Matching Engine Research Articles

멜로디 비교 시각화를 통한 음정 교정 시스템 구현

With the proliferation of digital music, public‘s interest in music and desire to sing well are increasing. This paper presents the implementation of a tone correction system through a visualization of comparison between music and humming data. For this we extract MIDI note from music and humming data and then design a matching engine using DTW algorithm which is for robust matching results against local timing variation and inaccurate tempo. This system is expected to correct the user’s wrong tone by visualization and feedback from the result.

A regular expression matching engine with hybrid memories

A key technique of network security inspection is by using the regular expression matching to locate the specific fingerprints of networking applications or attacks in the packet flows, and accordingly identify the underlying applications or attacks. However, due to the surge of various networking applications and attacks in recent years, even more fingerprints need to be investigated in this process, which leads to a high demand on a large memory space for regular expression matching. In addition, with the frequent upgrading of the network links nowadays, the network flow rate also increases dramatically. As a result, it demands the fast operation of regular expression matching accordingly with the enhanced throughput for network inspection. However, due to the limited space of the fast memory, the requirements on fast operations and large memory space are conflicting. On addressing this challenge, in this paper, we propose to use hybrid memory for regular expression matching. In specific, by investigating on the transition table state access probability through the Markov theory, it can be observed that there exist a number of states which are much more frequently accessed than others. Therefore, we devise a matching engine which is suitable for FPGA implementation with two-level memories, where the first-level memory uses the on-chip memory of FPGA to cache the frequently accessed state transitions, and the second-level memory, composed of slow and cheap DRAM, stores the whole state transitions. Furthermore, the L7-filter's regular expression patterns have been applied to obtain the state access probability, and different quantities of memory assignment approaches have also been investigated to evaluate the throughput.

An efficient multicharacter transition string-matching engine based on the aho-corasick algorithm

A string-matching engine capable of inspecting multiple characters in parallel can multiply the throughput. However, the space required for implementing a matching engine that can process multiple characters in parallel generally grows exponentially with respect to the characters to be processed in parallel. Based on the Aho-Corasick algorithm (AC-algorithm), this work presents a novel multicharacter transition Nondeterministic Finite Automaton (NFA) approach, called multicharacter AC-NFA , to allow for the inspection of multiple characters in parallel. This approach first converts an AC-trie to an AC-NFA by allowing for the simultaneous activation of multiple states and then converts the AC-NFA to a k -character AC-NFA by an algorithm with concatenation operations and assistant transitions. Additionally, the alignment problem, which occurs while multiple characters are being inspected in parallel, is solved using assistant transitions. Moreover, a corresponding output is provided for each inspected character by introducing priority multiplexers to determine the final matching outputs during implementation of the multicharacter AC-NFA. Consequently, the number of derived k -character transitions grows linearly with respect to the number k . Furthermore, the derived multicharacter AC-NFA is implemented on FPGAs for evaluation. The resulting throughput grows approximately 14 times and the hardware cost grows about 18 times for 16-character AC-NFA implementation, as compared with that for 1-character AC-NFA implementation. The achievable throughput is 21.4Gbps for the 16-character AC-NFA implementation operating at a 167.36MHz clock.

Context Aware Middle Layer Architecture for Service Discovery in MANETs

In MANETs, Service discovery is considered to be a crucial entity since they don’t possess a centralized infrastructure for communication between nodes present in the environment. In addition, the level of security varies for each and every variety of services provided by through the network. Hence, there arises a necessity for deploying an efficient adaptive intelligent context-aware approach omnipresent in each and every node existing in the ad hoc scenario enabling reduce the complexity in providing the services to the network users. In this paper, we formulate an Adaptable Intelligent Discovery of context-aware services framework (AIDCS) along with the context modeling approach in order to introduce a middleware-level method that support user-centric semantic service discovery process for mobile users. In our approach, we modify the identifying relation among the different context by introducing a middleware-level approach that implements semantic languages to model the properties of interacting entities and environment. The Profile Matching Engine (PME) supports a matching algorithm that performs the matchmaking process between the offered and requested capacity in order to determine the degree of semantic compatibility between user/device and service profiles. This algorithm provides user with desired service features along with the flexibility of matching according to user’s preferences that provides the user with modified information on available services. The performances of AIDCS are studied with the help of evaluation parameters such as packet delivery ratio, throughput, control overhead, total overhead through ns-2 simulations by varying the number of compromised nodes. From the simulation results obtained, it is proved that the proposed mechanism performs well when compared to the other service discovery mechanisms available in the literature to a optimal margin.

