This paper proposes a concept of a concurrent configuration of radio-frequency (RF) micromachined and micro-electro-mechanical-system (MEMS) devices. The devices are fabricated on an originally developed dielectric-air-metal (DAM) structure that suits for fabrication of various devices all together. The DAM structure can propose membrane-supported hollow elements embedded in a silicon wafer by creating cavities in it. Even though the devices have different cavity depths, they are processed by just one planarization. In addition, since the structure is worked only from the front side of the wafer, no flipping process as well as no wafer bonding process is required, and the fact realizes low-cost concurrent integration. As applications of the DAM structures, a hollow grounded co-planar waveguide, lumped element circuitries, and an MEMS switch are demonstrated.

We present a full description of a polarization-independent athermal differential quadrature phase shift keying (DQPSK) demodulator that employs silica-based planar lightwave circuit (PLC) technology. Silica-based PLC DQPSK demodulator has good characteristics including low polarization dependence, mass producibility, etc. However delay line interferometer (DLI) of demodulator had the large temperature dependence of its optical characteristics, so it required large power consumption to stabilize the chip temperature by the thermo-electric cooler (TEC). We previously made a quick report about an athermal DLI to reduce a power consumption by removing the TEC. In this paper, we focus on the details of the design and the fabrication method we used to achieve the athermal characteristics, and we describe the thermal stability of the signal demodulation and the reliability of our demodulator. We described two athermalization methods; the athermalization of the transmission spectrum and the athermalization of the polarization property. These methods were successfully demonstrated with keeping a high extinction ratio and a small footprint by introducing a novel interwoven DLI configuration. This configuration can also limit the degradation of the polarization dependent phase shift (PDf) to less than 1/10 that with the conventional configuration when the phase shifters on the waveguide are driven. We used our demodulator and examined its demodulation performance for a 43 Gbit/s DQPSK signal. We also verified its long-term reliability and thermal stability against the rapid temperature change. As a result, we confirmed that our athermal demodulator performed sufficiently well for use in DQPSK systems.

This paper treats a band-broadening design technique of a dual-band branch-line coupler with matching networks composed of an impedance step and a short-circuited stub based on the equivalent admittance approach. By replacing each right-handed transmission line (RH-TL) with a composite right/left-handed transmission line (CRLH-TL), very flat couplings over a relative bandwidth of about 10% can be obtained at two arbitrary operating frequencies in comparison with previous CRLH-TLs branch-line couplers. Furthermore, by adding periodical open-circuited stubs into RH-TLs of the designed CRLH-TLs branch-line coupler with matching networks, the entire size of the coupler can be reduced to about 50%. Verification of these band-broadening and size-reduction design techniques can be also shown by an electromagnetic simulation and experiment.

This paper proposes a pattern partitioning algorithm that maps multiple target patterns onto homogeneous memory-based string matchers. The proposed algorithm adopts the greedy search based on lexicographical sorting. By mapping as many target patterns as possible onto each string matcher, the memory requirements are greatly reduced.

This paper investigates the closure properties of 1-inkdot nondeterministic Turing machines and 1-inkdot alternating Turing machines with only universal states which have sublogarithmic space. We show for example that the classes of sets accepted by these Turing machines are not closed under length-preserving homomorphism, concatenation with regular set, Kleene closure, and complementation.

A cache attack against DICING is presented. Cache attacks use CPU cache miss and hit information as side-channel information. DICING is a stream cipher that was proposed at eSTREAM. No effective attack on DICING has been reported before. Because DICING uses a key-dependent S-box and there is no key addition before the first S-box layer, a conventional cache attack is considered to be difficult. We therefore investigated an access-driven cache attack that employs the special features of transformation L to give the chosen IV. We also investigated reduction of the computational complexity required to obtain the secret key from the information gained in the cache attack. We were able to obtain a 40-bit key differential given a total of 218 chosen IVs on a Pentium III processor. From the obtained key differential, the 128-bit secret key could be recovered with computational complexity of from 249 to 263. This result shows that the new cache attack, which is based on a different attack model, is also applicable in an actual environment.

