This brief paper describes a background calibration algorithm for a pipelined ADC with an open-loop amplifier using a Split ADC structure. The open-loop amplifier is employed as a residue amplifier in the first stage of the pipelined ADC to realize low power and high speed. However the residue amplifier as well as the DAC suffer from gain error and non-linearity, and hence they need calibration; conventional background calibration methods take a long time to converge. We investigated the split ADC structure for its background calibration with fast convergence, and validated its effectiveness by MATLAB simulation.

In this letter we present efficient iterative frequency domain equalization for single-carrier (SC) transmission systems with insufficient cyclic prefix (CP). Based on minimum mean square error (MMSE) criteria, iterative decision feedback frequency domain equalization (IDF-FDE) combined with cyclic prefix reconstruction (CPR) is derived to mitigate inter-symbol interference (ISI) and inter-carrier interference (ICI). Computer simulation results reveal that the proposed scheme significantly improves the performance of SC systems with insufficient CP compared with previous schemes.

This paper proposes a new method that reduces the computational cost of the phase-only correlation (POC)-based methods for displacement estimation in old film sequences. Conventional POC-based methods calculate all the points of the POC and only use the highest peak of the POC and its neighboring points to estimate the displacement with subpixel accuracy. Our proposed method reduces the computational cost by calculating the POC in a small region, instead of all the points of the POC. The proposed method combines a displacement pre-estimation with a modified inverse discrete Fourier transform (IDFT). The displacement pre-estimation uses the 1-D POCs of frame projections to pre-estimate the displacement with pixel accuracy and chooses a small region in the POC including the desired points for displacement estimation. The modified IDFT is then used to calculate the points in this small region for displacement estimation. Experimental results show that use of the proposed method can effectively reduce the computational cost of the POC-based methods without compromising the accuracy.

This paper studies a switched dynamical system based on the boost converter with a solar cell input. The solar cell is modeled by a piecewise linear current-controlled voltage source. A variant of peak-current-controlled switching is used in the boost converter. Applying the mapping procedure, the system dynamics can be analyzed precisely. As a main result, we have found an important example of trade-off between the maximum power point and stability: as a parameter (relates to the clock period) varies, the average power of a periodic orbit can have a peak near a period-doubling bifurcation set and an unstable periodic orbit can have the maximum power point.

The present paper introduces a new approach to the construction of a sequence set with a zero-correlation zone for both periodic and aperiodic correlation functions. The proposed sequences can be constructed from a pair of Hadamard matrices of orders n0 and n1. The constructed sequence set consists of n0 n1 ternary sequences, each of length n0(m+2)(n1+Δ), for a non-negative integer m and Δ ≥ 2. The zero-correlation zone of the proposed sequences is |τ| ≤ n0m+1-1, where τ is the phase shift. The proposed sequence set consists of n0 subsets, each with a member size n1. The correlation function of the sequences of a pair of different subsets, referred to as the inter-subset correlation function, has a zero-correlation zone with a width that is approximately Δ times that of the correlation function of sequences of the same subset (intra-subset correlation function). The inter-subset zero-correlation zone of the proposed sequences is |τ| ≤ Δn0m+1, where τ is the phase shift. The wide inter-subset zero-correlation enables performance improvement during application of the proposed sequence set.

To reduce the huge search space when customizing accelerators for the application specific instruction-set processor (ASIP), this paper proposes an automated customization method based on the data flow graph exploration. This method integrates the instruction identification and selection using an iterative improvement strategy, which uses a seed-growth algorithm to select the valid patterns that can bring higher performance enhancement. The search space is reduced by considering the performance factors during the identification stage. The experimental results indicate that the proposed method is feasible enough compared to the previous exhaustive algorithms.

It is an important problem to determine major collection times to meet the pause time goal for a generational garbage collector. From such a viewpoint, this paper proposes two stochastic models based on working schemes of a generational garbage collector: Garbage collections occur in a nonhomogeneous Poisson process, tenuring collection is made at a threshold level K, and major collection is made at time T or at Nth collection including minor and tenuring collections for the first model and at time T or at Nth collection including tenuring collections for the second model. Using the techniques of cumulative processes and reliability theory, expected cost rates are obtained, and optimal policies of major collection times which minimize them are discussed analytically and computed numerically.

We analyze the security notion of information-theoretic secrecy against an adversary who can make adaptive queries to the decryption oracle, and show that it is equivalent to requiring that the encryption scheme can perfectly encrypt +1 different messages. This immediately yields a lower bound on the key length and an optimal construction, namely (+1)-wise independent permutations. This also gives an operational interpretation to the notion of decryption oracles in information-theoretic security.

In this paper, a novel receiver architecture is proposed for multiple-input-multiple-output (MIMO) systems; the proposed architecture helps achieve superior performance in multipath fading channels when the number of layered streams exceeds the number of receiving antennas. In this architecture, the concept of “virtual channel” is adopted to attain diversity gain even when successive detection is applied for reducing computational complexity, while orthogonal frequency division multiplexing (OFDM) is employed to combat multipath fading. Actually, successive detection is carried out in all possible virtual channels, and the virtual channel with the minimum error probability is detected with the assistance of the maximum a-posteriori (MAP) decoder in the architecture. In addition, soft input and soft output (SISO) iterative detection is introduced in the virtual channel estimation scheme. The performance of the proposed architecture is verified by computer simulations. This architecture can be implemented with lesser complexity than that in maximum likelihood detection (MLD), but the gain in the former case exceeds that in the latter by 4.5 dB at the BER of 10-3 for 42 MIMO-OFDM.