An algorithm for generating model-sensitive search plans for pattern matching on EMF models

In this paper, we propose a new model-sensitive search plan generation algorithm to speed up the process of graph pattern matching. This dynamic-programming-based algorithm, which is able to handle general n-ary constraints in an integrated manner, collects statistical data from the underlying EMF model and uses this information for optimization purposes. Additionally, the search plan generation algorithm itself and its runtime effects on the pattern matching engine have been evaluated by complexity analysis techniques and by quantitative performance measurements, respectively.

Towards fast regular expression matching in practice

Regular expression matching is popular in today's network devices with deep inspection function, but due to lack of algorithmic scalability, it is still the performance bottleneck in practical network processing. To address this problem, our method first partition regular expression patterns into simple segments to avoid state explosion, and then compile these segments into a compact data structure to achieve fast matching. Preliminary experiments illustrate that our matching engine scales linearly with the size of the real-world pattern set, and outperforms state-of-the-art solutions.

Rule-Based Security Capabilities Matching for Web Services

A primary problem for security aware Web service discovery is how to discover security capabilities of Web services and how these security capabilities can be matched with security requirements of various requesters. Presently, most approaches are based on syntactic matching, which is prone to result in false negative because of lacking of semantics. In this paper, we propose a rule-based approach to decide whether security capabilities match security requirements. Based on a semantic model of security policy, security capabilities are inferred from security policy of Web services. General Web service security ontology is proposed to semantically model security requirements of various service requesters. The architecture of rule-based matching engine is also presented to describe the whole matching process. The prototype system and case study show that the proposed approach is flexible and feasible.

Implementation of a practical query-by-singing/humming (QbSH) system and its commercial applications

In this paper, a practical query-bysinging/humming (QbSH) system is proposed that uses polyphonic music tracks such as MP3 and AAC files to create the reference database (DB) unlike conventional QbSH systems. To create the reference DB, we propose a method for melody extraction from polyphonic music signals based on harmonic structure. In addition, we propose a matching engine using modified dynamic time warping (DTW) that uses chroma-scale representation and asymmetric path of DTW to reduce the influence of melody extraction error. We implemented three different prototypes for its commercial applications like smart phone, laptop and karaoke. We evaluated the performance of the proposed practical QbSH system with monophonic and polyphonic music datasets, and confirmed that it has an acceptable performance for commercial applications.

A memory-based NFA regular expression match engine for signature-based intrusion detection

Signature-based intrusion detection is required to inspect network traffic at wire-speed. Matching packet payloads against patterns specified with regular expression is a computation intensive task. Hence, the design of hardware accelerator to speed up regular expression matching has been an active research area. A systematic approach to detect regular expression is based on finite automaton. The space-time trade-off between deterministic finite automaton (DFA) and non-deterministic finite automaton (NFA) is well-known. DFA can offer constant throughput but it may suffer from the state explosion problem. Hence, implementation of DFA for large pattern sets on embedded device with limited on-chip memory may not be viable. NFA requires linear space but the throughput can be very low. Implementations of NFA with hardwired circuits can overcome the speed deficiency by exploiting the massive parallelism offered by dedicated hardware circuitries, but this approach does not support efficient dynamic updates. In this paper, we shall present a memory-based architecture for the implementation of NFA to speed up regular expression matching for signature-based intrusion detection. The proposed method supports dynamic updates and offers constant throughput so that it can be used to supplement the existing DFA-based methods in handling large pattern sets.

