The potential transmission capacity of a standard single-mode fiber peaks at around 100Tb/s owing to fiber nonlinearity and the bandwidth limitation of amplifiers. As the last frontier of multiplexing, space-division multiplexing (SDM) has been studied intensively in recent years. Although there is still time to deploy such a novel fiber communication infrastructure; basic research on SDM has been carried out. Therefore, a comprehensive review is worthwhile at this time toward further practical investigations.

This paper describes a design methodology for process variation aware D-Flip-Flop (DFF) using regression analysis. We propose to use a regression analysis to model the worst-case delay characteristics of a DFF under process variation. We utilize the regression equation for transistor width tuning of the DFF to improve its worst-case delay performance. Regression analysis can not only identify the performance-critical transistors inside the DFF, but also shows these impacts on DFF delay performance in quantitative form. Proposed design methodology is verified using Monte-Carlo simulation. The result shows the proposed method achieves to design a DFF which has similar or better delay characteristics in comparison with the DFF designed by an experienced cell designer.

A parallel phrase matching (PM) engine for dictionary compression is presented. Hardware based parallel chaining hash can eliminate erroneous PM results raised by hash collision; while newly-designed storage architecture holding PM results solved the data dependency issue; Thus, the average compression speed is increased by 53%.

In this paper, the analog code modulation characteristics of distributed-based transversal filters (DTFs) suitable for use in spectrally encoded CDMA systems are presented. The DTF is verified as an appropriate method to use in high-speed CDMA systems as opposed to previously proposed methods, which are intended for Direct Sequence (DS) CDMA systems. The large degree of freedom of DTF design permits controlling the filter pulse response to generate well specified temporal phase-coded signals. A decoder structure that performs bipolar detection of user subbands giving rise to a Spectral-Amplitude Encoded CDMA system is considered. Practical implementations require truncating the spreading signals by a time window of duration equal to the span time of the tapped delay line. Filter functions are chosen to demodulate the matched channel and achieve improved user interference rejection avoiding the need for transversal filters featuring a large number of taps. As a proof-of-concept of the electronic SAE scheme, practical circuit designs are developed at low speeds (3-dB point at 1 GHz) demonstrating the viability of the proposal.

Forward Error Correction (FEC) schemes have played an important role in intensity-modulation direct-detection (IM/DD) Visible Light Communication (VLC) systems. While hard-decision FEC schemes are inferior to soft-decision FEC codes in terms of decoding performance, they are widely used in these VLC systems because receivers are only capable of recognizing logical values 0 and 1. In this letter, we propose a method to calculate the log-likelihood ratios (LLR) values which are used as input of soft-decision FEC decoders. Simulation results show that Polar decoder using proposed method performs better than that of using the hard-decision technique.

In this paper, we propose a generation method of new mutually zero-correlation zone set of optical orthogonal sequences (MZCZ-OOS) consisting of binary and bi-phase sequence pairs based on the optical zero-correlation zone (ZCZ) sequence set. The MZCZ-OOS is composed of several small orthogonal sequence sets. The sequences that belong to same subsets are orthogonal, and there is a ZCZ between the sequence that belong to different subsets. The set is suitable for the M-ary quasi-synchronous optical code-division multiple access (M-ary/QS-OCDMA) system. The product of set size S and family size M of proposed MMZCZ-OOS is more than the upper bound of optical ZCZ sequence set, and is fewer than the that of optical orthogonal sequence set.

Lasso regression based on the L1 regularization is one of the most popular sparse estimation methods. It is often required to set appropriately in advance the regularization parameter that determines the degree of regularization. Although the empirical Bayes approach provides an effective method to estimate the regularization parameter, its solution has yet to be fully investigated in the lasso regression model. In this study, we analyze the empirical Bayes estimator of the one-parameter model of lasso regression and show its uniqueness and its properties. Furthermore, we compare this estimator with that of the variational approximation, and its accuracy is evaluated.

This paper proposes a block-permutation-based encryption (BPBE) scheme for the encryption-then-compression (ETC) system that enhances the color scrambling. A BPBE image can be obtained through four processes, positional scrambling, block rotation/flip, negative-positive transformation, and color component shuffling, after dividing the original image into multiple blocks. The proposed scheme scrambles the R, G, and B components independently in positional scrambling, block rotation/flip, and negative-positive transformation, by assigning different keys to each color component. The conventional scheme considers the compression efficiency using JPEG and JPEG 2000, which need a color conversion before the compression process by default. Therefore, the conventional scheme scrambles the color components identically in each process. In contrast, the proposed scheme takes into account the RGB-based compression, such as JPEG-LS, and thus can increase the extent of the scrambling. The resilience against jigsaw puzzle solver (JPS) can consequently be increased owing to the wider color distribution of the BPBE image. Additionally, the key space for resilience against brute-force attacks has also been expanded exponentially. Furthermore, the proposed scheme can maintain the JPEG-LS compression efficiency compared to the conventional scheme. We confirm the effectiveness of the proposed scheme by experiments and analyses.

