A reconfigurable broadband linear power amplifier (PA) for long-range WLAN applications fabricated in a 180nm RF CMOS process is presented here. The proposed reconfigurable in/output matching network provides the PA with broadband capability at the two center frequencies of 0.5GHz and 0.85GHz. The output matching network is realized by a switchable transformer which shows maximum peak passive efficiencies of 65.03% and 73.45% at 0.45GHz and 0.725GHz, respectively. With continuous wave sources, a 1-dB bandwidth (BW1-dB) according to saturated output power is 0.4-1.2GHz, where it shows a minimum output power with a power added efficiency (PAE) of 25.62dBm at 19.65%. Using an adaptive power cell configuration at the common gate transistor, the measured PA under LTE 16-QAM 20MHz (40MHz) signals shows an average output power with a PAE exceeding 20.22 (20.15) dBm with 7.42 (7.35)% at an ACLRE-UTRA of -30dBc, within the BW1-dB.

Generation of secure signatures suitable for spread-spectrum video watermarking is proposed. The method embeds a message, which is a two-dimensional binary pattern, into a three-dimensional volume, such as video, by addition of a signature. The message can be a mark or a logo indicating the copyright information. The signature is generated by shuffling or permuting random matrices along the third or time axis so that the message is extracted when they are accumulated after demodulation by the correct key. In this way, a message is hidden in the signature having equal probability of decoding any variation of the message, where the key is used to determine which one to extract. Security of the proposed method, stemming from the permutation, is evaluated as resistance to blind estimation of secret information. The matrix-based permutation allows the message to survive the spatial down-sampling without sacrificing the security. The downside of the proposed method is that it needs more data or frames to decode a reliable information compared to the conventional spread-spectrum modulation. However this is minimized by segmenting the matrices and applying permutation to sub-matrices independently. Message detectability is theoretically analyzed. Superiority of our method in terms of robustness to blind message estimation and down-sampling is verified experimentally.

This letter proposes a novel speech enhancement system based on the ‘L’ shaped triple-microphone. The modified coherence-based algorithm and the first-order differential beamforming are combined to filter the spatial distributed noise. The experimental results reveal that the proposed algorithm achieves significant performance in spatial filtering under different noise scenarios.

Many linear codes with two or three weights have recently been constructed due to their applications in consumer electronics, communication, data storage system, secret sharing, authentication codes, association schemes, and strongly regular graphs. In this paper, two classes of p-ary linear codes with two or three weights are presented. The first class of linear codes with two or three weights is obtained from a certain non-quadratic function. The second class of linear codes with two weights is obtained from the images of a certain function on $mathbb{F}_{p^m}$. In some cases, the resulted linear codes are optimal in the sense that they meet the Griesmer bound.

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.

As energy efficiency has become a major design constraint or objective, heterogeneous manycore architectures have emerged as mainstream target platforms not only in server systems but also in embedded systems. Manycore accelerators such as GPUs are getting also popular in embedded domains, as well as the heterogeneous CPU cores. However, as the number of cores in an embedded GPU is far less than that of a server GPU, it is important to utilize both heterogeneous multi-core CPUs and GPUs to achieve the desired throughput with the minimal energy consumption. In this paper, we present a case study of mapping LBP-based face detection onto a recent CPU-GPU heterogeneous embedded platform, which exploits both task parallelism and data parallelism to achieve maximal energy efficiency with a real-time constraint. We first present the parallelization technique of each task for the GPU execution, then we propose performance and energy models for both task-parallel and data-parallel executions on heterogeneous processors, which are used in design space exploration for the optimal mapping. The design space is huge since not only processor heterogeneity such as CPU-GPU and big.LITTLE, but also various data partitioning ratios for the data-parallel execution on these heterogeneous processors are considered. In our case study of LBP face detection on Exynos 5422, the estimation error of the proposed performance and energy models were on average -2.19% and -3.67% respectively. By systematically finding the optimal mappings with the proposed models, we could achieve 28.6% less energy consumption compared to the manual mapping, while still meeting the real-time constraint.

