This paper proposes a new part-based approach for skew estimation of document images. The proposed method first estimates skew angles on rather small areas, which are the local parts of characters, and subsequently determines the global skew angle by aggregating those local estimations. A local skew estimation on a part of a skewed character is performed by finding an identical part from prepared upright character images and calculating the angular difference. Specifically, a keypoint detector (e.g. SURF) is used to determine the local parts of characters, and once the parts are described as feature vectors, a nearest neighbor search is conducted in the instance database to identify the parts. Finally, a local skew estimation is acquired by calculating the difference of the dominant angles of brightness gradient of the parts. After the local skew estimation, the global skew angle is estimated by the majority voting of those local estimations, disregarding some noisy estimations. Our experiments have shown that the proposed method is more robust to short and sparse text lines and non-text backgrounds in document images compared to conventional methods.

This paper presents a proposed NMOS-centered 6T SRAM cell layout that reduces a neutron-induced multiple-cell-upset (MCU) SER on a same wordline. We implemented an 1-Mb SRAM macro in a 65-nm CMOS process and irradiated neutrons as a neutron-accelerated test to evaluate the MCU SER. The proposed 6T SRAM macro improves the horizontal MCU SER by 67–98% compared with a general macro that has PMOS-centered 6T SRAM cells.

We propose a composite DCT basis line test signal to evaluate the video quality of a DTV encoder. The proposed composite test signal contains a frame index, a calibration square wave, and 7-field basis signals. The results show that the proposed method may be useful for an in-service video quality verifier, using an ordinary oscilloscope instead of special equipment.

The Semantic Web uses RDF/RDFS, which can enable a machine to understand web data without human interference. But most web data is not available in RDF/RDFS documents because most web data is still stored in databases. It is much more favorable to use stored data in a database to build the Semantic Web. This paper proposes an enhanced relational RDF/RDFS interoperable data model (ER2iDM) and a transformation procedure from relational data model (RDM) to RDF/RDFS based on ER2iDM. The ER2iDM is a data model that plays the role of an inter-mediator between RDM and RDF/RDFS during a transformation procedure. The data and schema information in the database are migrated to the ER2iDM according to the proposed translation procedures without incurring loss of meaning of the entities, relationships, and data. The RDF/RDFS generation tool makes a RDF/RDFS XML document automatically from the ER2iDM. The proposed ER2iDM and transformation procedure provides detailed guidelines for transformation from RDM to RDF/RDFS unlike existing studies; therefore, we can more efficiently build up the Semantic Web using database stored data.

Although shared caches allow the dynamic allocation of limited cache capacity among cores, traditional LRU replacement policies often cannot prevent negative interference among cores. To address the contention problem in shared caches, cache partitioning and application scheduling techniques have been extensively studied. Partitioning explicitly determines cache capacity for each core to maximize the overall throughput. On the other hand, application scheduling by operating systems groups the least interfering applications for each shared cache, when multiple shared caches exist in systems. Although application scheduling can mitigate the contention problem without any extra hardware support, its effect can be limited for some severe contentions. This paper proposes a low cost solution, based on application scheduling with a simple cache insertion control. Instead of using a full hardware-based cache partitioning mechanism, the proposed technique mostly relies on application scheduling. It selectively uses LRU insertion to the shared caches, which can be added with negligible hardware changes from the current commercial processor designs. For the completeness of cache interference evaluation, this paper examines all possible mixes from a set of applications, instead of using a just few selected mixes. The evaluation shows that the proposed technique can mitigate the cache contention problem effectively, close to the ideal scheduling and partitioning.

In this paper, we propose a novel scheme for efficient video broadcasting over WLANs using the IEEE 802.11e HCCA MAC and H.264/SVC. We rearrange the outgoing sequence of H.264/SVC NAL units according to their dimension, temporal, and quality scalability. In addition, our proposed scheme broadcasts the NAL units at various data-rates by using the link adaptation function of IEEE 802.11 PHY. Our scheme is verified using NCTUns network simulator, and is evaluated in terms of throughput, delay, and quality of experience (QoE) using structural similarity (SSIM) rather than mean square error (MSE). We employee a real video clip to increase the reliability of the simulation in which the video clip is compressed as VBR with 24 scalable layers by JSVM reference codec of the H.264/SVC. In the simulation topology, a host broadcasts the video clip to 10 wireless stations which are within 150 meters from an AP. We present performance comparisons between our proposed scheme and the scheme provided by the IEEE 802.11e HCCA standard, which is to be referred to as the simple scheme in this paper. The proposed scheme noticeably enhances in the three performance metrics. All wireless stations by the proposed scheme receive more video data than the simple scheme around 2530% within a delay bound of 1 second. The proposed scheme controls the end-to-end delay to 510% under that of the simple scheme. As for the throughput and the delay performance, the proposed scheme enhances the video quality by up to 67% compared to the simple scheme in SSIM evaluation.