In this letter, a generic method to construct mutually orthogonal binary zero correlation zone (ZCZ) sequence sets from mutually orthogonal complementary sequence sets (MOCSSs) with certain properties is presented at first. Then MOCSSs satisfying conditions are generated from binary orthogonal matrices with order N×N, where N=p-1, p is a prime. As a result, mutually orthogonal binary ZCZ sequence sets with parameters (2N2,N,N+1)-ZCZ can be obtained, the number of ZCZ sets is N. Note that each single ZCZ sequence set is optimal with respect to the theoretical bound.

A fusion framework between CNN and RNN is proposed dedicatedly for air-writing recognition. By modeling the air-writing using both spatial and temporal features, the proposed network can learn more information than existing techniques. Performance of the proposed network is evaluated by using the alphabet and numeric datasets in the public database namely the 6DMG. Average accuracy of the proposed fusion network outperforms other techniques, i.e. 99.25% and 99.83% are observed in the alphabet gesture and the numeric gesture, respectively. Simplified structure of RNN is also proposed, which can attain about two folds speed-up of ordinary BLSTM network. It is also confirmed that only the distance between consecutive sampling points is enough to attain high recognition performance.

We studied complicated superstable periodic orbits (SSPOs) in a spiking neuron model with a rectangular threshold signal. The neuron exhibited SSPOs with various periods that changed dramatically when we varied the parameter space. Using a one-dimensional return map defined by the spike phase, we evaluated period changes and showed its complicated distribution. Finally, we constructed a test circuit to confirm the typical phenomena displayed by the mathematical model.

This paper presents an efficient acceleration algorithm for Lloyd-type k-means clustering, which is suitable to a large-scale and high-dimensional data set with potentially numerous classes. The algorithm employs a novel projection-based filter (PRJ) to avoid unnecessary distance calculations, resulting in high-speed performance keeping the same results as a standard Lloyd's algorithm. The PRJ exploits a summable lower bound on a squared distance defined in a lower-dimensional space to which data points are projected. The summable lower bound can make the bound tighter dynamically by incremental addition of components in the lower-dimensional space within each iteration although the existing lower bounds used in other acceleration algorithms work only once as a fixed filter. Experimental results on large-scale and high-dimensional real image data sets demonstrate that the proposed algorithm works at high speed and with low memory consumption when large k values are given, compared with the state-of-the-art algorithms.

By exploiting the inherent sparsity of wireless channels, the channel estimation in an orthogonal frequency division multiplexing (OFDM) system can be cast as a compressed sensing (CS) problem to estimate the channel more accurately. Practically, matching pursuit algorithms such as orthogonal matching pursuit (OMP) are used, where path delays of the channel is guessed based on correlation values for every quantized delay with residual. This full search approach requires a predefined grid of delays with high resolution, which induces the high computational complexity because correlation values with residual at a huge number of grid points should be calculated. Meanwhile, the correlation values with high resolution can be obtained by interpolation between the correlation values at a low resolution grid. Also, the interpolation can be implemented with a low pass filter (LPF). By using this fact, in this paper we substantially reduce the computational complexity to calculate the correlation values in channel estimation using CS.

We propose the cube-based perceptual encryption (C-PE), which consists of cube scrambling, cube rotation, cube negative/positive transformation, and cube color component shuffling, and describe its application to the encryption-then-compression (ETC) system of Motion JPEG (MJPEG). Especially, cube rotation replaces the blocks in the original frames with ones in not only the other frames but also the depth-wise cube sides (spatiotemporal sides) unlike conventional block-based perceptual encryption (B-PE). Since it makes intra-block observation more difficult and prevents unauthorized decryption from only a single frame, it is more robust than B-PE against attack methods without any decryption key. However, because the encrypted frames including the blocks from the spatiotemporal sides affect the MJPEG compression performance slightly, we also devise a version of C-PE with no spatiotemporal sides (NSS-C-PE) that hardly affects compression performance. C-PE makes the encrypted video sequence robust against the only single frame-based algorithmic brute force (ABF) attack with only 21 cubes. The experimental results show the compression efficiency and encryption robustness of the C-PE/NSS-C-PE-based ETC system. C-PE-based ETC system shows mixed results depending on videos, whereas NSS-C-PE-based ETC system shows that the BD-PSNR can be suppressed to about -0.03dB not depending on videos.

A new projection-based color-to-gray conversion method is proposed in this letter. In the proposed method, an objective function which considers color contrasts in an input image is defined. Projection coefficients are determined by minimizing the objective function. Experimental results show the validity of the proposed method.