A novel spectral library workflow to enhance protein identifications

The innovations in mass spectrometry-based investigations in proteome biology enable systematic characterization of molecular details in pathophysiological phenotypes. However, the process of delineating large-scale raw proteomic datasets into a biological context requires high-throughput data acquisition and processing. A spectral library search engine makes use of previously annotated experimental spectra as references for subsequent spectral analyses. This workflow delivers many advantages, including elevated analytical efficiency and specificity as well as reduced demands in computational capacity. In this study, we created a spectral matching engine to address challenges commonly associated with a library search workflow. Particularly, an improved sliding dot product algorithm, that is robust to systematic drifts of mass measurement in spectra, is introduced. Furthermore, a noise management protocol distinguishes spectra correlation attributed from noise and peptide fragments. It enables elevated separation between target spectral matches and false matches, thereby suppressing the possibility of propagating inaccurate peptide annotations from library spectra to query spectra. Moreover, preservation of original spectra also accommodates user contributions to further enhance the quality of the library. Collectively, this search engine supports reproducible data analyses using curated references, thereby broadening the accessibility of proteomics resources to biomedical investigators. This article is part of a Special Issue entitled: From protein structures to clinical applications.

A Hardware-Based String Matching Using State Transition Compression for Deep Packet Inspection

This letter proposes a memory-based parallel string matching engine using the compressed state transitions. In the finite-state machines of each string matcher, the pointers for representing the existence of state transitions are compressed. In addition, the bit fields for storing state transitions can be shared. Therefore, the total memory requirement can be minimized by reducing the memory size for storing state transitions.

A Hardware-Based String Matching Using State Transition Compression for Deep Packet Inspection

This letter proposes a memory-based parallel string matching engine using the compressed state transitions. In the finite-state machines of each string matcher, the pointers for representing the existence of state transitions are compressed. In addition, the bit fields for storing state transitions can be shared. Therefore, the total memory requirement can be minimized by reducing the memory size for storing state transitions.

ANALISA TEKNIS OPTIMALISASI SISTEM PROPULSI KAPAL IKAN MENGGUNAKAN CVT GEARBOX

The propulsion system of traditional fishing vessels until this time generally still using a single transmission gearbox. Transmission system with a single speed gearbox cannot operate at the optimum operation main engine, causing the engine operation is unefficient. To obtain the optimum operational of propulsion system is carried out loading curve analysis method using ANLOVA software and engine propeller matching. Calculation of ship resistance using Maxsurf with Ootmerssen method and DelfIII. The calculation result of ship resistance using both method is analyzed to obtain constraints on the ship accordance with the LSE method. The analysis of matching engine propeller data obtained from CVT gearbox ratios setting in the range of 5 to 9. The impact of setting the ratio obtained the spesific fuel consumption reduction of 6gram/kw/h or 720 gram/hour at speed of 7 knot, meanwhile the highest fuel consumption reduction obtained at the point of operation of 5:48 speed knots with a decrease spesific fuel consumption 21 gram/kW/h or 1260 grams/hour.

Efficient 3D face recognition handling facial expression and hair occlusion

This paper presents an efficient 3D face recognition method to handle facial expression and hair occlusion. The proposed method uses facial curves to form a rejection classifier and produce a facial deformation mapping and then adaptively selects regions for matching. When a new 3D face with an arbitrary pose and expression is queried, the pose is normalized based on the automatically detected nose tip and the principal component analysis (PCA) follows. Then, the facial curve in the nose region is extracted and used to form the rejection classifier which quickly eliminates dissimilar faces in the gallery for efficient recognition. Next, six facial regions which cover the face are segmented and curves in these regions are used to map facial deformation. Regions used for matching are automatically selected based on the deformation mapping. In the end, results of all the matching engines are fused by weighted sum rule. The approach is applied on the FRGC v2.0 dataset and a verification rate of 96.0% for ROC III is achieved as a false acceptance rate (FAR) of 0.1%. In the identification scenario, a rank-one accuracy of 97.8% is achieved.

