Human gait analysis has been widely used in medical and health fields. It is essential to extract spatio-temporal gait features (e.g., single support duration, step length, and toe angle) by partitioning the gait phase and estimating the footprint position/orientation in such fields. Therefore, we propose a method to partition the gait phase given a foot position sequence using mutually constrained piecewise linear approximation with dynamic programming, which not only represents normal gait well but also pathological gait without training data. We also propose a method to detect footprints by accumulating toe edges on the floor plane during stance phases, which enables us to detect footprints more clearly than a conventional method. Finally, we extract four spatial/temporal gait parameters for accuracy evaluation: single support duration, double support duration, toe angle, and step length. We conducted experiments to validate the proposed method using two types of gait patterns, that is, healthy and mimicked hemiplegic gait, from 10 subjects. We confirmed that the proposed method could estimate the spatial/temporal gait parameters more accurately than a conventional skeleton-based method regardless of the gait pattern.

We explore ways to optimize online, permutation-based authenticated encryption (AE) schemes for lightweight applications. The lightweight applications demand that AE schemes operate in resource-constrained environments, which raise two issues: 1) implementation costs must be low, and 2) ensuring proper use of a nonce is difficult due to its small size and lack of randomness. Regarding the implementation costs, recently it has been recognized that permutation-based (rather than block-cipher-based) schemes frequently show advantages. However, regarding the security under nonce misuse, the standard permutation-based duplex construction cannot ensure confidentiality. There exists one permutation-based scheme named APE which offers certain robustness against nonce misuse. Unfortunately, the APE construction has several drawbacks such as ciphertext expansion and bidirectional permutation circuits. The ciphertext expansion would require more bandwidth, and the bidirectional circuits would require a larger hardware footprint. In this paper, we propose new constructions of online permutation-based AE that require less bandwidth, a smaller hardware footprint and lower computational costs. We provide security proofs for the new constructions, demonstrating that they are as secure as the APE construction.

Aiming to solve the input/output (I/O) bottleneck concerning next-generation interconnections, 5×5-millimeters-squared silicon-photonics-based chip-scale optical transmitters/receivers (TXs/RXs) — called “optical I/O cores” — were developed. In addition to having a compact footprint, by employing low-power-consumption integrated circuits (ICs), as well as providing multimode-fiber (MMF) transmission in the O band and a user-friendly interface, the developed optical I/O cores allow common ease of use with applications such as multi-chip modules (MCMs) and active optical cables (AOCs). The power consumption of their hybrid-integrated ICs is 5mW/Gbps. Their high-density user-friendly optical interface has a spot-size-converter (SSC) function and permits the physical contact against the outer waveguides. As a result, they provide large enough misalignment tolerance to allow use of passive alignment and visual alignment. In a performance test, they demonstrated 25-Gbps/ch error-free operation over 300-m MMF.

This paper presents work on integrating wireless 3-D interconnection interface, namely ThruChip Interface (TCI), in three-dimensional field-programmable gate array (3-D FPGA) exploration tool (TPR). TCI is an emerging 3-D IC integration solution because of its advantages over cost, flexibility, reliability, comparable performance, and energy dissipation in comparison to through-silicon-via (TSV). Since the communication bandwidth of TCI is much higher than FPGA internal logic signals, in order to fully utilize its bandwidth, the time-division multiplexing (TDM) scheme is adopted. The experimental results show 25% on average and 58% at maximum path delay reduction over 2-D FPGA when five layers are used in TCI based 3-D FPGA architecture. Although the performance of TCI based 3-D FPGA architecture is 8% below that of TSV based 3-D FPGA on average, TCI based architecture can reduce active area consumed by vertical communication channels by 42% on average in comparison to TSV based architecture and hence leads to better delay and area product.

A novel method is proposed that can estimate the tag population in Radio Frequency Identification (RFID) systems by using a Hadamard code for the tag response. We formulate the maximum likelihood estimator for the tag population using the number of observed footprints. The lookup table of the estimation algorithm has low complexity. Simulation results show that the proposed estimator performs considerably better than the conventional schemes.

We present a model of quantization noise in block-coded videos with some assumptions in wavelet domain and propose a postprocessing method to reduce the quantization noise based on the model. A frame of video sequences is considered as a set of one-dimensional (1-D) horizontal and vertical signals. The quantization noise is considered as the sum of the blocking noise and the remainder noise. We model the blocking noise as an impulse or that along with a dispersed impulse at each block boundary in the wavelet domain. The validity of the blocking noise model is investigated. We also model the remainder noise as white Gaussian noise at non-edge pixels in the wavelet domain. Whether the model accommodates well to the remainder noise or not is also examined. The blocking noise is reduced by subtracting a profile, whose strength is adaptively estimated, at each block boundary from the coded signal. The remainder noise then is reduced by a soft-thresholding. We also propose a fast algorithm for the proposed method by approximating coefficients of shape profiles used in blocking noise reduction and inverse wavelet transform (WT) filters used in remainder noise reduction. The performance is evaluated for QCIF video sequences coded by H.263 TMN5 with quantization parameter (QP) in the range of 5-25 and is compared to that of the MPEG-4 verification model (VM) post-filter. Experimental results show that the proposed method yields not only PSNR improvement of maximum 0.5 dB over the VM post-filter but also subjective quality nearly free of the blocking artifact and edge blur.

