to prove the efficiency of the proposed algorithm over both the distributed and non-distributed approaches. The experimental results show the efficiency of DHUTISP over state-of-the-art algorithms, namely, BigHUSP, AHUS-P, PUSOM and UTMining_A.

An adaptive-gridding lattice Boltzmann method with linked-list data structure for two-dimensional viscous flows

An adaptive-gridding lattice Boltzmann method with linked-list data structure for two-dimensional viscous flows

Towards a complete chaotic map‐based mobile RFID authentication protocol with efficient anticollision tag indexing

AbstractRadio frequency identification technology is one of the modern popular emerging technologies proven to advance a wide range of applications in both academia and industry. However, potential security risks, privacy issues, and efficiency are still open challenges that must be addressed seriously to achieve enhanced protection. In this paper, we show that the Akgün et al. protocol remains unsafe and imperfect in terms of the security issue and database identification efficiency. We, first, demonstrate that the Akgün et al. protocol suffers from a stolen/lost reader attack, which could expose users to some life threatening situations and unwanted collection of sensitive private information. Second, we found that all indexing identification‐based protocols and exceptionally the Akgün et al. protocol cannot be used properly when the collision of tag index occurs, which exhibits a low efficiency in the database entry identification process. As the Akgün et al. protocol is almost perfect, it is worthy to be enhanced. Therefore, towards a complete and secure mobile radio frequency identification authentication protocol, we propose efficient solutions that could overcome the aforementioned security problem and offer high identification efficiency using efficient anticollision procedure based on dynamic linked list data structure principle. Via informal and formal security analysis, we show that the improved version is insensitive to the most common attacks and is more efficient than the existing schemes in the literature. Copyright © 2016 John Wiley & Sons, Ltd.

A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors.

Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.

Pipelined hierarchical architecture for high performance packet classification

Hierarchical search structures satisfying good memory and update performance demands, are encouraging solution for packet classification in multi-core processors. However, pipelined hardware implementation of these algorithms has two major issues: (1) backtracking which causes stalling the pipeline and (2) memory inefficiency owing to variation in the size of trie nodes. In this paper, we present a clustering algorithm named recursive leaf extraction (RLE) that partitions an input ruleset into a certain number of sub-rulesets to eradicate backtracking in hierarchical search structures. We further enhanced RLE method and proposed Optimized-RLE (O-RLE) algorithm to balance the size of clusters. Additionally, we present a ternary trie data structure (Tϵ) that takes the advantage of ϵ-branch property to segment large trie nodes into fixed size ϵ-nodes to solve the memory inefficiency problem. We propose two hierarchical data structures denoted Tree-Trieϵ (TTϵ) and its extended version Tree-Trieϵ-Linked List (TTϵL). TTϵ consists of a binary search tree in Stage 1 and multiple Tϵ structures in Stage 2. TTϵL comprises an additional linked-list (LL) data structure in Stage 3 that maintains the large portion of ϵ nodes and thus freely optimizes search delay with a significant improvement in memory efficiency (20.39 bytes/rule). To accommodate the proposed data structures, we designed high throughput SRAM-based parallel and pipelined architectures on Field Programmable Gate Arrays (FPGAs) (134 Gbps).

From invariant checking to invariant inference using randomized search

We describe a general framework c2i for generating an invariant inference procedure from an invariant checking procedure. Given a checker and a language of possible invariants, c2i generates an inference procedure that iteratively invokes two phases. The search phase uses randomized search to discover candidate invariants and the validate phase uses the checker to either prove or refute that the candidate is an actual invariant. To demonstrate the applicability of c2i , we use it to generate inference procedures that prove safety properties of numerical programs, prove non-termination of numerical programs, prove functional specifications of array manipulating programs, prove safety properties of string manipulating programs, and prove functional specifications of heap manipulating programs that use linked list data structures.

A binary multiple knapsack model for single machine scheduling with machine unavailability

This paper addresses the single machine weighted number of on-time jobs scheduling problem where the machine is unavailable during one or more maintenance periods and the jobs share a common due date. It models the problem as a binary multiple knapsack (MKP), and offers an alternative proof that the problem is NP-Complete in the strong sense. Subsequently, it shows that some large-sized instances can be solved exactly within less than a second using an off-the-shelf solver. For difficult instances, the paper proposes a variable neighborhood search based heuristic V that explores the MKP nature of the problem to determine near-optima. V is dotted with two mechanisms that speed its convergence toward near-global optima: a linked list data structure and a dynamic threshold acceptance criterion. Experimental results provide computational evidence of the efficiency and efficacy of V for benchmark MKP instances and for scheduling problems alike. It further discusses the robustness of V with respect to the initial solution and problem׳s parameters.

An adaptive-gridding lattice Boltzmann method with linked-list data structure for two-dimensional viscous flows

An adaptive mesh refinement technique for lattice Boltzmann method (LBM) is proposed in this paper. It combines hierarchical linked-list data structure and the LBM calculation. Based on uniform meshes, the adaptive algorithm refines the meshes by constructing the linked-lists of nodes, cells and levels for mesh levels refined. To guarantee the stability of numerical scheme, quadratic bubble function for the nodal momentum is used to interpolate in the LBM calculation of different mesh levels. For the flows of relatively higher Re, large Eddy simulation (LES) is adopted to solve turbulence problems. Because of the use of adaptive technique, the computational time can be cut and accurate flow field information can be captured. OpenMP parallel for linked-list data structure is used to improve computational efficiency. To verify the present method, flows over circular cylinder at Re = 40, 300, 500, 1,000 and 3,900 and NACA0012 airfoil at Re = 105 for AOA = 4° are simulated.