Finding a kernel mapping function for support vector machines (SVMs) is a key step towards construction of a high-performanced SVM-based classifier. While some recent methods exploited an evolutional approach to construct a suitable multifunction kernel, most of them searched randomly and diversely. In this paper, the concept of a family of identical-structured kernel trees is proposed to enable exploration of structure space using genetic programming whereas to pursue investigation of parameter space on a certain tree using evolution strategy. To control balance between structure and parameter search towards an optimal kernel, simulated annealing is introduced. By experiments on a number of benchmark datasets in the UCI and text classification collection, the proposed method is shown to be able to find a better optimal solution than other search methods, including grid search and gradient search.

Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.

Motion compensation is widely used in many video coding standards. Due to its bandwidth requirement and complexity, motion compensation is one of the most challenging parts in the design of high definition video decoder. In this paper, we propose a high performance and low bandwidth motion compensation design, which supports H.264/AVC, MPEG-1/2 and Chinese AVS standards. We introduce a 2-Dimensional cache that can greatly reduce the external bandwidth requirement. Similarities among the 3 standards are also explored to reduce hardware cost. We also propose a block-pipelining strategy to conceal the long latency of external memory access. Experimental results show that our motion compensation design can reduce the bandwidth by 74% in average and it can real-time decode 1920x1088@30 fps video stream at 80 MHz.

This position paper outlines the author's view on architectural directions and key technology enablers for the future mobile Internet. It is pointed out that mobile and wireless services will dominate Internet usage in the near future, and it is therefore important to design next-generation network protocols with features suitable for efficiently serving emerging wireless scenarios and applications. Several key requirements for mobile/wireless scenarios are identified - these include new capabilities such as dynamic spectrum coordination, cross-layer support, disconnection tolerant routing, content addressing, and location awareness. Specific examples of enabling technologies which address some of these requirements are given from ongoing research projects at WINLAB. Topics covered briefly include wireless network virtualization, the cache-and-forward (CNF) protocol, geographic (GEO) protocol stack, cognitive radio protocols, and open networking testbeds.

This paper proposes a hardware-based parallel pattern matching engine using a memory-based bit-split string matcher architecture. The proposed bit-split string matcher separates the transition table from the state table, so that state transitions towards the initial state are not stored. Therefore, total memory requirements can be minimized.

This paper proposes a topic-independent method for automatically scoring essay content. Unlike conventional topic-dependent methods, it predicts the human-assigned score of a given essay without training essays written to the same topic as the target essay. To achieve this, this paper introduces a new measure called MIDF that measures how important and relevant a word is in a given essay. The proposed method predicts the score relying on the distribution of MIDF. Surprisingly, experiments show that the proposed method achieves an accuracy of 0.848 and performs as well as or even better than conventional topic-dependent methods.

In this paper, we demonstrate a 10.66 Gb/s bidirectional TDM over long-reach WDM hybrid PON supported by distributed Raman amplification, and the power budget margin is measured to be 15 dB for downstream transmission and 12 dB for upstream transmission, with dual Raman pump power of 300 mW.

Recently, two-level cache, L1 cache and L2 cache, is commonly used in a processor. Particularly in an embedded system whereby a single application or a class of applications is repeatedly executed on a processor, its cache configuration can be customized such that an optimal one is achieved. An optimal two-level cache configuration can be obtained which minimizes overall memory access time or memory energy consumption by varying the three cache parameters: the number of sets, a line size, and an associativity, for L1 cache and L2 cache. In this paper, we first extend the L1 cache simulation algorithm so that we can explore two-level cache configuration. Second, we propose two-level cache design space exploration algorithms: CRCB-T1 and CRCB-T2, each of which is based on applying Cache Inclusion Property to two-level cache configuration. Each of the proposed algorithms realizes exact cache simulation but decreases the number of cache hit/miss judgments by a factor of several thousands. Experimental results show that, by using our approach, the number of cache hit/miss judgments required to optimize a cache configurations is reduced to 1/50-1/5500 compared to the exhaustive approach. As a result, our proposed approach totally runs an average of 1398.25 times faster compared to the exhaustive approach. Our proposed cache simulation approach achieves the world fastest two-level cache design space exploration.

