In the successive refinement problem, a fixed-length sequence emitted from an information source is encoded into two codewords by two encoders in order to give two reconstructions of the sequence. One of two reconstructions is obtained by one of two codewords, and the other reconstruction is obtained by all two codewords. For this coding problem, we give non-asymptotic inner and outer bounds on pairs of numbers of codewords of two encoders such that each probability that a distortion exceeds a given distortion level is less than a given probability level. We also give a general formula for the rate-distortion region for general sources, where the rate-distortion region is the set of rate pairs of two encoders such that each maximum value of possible distortions is less than a given distortion level.

Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.

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.

In this paper, we propose a design of reversible adder/subtractor blocks and arithmetic logic units (ALUs). The main concept of our approach is different from that of the existing related studies; we emphasize the function design. Our approach of investigating the reversible functions includes (a) the embedding of irreversible functions into incompletely-specified reversible functions, (b) the operation assignment, and (c) the permutation of function outputs. We give some extensions of these techniques for further improvements in the design of reversible functions. The resulting reversible circuits are smaller than that of the existing design in terms of the number of multiple-control Toffoli gates. To evaluate the quantum cost of the obtained circuits, we convert the circuits to reduced quantum circuits for experiments. The results also show the superiority of our realization of adder/subtractor blocks and ALUs in quantum cost.

Spectrum sensing is the first task performed by cognitive radio (CR) networks. In this paper we propose a spectrum sensing algorithm for orthogonal frequency division multiplex (OFDM) signal based on deep learning and covariance matrix graph. The advantage of deep learning in image processing is applied to the spectrum sensing of OFDM signals. We start by building the spectrum sensing model of OFDM signal, and then analyze structural characteristics of covariance matrix (CM). Once CM has been normalized and transformed into a gray level representation, the gray scale map of covariance matrix (GSM-CM) is established. Then, the convolutional neural network (CNN) is designed based on the LeNet-5 network, which is used to learn the training data to obtain more abstract features hierarchically. Finally, the test data is input into the trained spectrum sensing network model, based on which spectrum sensing of OFDM signals is completed. Simulation results show that this method can complete the spectrum sensing task by taking advantage of the GSM-CM model, which has better spectrum sensing performance for OFDM signals under low SNR than existing methods.

Network coding signature (NCS) scheme is a cryptographic tool for network coding against pollution attacks. In [5], Chang et al. first introduced the related-key attack (RKA) to the NCS schemes and tried to give an instantiation of it. However, their instantiation is based on the random oracle (RO) model. In this letter, we present a standard-model instantiation. In particular, we prove that standard-model-based NCS scheme introduced by Boneh et al. in [4] (BFKW scheme, for short) can achieve Φ-RKA security if the underlying signature scheme is also Φ-RKA secure, where Φ is any family of functions defined on signing keys of NCS schemes.

Multiple input multiple output with orthogonal frequency division multiplexing (MIMO-OFDM) is used in various parts of wireless communication systems. Because the MIMO-OFDM system simultaneously transmits parallel data streams and each receive antenna receives all data streams at one time, the detection ability of the receiver is very important. Among the detection schemes suitable for OFDM, maximum likelihood (ML) detection has optimal performance, but its complexity is so high that it is infeasible. Linear detection schemes such as zero-forcing (ZF) and minimum mean square error (MMSE) have low complexity, but also low performance. Among non-linear detection schemes, the near-ML detection which is the sphere detection (SD) or the QR decomposition with M algorithm (QRD-M) also has optimal performance but the complexity of SD and QRD-M detection is also too high. Other non-linear detection schemes like successive interference cancellation (SIC) detection have low complexity. However, the performance of SIC detection is lower than other non-linear detection schemes. In this paper, selectively iterative detection is proposed for MIMO-OFDM system; it offers low complexity and good performance.

This paper proposes a novel dynamic channel assignment scheme named interference-aware dynamic channel assignment (IA-DCA) for the downlink of enterprise small-cell networks (ESNs) that employ orthogonal frequency division multiple access (OFDMA) and frequency division duplexing (FDD). In ESNs, a lot of small-cell access points (SAPs) are densely deployed in a building and thus small-cell user equipments (SUEs) have more serious co-tier interference from neighbor SAPs than the conventional small-cell network. Therefore, in the proposed IA-DCA scheme, a local gateway (LGW) dynamically assigns different numbers of subchannel groups to SUEs through their serving SAPs according to the given traffic load and interference information. Through simulation results, we show that the proposed IA-DCA scheme outperforms other dynamic channel assignment schemes based on graph coloring algorithm in terms of the mean SUE capacity, fairness, and mean SAP channel utilization.