Large-scale introduction of renewable energy such as photovoltaic energy and wind is a big motivation for renovating conventional grid systems. To be independent from existing power grids and to use renewable energy as much as possible, a decentralized energy network is proposed as a new grid system. The decentralized energy network is placed among houses to connect them with each other, and each house has a PV panel and a battery. A contribution of this paper is a network topology and battery size exploration for the decentralized energy network in order to make effective use of renewable energy. The proposed method for exploring the decentralized energy network design is inspired by the design methodology of VLSI systems, especially design space exploration in system-level design. The proposed method is based on mixed integer programming (MIP) base power flow optimization, and it was evaluated for all design instances. Experimental results show that the decentralized energy network has the following features. 1) The energy loss and energy purchased due to power shortage were not affected by each battery size but largely affected by the sum of all battery sizes in the network, and 2) the network topology did not largely affect the energy loss and the purchased energy. These results will become a useful guide to designing an optimal decentralized energy network for each region.

Information-centric networking has been touted as an alternative to the current Internet architecture. Our work addresses a crucial part of such a proposal, namely the design of a network node within an information-centric networking architecture. Special attention is given in providing a platform for development and experimentation in an emerging network research area; an area that questions many starting points of the current Internet. In this paper, we describe the service model exposed to applications and provide background on the operation of the platform. For illustration, we present current efforts in deployment and experimentation with demo applications presented, too.

Music-similarity computation is an essential building block for the browsing, retrieval, and indexing of digital music archives. This paper proposes a music similarity function based on the centroid model, which divides the feature space into non-overlapping clusters for the efficient computation of the timber distance of two songs. We place particular emphasis on the centroid deviation as a feature for music-similarity computation. Experiments show that the centroid-model representation of the auditory features is promising for music-similarity computation.

We propose a computing method for linear convolution and linear correlation between sequences using discrete cosine transform (DCT). Zero-padding is considered as well as linear convolution using discrete Fourier transform (DFT). Analyzing the circular convolution between symmetrically extended sequences, we derive the condition for zero-padding before and after the sequences. The proposed method can calculate linear convolution for any filter and also calculate linear correlation without reversing one of the input sequences. The computational complexity of the proposed method is lower than that of linear convolution using DFT.

Discrete structures are foundational material for computer science and mathematics, which are related to set theory, symbolic logic, inductive proof, graph theory, combinatorics, probability theory, etc. Many problems solved by computers can be decomposed into discrete structures using simple primitive algebraic operations. It is very important to represent discrete structures compactly and to execute efficiently tasks such as equivalency/validity checking, analysis of models, and optimization. Recently, BDDs (Binary Decision Diagrams) and ZDDs (Zero-suppressed BDDs) have attracted a great deal of attention, because they efficiently represent and manipulate large-scale combinational logic data, which are the basic discrete structures in various fields of application. Although a quarter of a century has passed since Bryant's first idea, there are still a lot of interesting and exciting research topics related to BDD and ZDD. BDD/ZDD is based on in-memory data processing techniques, and it enjoys the advantage of using random access memory. Recent commodity PCs are equipped with gigabytes of main memory, and we can now solve large-scale problems which used to be impossible due to memory shortage. Thus, especially since 2000, the scope of BDD/ZDD methods has increased. This survey paper describes the history of, and recent research activity pertaining to, techniques related to BDD and ZDD.

By using impedance (Z) matching circuits in a low-cost transistor outline (TO) CAN package for a 10 Gb/s transmitter, we achieve a cost-effective and small bidirectional optical subassembly (BOSA) with excellent optical transmission waveforms and a > 40% mask margin over a wide temperature range (-10 to 85). We describe a design for Z matching circuits and simulation results, and discuss the advantage of the cost-effective compensation technique.

