We propose a polarization-multiplexing QPSK modulator for synthesis of a 16 QAM signal. The generation mechanism of 16 QAM is based on an electro-optic vector digital-to-analog converter, which can generate optical multilevel signals from binary electric data sequences. A quad-parallel Mach-Zehnder modulator (QPMZM) used in our previous research requires precise control of electric signals or fabrication of a variable optical attenuator, which significantly raises the degree of difficulty to control electric signals or device fabrication. To overcome this difficulty, we developed the polarization-multiplexing QPSK modulator, which improved the method of superposition of QPSK signals. In the polarization-multiplexing QPSK modulator, two QPSK signals are output with orthogonal polarization and superposed through a polarizer. The amplitude ratio between the two QPSK signals can be precisely controlled by rotating the polarizer to arrange the 16 symbols equally. Generation of 16 QAM with 40 Gb/s and a bit error rate of 5.6910-5 was successfully demonstrated using the polarization-multiplexing QPSK modulator. This modulator has simpler configuration than the previous one, utilized a dual-polarization MZM, alleviating complicated control of electric signals.

Two designs of wideband compact MIMO antenna using printed dipoles are proposed in this paper. One is a three-port orthogonal polarization antenna and the other is a cube-six-port antenna. Measured results for the antennas show that they resonate at 2.6 GHz and support a bandwidth of over 400 MHz. The worst mutual coupling for the three-port orthogonal polarization antenna is kept under -20 dB whereas that level of the cube-six-port antenna is -18 dB. A number of experiments are conducted on MIMO systems with these compact antennas and linear antenna arrays. Measured data are analyzed to examine channel characteristics, such as cumulative distribution functions (CDFs) of eigenvalues. Furthermore, the effect of different antenna configurations on channel capacity is highlighted and discussed. A high data rate capacity can be achieved with the compact antennas, particularly from the cube-six-port variant. These antennas might be applied in actual MIMO systems in wireless communications.

This paper introduces a tensorial representation of multiple cameras with arbitrary curvilinear motions. It enables us to define a multilinear relationship among image points derived from non-rigid object motions viewed from multiple cameras with arbitrary curvilinear motions. We show the new multilinear relationship is useful for generating images and reconstructing 3D non-rigid object motions viewed from cameras with arbitrary curvilinear motions. The method is tested in real image sequences.

A Boolean function is said to be correlation immune if its output leaks no information about its input values. Such functions have many applications in computer security practices including the construction of key stream generators from a set of shift registers. Finding methods for easy construction of correlation immune Boolean functions has been an active research area since the introduction of the notion by Siegenthaler. In this paper, we present several constructions of nonpalindromic correlation immune symmetric Boolean functions. Our methods involve finding binomial coefficient identities and obtaining new correlation immune functions from known correlation immune functions. We also consider the construction of higher order correlation immunity symmetric functions and propose a class of third order correlation immune symmetric functions on n variables, where n+1(≥ 9) is a perfect square.

We consider RFID bookshelves, which detect the location of books using RFID. An RFID bookshelf has the antennas of RFID readers in the boards, and detects the location of an RFID tag attached to a book. However, the accuracy is not good with the experience of the existing system, and sometimes reads the tag of the next or even further area. In this paper, we propose a method to improve the location detection using naive Bayes classifer, and show the experimental result. We obtained 78.6% of F-measure for total 12658 instances, and show the advantage against the straightforward approach of calculating the center of gravity of the read readers. More importantly, we show the performance is less dependent of a change of layouts and a difference of books by leave-1-layout/book-out cross validation. This is favorable for the feasibility in library operation.

The least-squares probabilistic classifier (LSPC) is a computationally-efficient alternative to kernel logistic regression. However, to assure its learned probabilities to be non-negative, LSPC involves a post-processing step of rounding up negative parameters to zero, which can unexpectedly influence classification performance. In order to mitigate this problem, we propose a simple alternative scheme that directly rounds up the classifier's negative outputs, not negative parameters. Through extensive experiments including real-world image classification and audio tagging tasks, we demonstrate that the proposed modification significantly improves classification accuracy, while the computational advantage of the original LSPC remains unchanged.