Fully integrated CMOS power amplifiers (PAs) with a two-winding and single-winding combined transformer (TS transformer) are presented. The general analysis of the TS transformer and other power-combining transformers, i.e., the series-combining transformer and parallel-combining transformer, is presented in terms of the transformer design parameters. Compared with other power-combining transformers, the proposed power-combining TS transformer offers high-efficiency with a compact form factor. In addition, a fully integrated CMOS PA using the TS transformer with multi-gated transistors (MGTRs) and adaptive bias circuit has been proposed to improve linearity. The proposed PAs are implemented using 65-nm CMOS technology. The implemented PA with the TS transformer achieves a saturated output power of 26.7 dBm with drain efficiency (DE) of 47.7%. The PA achieves 20.13-dBm output power with 21.4% DE while satisfying the -25-dB error vector magnitude (EVM) requirement when tested with the WLAN 802.11g signal. The implemented PA using the TS transformer with MGTRs and adaptive bias circuit achieves the -30-dB EVM requirement up to an output power of 17.13 dBm with 10.43% DE when tested using the WLAN 802.11ac signal.

The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

Let X, Y be two correlated discrete random variables. We consider an estimation of X from encoded data φ(Y) of Y by some encoder function φ(Y). We derive an inequality describing a relation of the correct probability of estimation and the mutual information between X and φ(Y). This inequality may be useful for the secure analysis of crypto system when we use the success probability of estimating secret data as a security criterion. It also provides an intuitive meaning of the secrecy exponent in the strong secrecy criterion.

This paper intends to reduce duration times in typical collective communications. We introduce logical addressing system apart from the physical one and, by rearranging the logical node addresses properly, we intend to reduce communication overheads so that ideal communication is performed. One of the key issues is rearrangement of the logical addressing system. We introduce genetic algorithm (GA) as meta-heuristic solution as well as the random search strategy. Our GA-based method achieves at most 2.50 times speedup in three-traffic-pattern cases.

We normally hold a lot of confidential information in hard disk drives and solid-state drives. When we want to erase such information to prevent the leakage, we have to overwrite the sequence of information with a sequence of symbols independent of the information. The overwriting is needed only at places where overwritten symbols are different from original symbols. Then, the cost of overwrites such as the number of overwritten symbols to erase information is important. In this paper, we clarify the minimum cost such as the minimum number of overwrites to erase information under weak and strong independence criteria. The former (resp. the latter) criterion represents that the mutual information between the original sequence and the overwritten sequence normalized (resp. not normalized) by the length of the sequences is less than a given desired value.

The security of a software program critically depends on the prevention of vulnerabilities in the source code; however, conventional computer programs lack the ability to identify vulnerable code in another program. Our research was aimed at developing a technique capable of generating substitution code for the detection of buffer overflow vulnerability in C/C++ programs. The technique automatically verifies and sanitizes code instrumentation by comparing the result of each candidate variable with that expected from the input data. Our results showed that statements containing buffer overflow vulnerabilities could be detected and prevented by using a substitution variable and by sanitizing code vulnerabilities based on the size of the variables. Thus, faults can be detected prior to execution of the statement, preventing malicious access. Our approach is particularly useful for enhancing software security monitoring, and for designing retrofitting techniques in applications.

In 1973, Arimoto proved the strong converse theorem for the discrete memoryless channels stating that when transmission rate R is above channel capacity C, the error probability of decoding goes to one as the block length n of code word tends to infinity. He proved the theorem by deriving the exponent function of error probability of correct decoding that is positive if and only if R > C. Subsequently, in 1979, Dueck and Körner determined the optimal exponent of correct decoding. Recently the author determined the optimal exponent on the correct probability of decoding have the form similar to that of Dueck and Körner determined. In this paper we give a rigorous proof of the equivalence of the above exponet function of Dueck and Körner to a exponent function which can be regarded as an extention of Arimoto's bound to the case with the cost constraint on the channel input.