This paper presents a novel topic modeling (TM) approach for discovering meaningful topics for Web APIs, which is a potential dimensionality reduction way for efficient and effective classification, retrieval, organization, and management of numerous APIs. We exploit the possibility of conducting TM on multi-labeled APIs by combining a supervised TM (known as Labeled LDA) with ontology. Experiments conducting on real-world API data set show that the proposed method outperforms standard Labeled LDA with an average gain of 7.0% in measuring quality of the generated topics. In addition, we also evaluate the similarity matching between topics generated by our method and standard Labeled LDA, which demonstrates the significance of incorporating ontology.

In order to cope with the continuous evolution in cyber threats, many security products (e.g., IDS/IPS, TMS, Firewalls) are being deployed in the network of organizations, but it is not so easy to monitor and analyze the security events triggered by the security products constantly and effectively. Thus, in many cases, real-time incident analysis and response activities for each organization are assigned to an external dedicated security center. However, since the external security center deploys its security appliances to only the boundary or the single point of the network, it is very difficult to understand the entire network situation and respond to security incidents rapidly and accurately if they depend on only a single type of security information. In addition, security appliances trigger an unmanageable amount of alerts (in fact, by some estimates, several thousands of alerts are raised everyday, and about 99% of them are false positives), this situation makes it difficult for the analyst to investigate all of them and to identify which alerts are more serious and which are not. In this paper, therefore, we propose an advanced incident response methodology to overcome the limitations of the existing incident response scheme. The main idea of our methodology is to utilize polymorphic security events which can be easily obtained from the security appliances deployed in each organization, and to subject them to correlation analysis. We evaluate the proposed methodology using diverse types of real security information and the results show the effectiveness and superiority of the proposed incident response methodology.

We have succeeded in demonstrating high-performance four-channel 25 Gb/s integrated receiver for 100 Gb/s Ethernet with a built-in spatial Demux optics and an integrated PD array. All components which configure to the Demux optics adhered to a prism. Because of the shaping accuracy for prism, the insertion loss was able to suppress to 0.8 dB with small size. The connection point of the package for high speed electrical signals was improved to decrease the transmission loss. The small size of 12 mm 17 mm 7 mm compact package with a side-wall electrical connector has been achieved, which is compatible with the assembly in CFP2 form-factor. We observed the sensitivity at average power of -12.1 dBm and the power penalty of sensitivity due to the crosstalk of less than 0.1 dB.

In this paper, we proposed that an area- and speed-effective fixed-point pipelined divider be used for reducing the bit-width of a division unit to fit a mobile rendering processor. To decide the bit-width of a division unit, error analysis has been carried out in various ways. As a result, when the original bit-width was 31-bit, the proposed method reduced the bit-width to 24-bit and reduced the area by 42% with a maximum error of 0.00001%.

In this paper, we propose a stride static chunking deduplication algorithm using a hybrid approach that exploits the advantages of static chunking and byte-shift chunking algorithm. The key contribution of our approach is to reduce the computation time and enhance deduplication performance. We assume that duplicated data blocks are generally gathered into groups; thus, if we find one duplicated data block using byte-shift, then we can find subsequent data blocks with the static chunking approach. Experimental results show that stride static chunking algorithm gives significant benefits over static chunking, byte-shift chunking and variable-length chunking algorithm, particularly for reducing processing time and storage space.

Despite the improvement of the accuracy of RFID readers, there are still erroneous readings such as missed reads and ghost reads. In this letter, we propose two effective models, a Bayesian inference-based decision model and a path-based detection model, to increase the accuracy of RFID data cleaning in RFID based supply chain management. In addition, the maximum entropy model is introduced for determining the value of sliding window size. Experiment results validate the performance of the proposed method and show that it is able to clean raw RFID data with a higher accuracy.

Passive-blind image forensics is a technique that judges whether an image is forged in the absence of watermarking. In image forgery, region duplication is a simple and widely used method. In this paper, we proposed a novel method to detect image region duplication using the spin image which is an intensity-based and rotation invariant descriptor. The method can detect region duplication exactly and is robust to geometric transformations. Furthermore, it is superior to the popular SIFT-based detection method when the copied patch is from smooth background. The experiments have proved the method's effectiveness.

Square root-raised-cosine (SRRC) filters are used in many systems for spectrum shaping, which leads to a high peak-to-average power ratio (PAPR). Nevertheless, some applications demand a low PAPR in terms of both the error performance and the strict restriction of the spectrum mask. In this letter, we propose a PAPR reduction method based on the modified active constellation extension for systems using SRRC filters. Results show that the proposed method substantially reduces the PAPR, and therefore it is applicable to satellite communications to improve the power efficiency at the transmitter.