Pedestrian dead reckoning (PDR) based on human gait locomotion is a promising solution for indoor location services, which independently determine the relative position of the user using multiple sensors. Most existing PDR methods assume that all sensors are mounted in a fixed position on the user's body while walking. However, it is inconvenient for a user to mount his/her mobile phone or additional sensor modules in a specific position on his/her body such as the torso. In this paper, we propose a new PDR method and a prototype system suitable for indoor navigation systems on a mobile phone. Our method determines the user's relative position even if the sensors' orientation relative to the user is not given and changes from moment to moment. Therefore, the user does not have to mount the mobile phone containing sensors on the body and can carry it in a natural way while walking, e.g., while swinging the arms. Detailed algorithms, implementation and experimental evaluation results are presented.

Although the Marching Cube (MC) algorithm is very popular for displaying images of voxel-based objects, its slow surface extraction process is usually considered to be one of its major disadvantages. It was pointed out that for the original MC algorithm, we can limit vertex calculations to once per vertex to speed up the surface extraction process, however, it did not mention how this process could be done efficiently. Neither was the reuse of these MC vertices looked into seriously in the literature. In this paper, we propose a “Group Marching Cube” (GMC) algorithm, to reduce the time needed for the vertex identification process, which is part of the surface extraction process. Since most of the triangle-vertices of an iso-surface are shared by many MC triangles, the vertex identification process can avoid the duplication of the vertices in the vertex array of the resultant triangle data. The MC algorithm is usually done through a hash table mechanism proposed in the literature and used by many software systems. Our proposed GMC algorithm considers a group of voxels simultaneously for the application of the MC algorithm to explore interesting features of the original MC algorithm that have not been discussed in the literature. Based on our experiments, for an object with more than 1 million vertices, the GMC algorithm is 3 to more than 10 times faster than the algorithm using a hash table. Another significant advantage of GMC is its compatibility with other algorithms that accelerate the MC algorithm. Together, the overall performance of the original MC algorithm is promoted even further.

Brain-Computer Interfaces (BCIs) are systems that translate one's thoughts into commands to restore control and communication to severely paralyzed people, and they are also appealing to healthy people. One of the challenges is to improve the performance of BCIs, often measured by the accuracy and the trial duration, or the information transfer rate (ITR), i.e., the mutual information per unit time. Since BCIs are communications between a user and a system, error control schemes such as forward error correction and automatic repeat request (ARQ) can be applied to BCIs to improve the accuracy. This paper presents reliability-based ARQ (RB-ARQ), a variation of ARQ designed for BCIs, which employs the maximum posterior probability for the repeat decision. The current results show that RB-ARQ is more effective than the conventional methods, i.e., better accuracy when trial duration was the same, and shorter trial duration when the accuracy was the same. This resulted in a greater information transfer rate and a greater utility, which is a more practical performance measure in the P300 speller task. The results also show that such users who achieve a poor accuracy for some reason can benefit the most from RB-ARQ, which could make BCIs more universal.

Dependence of within-die delay variations on power supply voltage (VDD) is measured down to 0.4 V. The VDD dependence of the within-die delay variation of manual layout and irregular auto place and route (P&R) layout are compared for the first time. The measured relative delay (=sigma/average) variation difference between the manual layout and the P&R layout decreases from 1.56% to 0.07% with reducing VDD from 1.2 V to 0.4 V, because the random delay variations due to the random transistor variations dominate total delay variations instead of the delay variations due to interconnect length variations at low VDD.

This paper presents an efficient yet powerful codebook model, named classified codebook model, to categorize natural scene category. The current codebook model typically resorts to large codebook to obtain higher performance for scene categorization, which severely limits the practical applicability of the model. Our model formulates the codebook model with the theory of vector quantization, and thus uses the famous technique of classified vector quantization for scene-category modeling. The significant feature in our model is that it is beneficial for scene categorization, especially at small codebook size, while saving much computation complexity for quantization. We evaluate the proposed model on a well-known challenging scene dataset: 15 Natural Scenes. The experiments have demonstrated that our model can decrease the computation time for codebook generation. What is more, our model can get better performance for scene categorization, and the gain of performance becomes more pronounced at small codebook size.

Recently, global positioning system (GPS)-enabled mobile units have been popular in wireless mobile communications systems, and thus it becomes possible for mobile units to estimate the velocity before a random access for initiating communications. Motivated by this, we propose a new random access scheme establishing two or more access slot groups corresponding to velocity ranges of mobile units, where each mobile unit attempts a random access only at the slot group corresponding to its current velocity. It gives advantages that access slots can be flexibly grouped according to vehicle traffic conditions and detection algorithms can be optimized to each velocity range.