In multiprocessors, memory models are introduced to describe the executions of programs among processors. Relaxed memory models, which relax the order of executions, are used in the most of the modern processors, such as ARM and POWER. Due to a relaxed memory model could change the program semantics, the executions of the programs might not be the same as our expectation that should preserve the program correctness. In addition to relaxed memory models, the way to execute an instruction is described by an instruction semantics, which varies among processor architectures. Dealing with instruction semantics among a variety of assembly programs is a challenge for program verification. Thus, this paper proposes a way to verify a variety of assembly programs that are executed under a relaxed memory model. The variety of assembly programs can be abstracted as the way to execute the programs by introducing an operation structure. Besides, there are existing frameworks for modeling relaxed memory models, which can realize program executions to be verified with a program property. Our work adopts an SMT solver to automatically reveal the program executions under a memory model and verify whether the executions violate the program property or not. If there is any execution from the solver, the program correctness is not preserved under the relaxed memory model. To verify programs, an experimental tool was developed to encode the given programs for a memory model into a first-order formula that violates the program correctness. The tool adopts a modeling framework to encode the programs into a formula for the SMT solver. The solver then automatically finds a valuation that satisfies the formula. In our experiments, two encoding methods were implemented based on two modeling frameworks. The valuations resulted by the solver can be considered as the bugs occurring in the original programs.

Impedance mismatching is a major obstacle hindering the application of DC power line communication (DC-PLC) due to the unpredictability of access impedance and random loads. Researchers and manufacturers typically estimate the power line impedance level and use a fixed single-winding coupler to carry out signal coupling, which does not achieve accurate impedance matching and leads to large signal attenuation and low reliability. In this paper, a lumped parameter power line communication model for DC-PLC is established in which the optimal receiver winding ratio is derived from equivalent circuits. A modifiable impedance matching coupler was designed to achieve dynamic impedance matching, and a series of simulations were run to analyze the relationship among optimal winding ratio, power line impedance and series loads. The performance of different winding ratio couplers under varied frequency and load impedance was also measured in a laboratory environment to find that adopting the modifiable impedance matching coupler is indeed a useful strategy for achieving adaptive impedance matching with maximum signal power transfer.

Depth image based rendering (DIBR), which is utilized to render virtual views with a color image and the corresponding depth map, is one of the key procedures in the 2D to 3D conversion process. However, some troubling problems, such as depth edge misalignment, disocclusion occurrences and cracks at resampling, still exist in current DIBR systems. To solve these problems, in this paper, we present a robust depth image based rendering scheme for stereoscopic view synthesis. The cores of the proposed scheme are two depth map filters which share a common domain transform based filtering framework. As a first step, a filter of this framework is carried out to realize texture-depth boundary alignments and directional disocclusion reduction smoothing simultaneously. Then after depth map 3D warping, another adaptive filter is used on the warped depth maps with delivered scene gradient structures to further diminish the remaining cracks and noises. Finally, with the optimized depth map of the virtual view, backward texture warping is adopted to retrieve the final texture virtual view. The proposed scheme enables to yield visually satisfactory results for high quality 2D to 3D conversion. Experimental results demonstrate the excellent performances of the proposed approach.

In this paper, for any given prime power q, using Helleseth-Gong sequences with ideal auto-correlation property, we propose a class of perfect sequences of length (qm-1)/(q-1). As an application, a subclass of constructed perfect sequences is used to design optimal and perfect difference systems of sets.

We analyze the behaviors of the FXLMS algorithm using a statistical-mechanical method. The cross-correlation between a primary path and an adaptive filter and the autocorrelation of the adaptive filter are treated as macroscopic variables. We obtain simultaneous differential equations that describe the dynamical behaviors of the macroscopic variables under the condition that the tapped-delay line is sufficiently long. The obtained equations are deterministic and closed-form. We analytically solve the equations to obtain the correlations and finally compute the mean-square error. The obtained theory can quantitatively predict the behaviors of computer simulations including the cases of both not only white but also nonwhite reference signals. The theory also gives the upper limit of the step size in the FXLMS algorithm.

Boolean functions used in stream ciphers and block ciphers should have high second-order nonlinearity to resist several known attacks and some potential attacks which may exist but are not yet efficient and might be improved in the future. The second-order nonlinearity of Boolean functions also plays an important role in coding theory, since its maximal value equals the covering radius of the second-order Reed-Muller code. But it is an extremely hard task to calculate and even to bound the second-order nonlinearity of Boolean functions. In this paper, we present a lower bound on the second-order nonlinearity of the generalized Maiorana-McFarland Boolean functions. As applications of our bound, we provide more simpler and direct proofs for two known lower bounds on the second-order nonlinearity of functions in the class of Maiorana-McFarland bent functions. We also derive a lower bound on the second-order nonlinearity of the functions which were conjectured bent by Canteaut and whose bentness was proved by Leander, by further employing our bound.

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.