Multi-Stride String Searching for High-Speed Content Inspection

Design of hardware-assisted high-speed string-matching engine for content inspection has been an active research topic. Scalability, flexibility and speed are the three major challenges. In this paper, we shall present a high-speed string matching engine for virus scanning that can process 3 bytes of input data per cycle. Our design uses a memory-based architecture. The hardware circuits need not be reconfigured when the pattern set is updated. We evaluate our design using the ClamAV virus database with over 82K patterns, and the memory cost of our method is about 2.4Â MB. The proposed method is an improved version of our previously published method called quick sampling with on demand verification. The previous design has a memory cost of 1.4Â MB and a throughput of 1 byte per cycle. Two novel architectural features are incorporated into the new design, namely a new technique to construct near-minimal dynamic perfect hash tables using the bit-shuffle approach, and the introduction of a new concept called byte-shift invariant code (BSIC). With the BSIC, a suffix verification unit can be shared by multiple prefix sampling units. Hence, the processing rate of the new design can be speeded up to three times the processing rate of the old design, while the memory cost is only increased by 72%.

다성음원 기반 QbSH 시스템을 위한 매칭엔진의 설계 및 구현

This paper proposes a matching engine of query-by-singing/humming (QbSH) system which retrieves the most similar music information by comparing the input data with the extracted feature information from polyphonic music like MP3. The feature sequences transcribed from polyphonic music may have many errors. So, to reduce the influence of errors and improve the performance, the chroma-scale representation, compensation and asymmetric DTW (Dynamic Time Warping) are adopted in the matching engine. The performance of various distance metrics are also investigated in this paper. In our experiment, the proposed QbSH system achieves MRR (Mean Reciprocal Rank) of 0.718 for 1000 singing/humming queries when searching from a database of 450 polyphonic musics.

Accelerating Application Identification with Two-Stage Matching and Pre-Classification

Modern datacenter and enterprise networks require application identification to enable granular traffic control that either improves data transfer rates or ensures network security. Providing application visibility as a core network function is challenging due to its performance requirements, including high throughput, low memory usage, and high identification accuracy. This paper presents a payload-based application identification method using a signature matching engine utilizing characteristics of the application identification. The solution uses two-stage matching and pre-classification to simultaneously improve the throughput and reduce the memory. Compared to a state-of-the-art common regular expression engine, this matching engine achieves 38% memory use reduction and triples the throughput. In addition, the solution is orthogonal to most existing optimization techniques for regular expression matching, which means it can be leveraged to further increase the performance of other matching algorithms.

CPM: CUCKOO-BASED PATTERN MATCHING APPLIED FOR NIDS

This paper describes the Cuckoo-based Pattern Matching (CPM) engine which based on a recently developed hashing algorithm called Cuckoo Hashing. We implemented the improved parallel Cuckoo Hashing suitable for hardware-based multi-pattern matching with arbitrary length. CPM is scalable with multi-character per clock cycle to sustain higher throughput rates with lower hardware resources. With the power of massively parallel processing, the speedup of CPM is up to 128X as compared with serial Cuckoo implementation. Compared to other hardware systems, CPM is far better in performance and save 30% of the area compared with the best system.

Pattern matching engine based on multi-dimensional bloom filters

Concerning the defects of traditional rule matching engine,a solution using multi-dimensional bloom filters based on Field Programmable Gate Array(FPGA) was proposed.The rule matching engine was designed to process both packet header and payload in parallel.The suspicious strings were picked up by the multi-dimensional bloom filters engines,and then sent to bit-split state machine for verification.The experimental results demonstrate that the false positive probability of the engine is reduced by using the multi-dimensional bloom filters,which results in a higher throughput.

Deterministic finite automata characterization and optimization for scalable pattern matching

Memory-based Deterministic Finite Automata (DFA) are ideal for pattern matching in network intrusion detection systems due to their deterministic performance and ease of update of new patterns, however severe DFA memory requirements make it impractical to implement thousands of patterns. This article aims to understand the basic relationship between DFA characteristics and memory requirements, and to design a practical memory-based pattern matching engine. We present a methodology that consists of theoretical DFA characterization, encoding optimization, and implementation architecture. Results show the validity of the characterization metrics, effectiveness of the encoding techniques, and efficiency of the memory-based pattern engines.