While classification techniques can be applied for automatic unknown word recognition in a language without word boundary, it faces with the problem of unbalanced datasets where the number of positive unknown word candidates is dominantly smaller than that of negative candidates. To solve this problem, this paper presents a corpus-based approach that introduces a so-called group-based ranking evaluation technique into ensemble learning in order to generate a sequence of classification models that later collaborate to select the most probable unknown word from multiple candidates. Given a classification model, the group-based ranking evaluation (GRE) is applied to construct a training dataset for learning the succeeding model, by weighing each of its candidates according to their ranks and correctness when the candidates of an unknown word are considered as one group. A number of experiments have been conducted on a large Thai medical text to evaluate performance of the proposed group-based ranking evaluation approach, namely V-GRE, compared to the conventional naive Bayes classifier and our vanilla version without ensemble learning. As the result, the proposed method achieves an accuracy of 90.930.50% when the first rank is selected while it gains 97.260.26% when the top-ten candidates are considered, that is 8.45% and 6.79% improvement over the conventional record-based naive Bayes classifier and the vanilla version. Another result on applying only best features show 93.930.22% and up to 98.85 0.15% accuracy for top-1 and top-10, respectively. They are 3.97% and 9.78% improvement over naive Bayes and the vanilla version. Finally, an error analysis is given.

This paper studies reflection and transmission of a TE plane wave from a two-dimensional random slab with statistically anisotropic fluctuation by means of the stochastic functional approach. By starting with a representation of the random wavefield presented in the previous paper [IEICE Trans. Electron., vol.E92-C, no.1, pp.77-84, Jan. 2009], a solution algorithm of the multiple renormalized mass operator is newly shown even for anisotropic fluctuation. The multiple renormalized mass operator, the first-order incoherent scattering cross section and the optical theorem are numerically calculated and illustrated in figures. The relation between statistical properties and anisotropic fluctuation is discussed.

This paper proposes a method for handling out-of-vocabulary (OOV) words that cannot be translated using conventional phrase-based statistical machine translation (SMT) systems. For a given OOV word, lexical approximation techniques are utilized to identify spelling and inflectional word variants that occur in the training data. All OOV words in the source sentence are then replaced with appropriate word variants found in the training corpus, thus reducing the number of OOV words in the input. Moreover, in order to increase the coverage of such word translations, the SMT translation model is extended by adding new phrase translations for all source language words that do not have a single-word entry in the original phrase-table but only appear in the context of larger phrases. The effectiveness of the proposed methods is investigated for the translation of Hindi to English, Chinese, and Japanese.

This paper presents a reordering model using a source-side parse-tree for phrase-based statistical machine translation. The proposed model is an extension of IST-ITG (imposing source tree on inversion transduction grammar) constraints. In the proposed method, the target-side word order is obtained by rotating nodes of the source-side parse-tree. We modeled the node rotation, monotone or swap, using word alignments based on a training parallel corpus and source-side parse-trees. The model efficiently suppresses erroneous target word orderings, especially global orderings. Furthermore, the proposed method conducts a probabilistic evaluation of target word reorderings. In English-to-Japanese and English-to-Chinese translation experiments, the proposed method resulted in a 0.49-point improvement (29.31 to 29.80) and a 0.33-point improvement (18.60 to 18.93) in word BLEU-4 compared with IST-ITG constraints, respectively. This indicates the validity of the proposed reordering model.

To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.

Bloom filters are widely used for various network applications. Because of the limited size of on-chip memory and the large volume of network traffic, Bloom filters are often required to update their contents incrementally. Two techniques have been used for this purpose: cold cache and double buffering. Cold cache outperforms double buffering in terms of the average cache ratio. However, double buffering works much better than cold cache for the worst-case cache hit ratio. In this paper, we propose a new updating scheme for Bloom filters, which updates the contents of Bloom filter incrementally while the assigned memory space is fully utilized. The proposed scheme works better than cold cache in terms of the average cache hit ratio. At the same time, it outperforms double buffering for the worst-case cache hit ratio.