Multi-pipelined and memory-efficient packet classification engines on FPGAs

A packet classification task incorporated in network firewalls to recognize and sift threats or unauthorized network accesses is accomplished by checking incoming packet headers against a pre-defined filter set. Plenty of solutions to reduce the memory requirement of filter set storage and accommodate packet classification to line rates have been proposed over the past decade. Among all the existing approaches, hierarchical data structures maintain great memory performance however their hardware realization suffers from two issues: (i) backtracking and (ii) memory inefficiency. In this paper, we propose two data structures denoted range tree-linked list hierarchical search structure (RLHS) and value-coded trie structure with ϵ-branch property (VCϵ) for packet classification. RLHS resolves backtracking by exploiting range tree in Stage 1 and linked list data structure in Stage 2 overcomes the memory inefficiency. VCϵ trie that naturally does not involve backtracking problem, solves memory inefficiency issue by comprising a fixed size bin at each node. Apart from conventional binary trie, a new rule is inserted into the first available bin on the path of this rule in a VCϵ trie, and ϵ-branch property is utilized in case all the bins are full. We also propose a rule categorization algorithm that partitions an input ruleset by considering the field features of rules to minimize the memory requirement. To support the proposed data structures, we designed high throughput SRAM-based parallel and pipelined architectures on Field Programmable Gate Arrays (FPGAs).

Program in C for studying characteristic properties of two-body interactions in the framework of spectral distribution theory

We present a program in C that employs spectral distribution theory for studies of characteristic properties of a many-particle quantum-mechanical system and the underlying few-body interaction. In particular, the program focuses on two-body nuclear interactions given in a JT-coupled harmonic oscillator basis and calculates correlation coefficients, a measure of similarity of any two interactions, as well as Hilbert–Schmidt norms specifying interaction strengths. An important feature of the program is its ability to identify the monopole part (centroid) of a 2-body interaction, as well as its ‘density-dependent’ one-body and two-body part, thereby providing key information on the evolution of shell gaps and binding energies for larger nuclear systems. As additional features, we provide statistical measures for ‘density-dependent’ interactions, as well as a mechanism to express an interaction in terms of two other interactions. This, in turn, allows one to identify, e.g., established features of the nuclear interaction (such as pairing correlations) within a general Hamiltonian. The program handles the radial degeneracy for ‘density-dependent’ one-body interactions and together with an efficient linked list data structure, facilitates studies of nuclear interactions in large model spaces that go beyond valence-shell applications. Program summaryProgram title: sdtCatalogue identifier: AEQG_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEQG_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 10888No. of bytes in distributed program, including test data, etc.: 88778Distribution format: tar.gzProgramming language: C.Computer: Laptop, Workstation.Operating system: Linux [tested on Linux (Kernel 2.6.9) with a gcc, version 3.4.6].RAM: Less than 10 MBClassification: 17.15.Nature of problem:The program calculates second-order energy moments, such as variances and correlation coefficients, widely used as measures of the overall strength of an interaction and its similarity to other interactions. It allows for studies of the physical properties of various interactions and their effect on many-particle systems.Solution method:Calculations are based on spectral distribution theory and invoke statistical measures provided by the theory.Running time:Less than 20 min (typically, several seconds) using a 1.80 GHz processor.

A New String Matching Algorithm and its Application in Hand-Written Digits Recognition

In this paper a new algorithm is introduced for syntactic pattern recognition and string matching by using linked listdata structure which later could be used for hand written digits recognition.At first,handwritten digits are changed to string as input pattern by using chaincodethen the achieved string is recognized by using refer algorithm being implemented by linked list. This refer algorithm is able to compute the distance between the chain code strings shown in the implementation. The suggested algorithm reduces time complexity ofleven shtein's algorithm from second-order to linear-orderand in addition is able to decrease the consumption memory and increase accuracy of handwritten digits recognition as well. Our proposed implemented algorithm has 94.8% accuracy over 3000 handwritten digits samples.

Measuring Algorithms Performance in Dynamic Linked List and Arrays

The focus of the research is on investigating the organization and structure of a list of data in order to find more efficient algorithmic solution. The aim of the realised experiment was to analyze, compare and measure the efficiency of algorithms for searching, insertion and deletion in a list of elements. An experimental case study is done through implementing the list as: 1) Array Data Structure and as 2) Dynamic Linked List Data Structure and measuring the performance. The contribution of the study is based on the realized experimental case study analyses, its insights and recommendations for improving the efficiency of algorithms for searching, insertion and deletion in a list of elements. The recommendations, and insights gained from the realized measurements are presented and discussed.

Wait-free linked-lists

The linked-list data structure is fundamental and ubiquitous. Lock-free versions of the linked-list are well known. However, the existence of a practical wait-free linked-list has been open. In this work we designed such a linked-list. To achieve better performance, we have also extended this design using the fast-path-slow-path methodology. The resulting implementation achieves performance which is competitive with that of Harris's lock-free list, while still guaranteeing non-starvation via wait-freedom. We have also developed a proof for the correctness and the wait-freedom of our design.