We describe a concept and realization of distance-adaptive (DA) resource allocation in spectrum-sliced elastic optical path network (SLICE). We modify the modulation format and cross-connection bandwidth of individual fixed-bit rate optical paths to optimize performance with respect to transmission distance. The shorter paths are allocated a smaller amount of resources which allows reducing the spectrum occupied by the channel. We show in calculation a reduction in required spectral resources of more than 60% when compared to the traditional traffic allocation schemes based on ITU-T grid. The concept is verified experimentally.

This paper proposes a new sine wave approximation method for the PAC of DDFS. Sine wave is approximated by removing the harmonic components from trapezoid waveform. Experimental results show that the proposed PAC is advantageous in the SFDR range less than 60 dBc due to its small hardware cost.

This paper proposes a windowed-sinc function based peak-to-average power ratio (PAPR) reduction scheme for applying the polar transmitter techniques to orthogonal frequency division multiplexing (OFDM), where the high PAPR problem occurs. The proposed algorithm mitigates the effect of excessive suppression due to successive peaks or relatively high peaks of a signal, which is often observed when applying the conventional peak windowing scheme. The bit error rate (BER) and error vector magnitude (EVM) performances are measured for various window types and lengths. The simulation results demonstrate that the proposed algorithm achieves significant improvement in terms of BER and PAPR reduction performance while maintaining similar spectrum performance compared to the conventional peak windowing scheme.

An algorithm for estimating sinusoidal parameters is presented. In this paper, it is assumed that an observed signal is a single sinusoidal signal contaminated by white Gaussian noise. Based on this assumption, the sinusoidal parameters can be found by minimizing a cost function using the mean squared error (MSE) between the observed signal and a sinusoidal signal with arbitrary sinusoidal parameters. Because the cost function is nonlinear and not convex, it has undesirable local minima. To solve the minimization problem, we propose to use the roots of an algebraic equation. The algebraic equation is derived straightforwardly from the cost function. We show that the global solution is formulated by using the roots of the algebraic equation.

We present a new structure for parallel affine projection (AP) filters with different step-sizes. By observing their error signals, the proposed alternating AP (A-AP) filter selects one of the two AP filters and updates the weights of the selected filter for each iteration. As a result, the total computations required for the proposed structure is almost the same as that for a single AP filter. Experimental results show that the proposed alternating selection scheme extracts the best properties of each component filter, namely fast convergence and small steady-state error.

We propose an improved TDoA (Time Difference of Arrival) localization scheme based on PSO (Particle Swarm Optimization) in UWB (Ultra Wide Band) systems. The proposed scheme is composed of two steps: the re-estimation of TDoA parameters and the re-localization of tag position. In both steps, the PSO algorithm is employed to improve the performance. In the first step, the proposed scheme re-estimates the TDoA parameters obtained by traditional TDoA localization to reduce the TDoA estimation error. In the second step, the proposed scheme with the TDoA parameters estimated in the first step, re-localizes the tag to minimize the location error. Simulation results show that the proposed scheme achieves better location performance than the traditional TDoA localization in various channel environments.

We present the recovery of 2.5 Gb/s synchronous 16-point quadrature amplitude modulation data in real-time for an linewidth-times-symbol-duration ratio of 0.00048 after transmission over 1.6 km standard single mode fiber.

A key traffic engineering problem in the Open Shortest Path First (OSPF)-based network is the determination of optimal link weights. From the network operators' point of view, there are two approaches to determining a set of link weights: Start-time Optimization (SO) and Run-time Optimization (RO). We previously presented a Preventive Start-time Optimization (PSO) scheme that determines an appropriate set of link weights at start time. It can counter both unexpected network congestion and network instability and thus overcomes the drawbacks of SO and RO, respectively. The previous work adopts a preventive start-time optimization algorithm with limited candidates, named PSO-L (PSO for Limited candidates). Although PSO-L relaxes the worst-case congestion, it does not confirm the optimal worst-case performance. To pursue this optimality, this paper proposes a preventive start-time optimization algorithm with a wide range of candidates, named PSO-W (PSO for Wide-range candidates). PSO-W upgrades the objective function of SO that determines the set of link weights at start time by considering all possible single link failures; its goal is to minimize the worst-case congestion. Numerical results via simulations show that PSO-W effectively relaxes the worst-case network congestion compared to SO, while it avoids the network instability caused by the run-time changes of link weights caused by RO. At the same time, PSO-W yields performance superior to that of PSO-L.

Advanced nanometer circuits are susceptible to errors caused by process, voltage, and temperature (PVT) variations or due to a single event upset (SEU). State-of-the-art design-for-variability (DFV)-aware flip-flops (FFs) suffer from their area and timing overheads. By utilizing C-element modules, two types of FFs are proposed for error detection and error correction.

An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.

The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.