This letter proposes a new scheduling method to improve scheduling efficiency of EPON. The proposed method uses a credit pool for each optical network unit (ONU) and for each service class. For high scheduling efficiency, the credit pool of an ONU can be negative amount to utilize the unused ONU credits. Also the proposed method dynamically excludes the lowest service class from scheduling to decrease a transmission cycle length. Using simulations, we show that the proposed method is better than the existing methods in mean delay.

We propose a new resource prediction method for the Demand Assigned Multiple Access (DAMA) scheme in satellite networks. Inaccurate prediction of future traffic causes degradation of QoS and utilization due to the long delay in satellite networks. The Dynamic Leveling Scheme (DLS) use a leveling method to modify its prediction to a discrete one to change the precision of the prediction result. This new scheme has two features: 1) It enhances the probability of successful prediction and 2) it can be applied to any type of existing prediction method. Simulations show enhanced utilization and performance of the satellite link.

Since video traffic has become a dominant flow component on the Internet, the Future Internet and New Generation Network must consider delay guarantees as a key feature in their designs. Using the stochastic network optimization, optimal control policies are designed for delay-constrained traffic in single-hop wireless networks. The resulting policy is a scheduling policy with delay guarantees. For a cross-layer design that involves both flow control and scheduling, the resulting policy is a flow control and scheduling policy that guarantees delay constraints and achieves utility performance within O(1/V) of the optimality.

A new compact form of the sliding window recursive least squares (SWRLS) algorithm, the I-SWRLS algorithm, is derived using an indefinite matrix. The resultant algorithm has a form similar to that of the traditional recursive least squares (RLS) algorithm, and is more computationally efficient than the conventional SWRLS algorithm including two Riccati equations. Furthermore, a computationally reduced version of the I-SWRLS algorithm is developed utilizing a shift property of the correlation matrix of input data. The resulting fast algorithm reduces the computational complexity from O(N2) to O(N) per iteration when the filter length (tap number) is N, but retains the same tracking performance as the original algorithm. This fast algorithm is much easier to implement than the existing SWC FTF algorithms.

This paper shows a simple methodology for shortening a ciphertext of reproducible key encapsulation mechanisms. Specifically, it transforms a key encapsulation mechanism having OW-CCCA security and reproducibility into that of IND-CCA secure in the random oracle model whose ciphertext is shorter. Various existing chosen-ciphertext secure key encapsulation mechanisms (in the standard model) are reproducible, and thus their ciphertext can be shortened by the proposed transformation. The transformed scheme requires only one additional hashing for encryption. This property enables us to implement both the original scheme and the transformed scheme into a single chip simultaneously with small gate-size overhead. Using this chip, a sender can flexibly switch schemes to encrypt a message in a message-by-message manner. Such a use of schemes is also analyzed.

This paper presents the design of a wideband low noise amplifier (LNA) for the 3GPP LTE (3rd Generation Partnership Project Long Term Evolution) standard. The proposed LNA uses a common gate topology with a noise cancellation technique for wideband (0.7 to 2.7 GHz) and low noise operation. The capacitive cross coupling technique is adopted for the common gate amplifier. Consequently input matching is achieved with lower transconductance, thereby reducing the power consumption and noise contribution. The LNA is designed in a 0.18 µm process and the simulations show lower than -10 dB input return loss (S11), and 2.42.6 dB noise figure (NF) over the entire operating band (0.72.7 GHz) while drawing 9 mA from a 1.8 V supply.

Identifying the statistical independence of random variables is one of the important tasks in statistical data analysis. In this paper, we propose a novel non-parametric independence test based on a least-squares density ratio estimator. Our method, called least-squares independence test (LSIT), is distribution-free, and thus it is more flexible than parametric approaches. Furthermore, it is equipped with a model selection procedure based on cross-validation. This is a significant advantage over existing non-parametric approaches which often require manual parameter tuning. The usefulness of the proposed method is shown through numerical experiments.

An algorithm for the discrimination between human upstairs and downstairs using a tri-axial accelerometer is presented in this paper, which consists of vertical acceleration calibration, extraction of two kinds of features (Interquartile Range and Wavelet Energy), effective feature subset selection with the wrapper approach, and SVM classification. The proposed algorithm can recognize upstairs and downstairs with 95.64% average accuracy for different sensor locations, i.e. located on the subject's waist belt, in the trousers pocket, and in the shirt pocket. Even for the mixed data from all sensor locations, the average recognition accuracy can reach 94.84%. Experimental results have successfully validated the effectiveness of the proposed method.