By modelling the quantization error as additive white noise in the transform domain, Wiener filter is used to reduce quantization noise for DCT coded images in DCT domain. Instead of deriving the spectrum of the transform coefficient, a DPCM loop is used to whiten the quantized DCT coefficients. The DPCM loop predicts the mean for each coefficient. By subtracting the mean, the quantized DCT coefficient is converted into the sum of prediction error and quantization noise. After the DPCM loop, the prediction error can be assumed uncorrelated to make the design of the subsequent Wiener filter easy. The Wiener filter is applied to remove the quantization noise to restore the prediction error. The original coefficient is reconstructed by adding the DPCM predicted mean with the restored prediction error. To increase the prediction accuracy, the decimated DCT coefficients in each subband are interpolated from the overlapped blocks.

A new excitation enhancement technique based on a harmonic model is proposed in this paper to improve the speech quality of low-bit-rate speech coders. This technique is employed only in the decoding process of speech coders and improves high-frequency components of excitation. We develop the procedure of harmonic model parameters estimation and harmonic generation and apply the technique to a current state-of-art low bit rate speech coder. Experiments on spectrum reading and spectrum distortion measurement show that the proposed excitation enhancement technique improves speech quality.

Many diverse methods have been developed in the field of biometric identification as a greater emphasis is placed on human-friendliness in the area of intelligent systems. One emerging method is the use of human footprint. However, in the previous research, there were some limitations resulting from the spatial resolution of sensors. One possible method to overcome this limitation is through the use additional information such as dynamic walking information in dynamic footprint. In this study, we suggest a new person recognition scheme based on overlapped foot shape and COP (Center Of Pressure) trajectory during one-step walking. And, we show the usefulness of the suggested method, obtaining a 98.6% recognition rate in our experiment with eleven people.

A postprocessing algorithm is presented for blocking artifact reduction in block-coded images using the adaptive filters along the pattern of neighborhood blocks. Blocking artifacts appear as irregular high-frequency components at block boundaries, thereby reducing the noncorrelation between blocks due to the independent quantization process of each block. Accordingly, block-adaptive filtering is proposed to remove such components and enable similar frequency distributions within two neighborhood blocks and a high correlation between blocks. This type of filtering consists of inter-block filtering to remove blocking artifacts at the block boundaries and intra-block filtering to remove ringing noises within a block. First, each block is classified into one of seven classes based on the characteristics of the DCT coefficient and MV (motion vector) received in the decoder. Thereafter, adaptive intra-block filters, approximated to the normalized frequency distributions of each class, are applied adaptively according to the various patterns and frequency distributions of each block as well as the filtering directions in order to reduce the blocking artifacts. Finally, intra-block filtering is performed on those blocks classified as complex to reduce any ringing noise without blurring the edges. Experimental tests confirmed the effectiveness of the proposed algorithm.

Based on a previous work on handwritten Japanese kanji character recognition, a postprocessing system for handwritten Japanese address recognition is proposed. Basically, the recognition system is composed of CombNET-II, a general-purpose large-scale character recognizer and MMVA, a modified majority voting system. Beginning with a set of character candidates, produced by a character recognizer for each character that composes the input word and a lexicon, an interpretation to the input word is generated. MMVA is used in the postprocessing stage to select the interpretation that accumulates the highest score. In the case of more than one possible interpretation, the Conflict Analyzing System calls the character recognizer again to generate scores for each character that composes each interpretation to determine the final output word. The proposed word recognition system was tested with 2 sets of handwritten Japanese city names, and recognition rates higher than 99% were achieved, demonstrating the effectiveness of the method.

A new postprocessing method using interpolated n-gram model for Japanese documents is proposed. The method has the advantages over conventional approaches in enabling high-speed, knowledge-free processing. In parameter estimation of an n-gram model for a large size of vocabulary, it is difficult to obtain sufficient training samples. To overcome poverty of samples, two smoothing methods for Japanese character trigram model are evaluated, and the superiority of deleted interpolation method is shown by using perplexity. A document recognition system based on the trigram model is constructed, which finds maximum likelihood solutions through Viterbi algorithm. Experimental results for three kinds of documents show that the performance is high when using deleted interpolation method for smoothing. 90% of OCR errors are corrected for the documents similar to training text data, and 75% of errors are corrected for the documents not so similar to training text data.