Indoor fingerprint location based on WiFi in large-scale indoor parking lots is more and more widely employed for vehicle lookup. However, the challenge is to ensure the location functionality because of the particularity and complexities of the indoor parking lot environment. To reduce the need to deploy of reference points (RPs) and the offline sampling workload, a partition-fitting fingerprint algorithm (P-FP) is proposed. To improve the location accuracy of the target, the PS-FP algorithm, a sampling importance resampling (SIR) particle filter with threshold based on P-FP, is further proposed. Firstly, the entire indoor parking lot is partitioned and the environmental coefficients of each partitioned section are gained by using the polynomial fitting model. To improve the quality of the offline fingerprint database, an error characteristic matrix is established using the difference between the fitting values and the actual measured values. Thus, the virtual RPs are deployed and C-means clustering is utilized to reduce the amount of online computation. To decrease the fluctuation of location coordinates, the SIR particle filter with a threshold setting is adopted to optimize the location coordinates. Finally, the optimal threshold value is obtained by comparing the mean location error. Test results demonstrated that PS-FP could achieve high location accuracy with few RPs and the mean location error is only about 0.7m. The cumulative distribution function (CDF) show that, using PS-FP, 98% of location errors are within 2m. Compared with the weighted K-nearest neighbors (WKNN) algorithm, the location accuracy by PS-FP exhibit an 84% improvement.

Generalized recursive circulant graphs (GRCGs for short) are a generalization of recursive circulant graphs and provide a new type of topology for interconnection networks. A graph of n vertices is said to be s-pancyclic for some $3leqslant sleqslant n$ if it contains cycles of every length t for $sleqslant tleqslant n$. The pancyclicity of recursive circulant graphs was investigated by Araki and Shibata (Inf. Process. Lett. vol.81, no.4, pp.187-190, 2002). In this paper, we are concerned with the s-pancyclicity of GRCGs.

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.

Reverberation chambers (RCs) are used widely in the electromagnetic measurement area. An RC is designed to have a long reverberation time, generate numerous modes, and provide good field uniformity within the chamber. The purpose of this paper is to describe the design process and measurement of the KRISS Reverberation Chamber (KRC). KRC models with 4.5m × 3.4m × 2.8m dimensions are simulated by 3D numerical simulation software. The field uniformity and correlation coefficient are then analyzed at 200MHz to obtain the optimized model. The simulation results show good performance in terms of field uniformity and are confirmed by measurement from 200MHz to 1GHz. The lowest usable frequency (LUF) of KRC was confirmed by field uniformity to be 200MHz. However, the stirrer correlation coefficient results show good performance above 300MHz.

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.

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.

The 1-bit digital radio frequency (DRF) transmitter using a band-pass delta-sigma modulator (BP-DSM) can output a radio frequency (RF) signal carrying a binary data stream with a constant data rate regardless of the carrier frequency, which makes it possible to transmit RF signals over digital optical links with a constant bit rate. However, the optical link requires a line coding, such as 8B10B or 64B66B, to constrain runlength and disparity, and the line coding corrupts the DRF power spectrum owing to additional or encoded data. This paper proposes a new line coding for BP-DSM, which is able to control the runlength and the disparity of the 1-bit data stream by adding a notch filter to the BP-DSM that suppresses the low frequency components. The notch filter stimulates the data change and balances the direct current (DC) components. It is demonstrated that the proposed line coding shortens the runlength from 50 bits to less than 8 bits and reduces the disparity from several thousand bits to 5 bits when the 1-bit DRF transmitter outputs an LTE signal with 5 MHz bandwidth, when using carrier frequencies from 0.5GHz to 2GHz and an output power variation of 60dB.

Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called “AnnexML”. At the training step, AnnexML constructs a k-nearest neighbor graph of label vectors and attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed by using an approximate nearest neighbor search method that efficiently explores the learned k-nearest neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world data sets and compared our method with recent state-of-the-art methods. Experimental results show that our AnnexML can significantly improve prediction accuracy, especially on data sets that have a larger label space. In addition, AnnexML improves the trade-off between prediction time and accuracy. At the same level of accuracy, the prediction time of AnnexML was up to 58 times faster than that of SLEEC, a state-of-the-art embedding-based method.

Recently, Super-resolution convolutional neural network (SRCNN) is widely known as a state of the art method for achieving single-image super resolution. However, performance problems such as jaggy and ringing artifacts exist in SRCNN. Moreover, in order to realize a real-time upconverting system for high-resolution video streams such as 4K/8K 60 fps, problems such as processing delay and implementation cost remain. In the present paper, we propose high-performance super-resolution via patch-based deep neural network (SR-PDNN) rather than a convolutional neural network (CNN). Despite the very simple end-to-end learning system, the SR-PDNN achieves higher performance than the conventional CNN-based approach. In addition, this system is suitable for ultra-low-delay video processing by hardware implementation using an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).

Discrete Gaussian is a cornerstone of many lattice-based cryptographic constructions. Aiming at the orthogonal lattice of a vector, we propose a discrete Gaussian rejection sampling algorithm, by modifying the dynamic programming process for subset sum problems. Within O(nq2) time, our algorithm generates a distribution statistically indistinguishable from discrete Gaussian at width s>ω(log n). Moreover, we apply our sampling algorithm to general high-dimensional dense lattices, and orthogonal lattices of matrices $matAinZ_q^{O(1) imes n}$. Compared with previous polynomial-time discrete Gaussian samplers, our algorithm does not rely on the short basis.

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.

We examined single crystal growth of benzothienobenzothiophene-based organic semiconductors by solution coating method using liquid crystal and investigated its electrical characteristics. As the results, we revealed that the averaged mobility in the saturation region reached 2.08 cm2/Vs along crystalline b-axis, and 1.08 cm2/Vs along crystalline a-axis.