When trying to estimate time-varying multipath channels by applying a basis expansion model (BEM) in orthogonal frequency division multiplexing (OFDM) systems, pilot clusters are contaminated by inter-carrier interference (ICI). The pilot cluster ICI (PC-ICI) degrades the estimation accuracy of BEM coefficients, which degrades system performance. In this paper, a PC-ICI suppression scheme is proposed, in which two coded symbols defined as weighted sums of data symbols are inserted on both sides of each pilot cluster. Under the assumption that the channel has Flat Doppler spectrum, the optimized weight coefficients are obtained by an alternating iterative optimization algorithm, so that the sum of the PC-ICI generated by the encoded symbols and the data symbols is minimized. By approximating the optimized weight coefficients, they are independent of the channel tap power. Furthermore, it is verified that the proposed scheme is robust to the estimation error of the normalized Doppler frequency offset and can be applied to channels with other types of Doppler spectra. Numerical simulation results show that, compared with the conventional schemes, the proposed scheme achieves significant improvements in the performance of PC-ICI suppression, channel estimation and system bit-error-ratio (BER).

A summer-embedded sense amplifier (SE SA) is proposed to reduce feedback loop delay (TFB) in a decision feedback equalizer (DFE). In the SE SA, the position of the ISI compensator is changed from the latch input to the latch output, and hence the TFB is reduced. The simulated DFE achieves 32Gb/s and 66fJ/b with a 1-V 65-nm CMOS process.

The number of IT services that use machine learning (ML) algorithms are continuously and rapidly growing, while many of them are used in practice to make some type of predictions from personal data. Not surprisingly, due to this sudden boom in ML, the way personal data are handled in ML systems are starting to raise serious privacy concerns that were previously unconsidered. Recently, Fredrikson et al. [USENIX 2014] [CCS 2015] proposed a novel attack against ML systems called the model inversion attack that aims to infer sensitive attribute values of a target user. In their work, for the model inversion attack to be successful, the adversary is required to obtain two types of information concerning the target user prior to the attack: the output value (i.e., prediction) of the ML system and all of the non-sensitive values used to learn the output. Therefore, although the attack does raise new privacy concerns, since the adversary is required to know all of the non-sensitive values in advance, it is not completely clear how much risk is incurred by the attack. In particular, even though the users may regard these values as non-sensitive, it may be difficult for the adversary to obtain all of the non-sensitive attribute values prior to the attack, hence making the attack invalid. The goal of this paper is to quantify the risk of model inversion attacks in the case when non-sensitive attributes of a target user are not available to the adversary. To this end, we first propose a general model inversion (GMI) framework, which models the amount of auxiliary information available to the adversary. Our framework captures the model inversion attack of Fredrikson et al. as a special case, while also capturing model inversion attacks that infer sensitive attributes without the knowledge of non-sensitive attributes. For the latter attack, we provide a general methodology on how we can infer sensitive attributes of a target user without knowledge of non-sensitive attributes. At a high level, we use the data poisoning paradigm in a conceptually novel way and inject malicious data into the ML system in order to modify the internal ML model being used into a target ML model; a special type of ML model which allows one to perform model inversion attacks without the knowledge of non-sensitive attributes. Finally, following our general methodology, we cast ML systems that internally use linear regression models into our GMI framework and propose a concrete algorithm for model inversion attacks that does not require knowledge of the non-sensitive attributes. We show the effectiveness of our model inversion attack through experimental evaluation using two real data sets.

In this letter, we consider several optimization problems associated with the configuration of grouping-based framed slotted ALOHA protocols. Closed-form formulas for determining the optimal values of system parameters such as the process termination time and confidence levels for partitioned groups are presented. Further, we address the maximum group size required for meaningful grouping gain and the effectiveness of the grouping technique in light of signaling overhead.

This paper proposes a novel pseudo multi-exposure image fusion method based on a single image. Multi-exposure image fusion is used to produce images without saturation regions, by using photos with different exposures. However, it is difficult to take photos suited for the multi-exposure image fusion when we take a photo of dynamic scenes or record a video. In addition, the multi-exposure image fusion cannot be applied to existing images with a single exposure or videos. The proposed method enables us to produce pseudo multi-exposure images from a single image. To produce multi-exposure images, the proposed method utilizes the relationship between the exposure values and pixel values, which is obtained by assuming that a digital camera has a linear response function. Moreover, it is shown that the use of a local contrast enhancement method allows us to produce pseudo multi-exposure images with higher quality. Most of conventional multi-exposure image fusion methods are also applicable to the proposed multi-exposure images. Experimental results show the effectiveness of the proposed method by comparing the proposed one with conventional ones.