High performance, low area multipliers are highly desired for modern and future DSP systems due to the increasing demand of high speed DSP applications. In this paper, we present an efficient architecture for an LUT-based truncated multiplier and its application in RGB to YCbCr color space conversion which can be used for digital TV, image and video processing systems. By employing an improved split LUT-based architecture and LUT optimization method, the proposed multiplier can reduce the value of area-delay product by up to 52% compared with other constant multiplier methods. The FPGA implementation of a color space conversion application employing the proposed multiplier also results in significant reduction of area-delay product of up to 48%.

This paper is about a new efficient method for the implementation of a Block Proportionate Normalized Least Mean Square (BPNLMS++) adaptive filter using the Fermat Number Transform (FNT) and its inverse (IFNT). These transforms present advantages compared to Fast Fourier Transform (FFT) and the inverse (IFFT). An efficient state space method for implementing the FNT over rectangular windows is used in the cases where there is a large overlap between the consecutive input signals. This is called Generalized Sliding Fermat Number Transform (GSFNT) and is useful for reducing the computational complexity of finite ring convolvers and correlators. In this contribution, we propose, as a first objective, an efficient state algorithm with the purpose of reducing the complexity of IFNT. This algorithm, called Inverse Generalized Sliding Fermat Number Transform (IGSFNT), uses the technique of Generalized Sliding associated to matricial calculation in the Galois Field. The second objective is to realize an implementation of the BPNLMS++ adaptive filter using GSFNT and IGSFNT, which can significantly reduce the computation complexity of the filter implantation on digital signal processors.

We have developed a long-range asynchronous on-chip data-transmission link based on multiple-valued single-track signaling for a highly reliable asynchronous Network-on-Chip. In the proposed signaling, 1-bit data with control information is represented by using a one-digit multi-level signal, so serial data can be transmitted asynchronously using only a single wire. The small number of wires alleviates the routing complexity of wiring long-range interconnects. The use of current-mode signaling makes it possible to transmit data at high speed without buffers or repeaters over a long interconnect wire because of the low-voltage swing of signaling, and it leads to low-latency data transmission. We achieve a latency of 0.45 ns, a throughput of 1.25 Gbps, and energy dissipation of 0.58 pJ/bit with a 10-mm interconnect wire under a 0.13 µm CMOS technology. This represents an 85% decrease in latency, a 150% increase in throughput, and a 90% decrease in energy dissipation compared to a conventional serial asynchronous data-transmission link.

To improve the observability during the post-silicon validation, it is the key to select the limited trace signals effectively for the data acquisition. This paper proposes an automated trace signal selection algorithm, which uses the pruning-based strategy to reduce the exploration space. First, the restoration range is covered for each candidate signals. Second, the constraints are generated based on the conjunctive normal form (CNF) to avoid the conflict. Finally the candidates are selected through pruning-based enumeration. The experimental results indicate that the proposed algorithm can bring higher restoration ratios and is more effective compared to existing methods.

This paper presents an inertial estimator learning automata scheme by which both the short-term and long-term perspectives of the environment can be incorporated in the stochastic estimator – the long term information crystallized in terms of the running reward-probability estimates, and the short term information used by considering whether the most recent response was a reward or a penalty. Thus, when the short-term perspective is considered, the stochastic estimator becomes pertinent in the context of the estimator algorithms. The proposed automata employ an inertial weight estimator as the short-term perspective to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed inertial estimator scheme, the estimates of the reward probabilities of actions are affected by the last response from environment. In this way, actions that have gotten the positive response from environment in the short time, have the opportunity to be estimated as “optimal”, to increase their choice probability and consequently, to be selected. The estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be ε-optimal in every stationary random environment. Extensive simulation results indicate that the proposed algorithm converges faster than the traditional stochastic-estimator-based S E_RI scheme, and the deterministic-estimator-based DGPA and DP_RI schemes when operating in stationary random environments.

MISTY1 is a 64-bit block cipher that has provable security against differential and linear cryptanalysis. MISTY1 is one of the algorithms selected in the European NESSIE project, and it is recommended for Japanese e-Government ciphers by the CRYPTREC project. In this paper, we report on 12th order differentials in 3-round MISTY1 with FL functions and 44th order differentials in 4-round MISTY1 with FL functions both previously unknown. We also report that both data complexity and computational complexity of higher order differential attacks on 6-round MISTY1 with FL functions and 7-round MISTY1 with FL functions using the 46th order differential can be reduced to as much as 1/22 of the previous values by using multiple 44th order differentials simultaneously.

It is well known that Boolean functions used in stream and block ciphers should have high algebraic immunity to resist algebraic attacks. Up to now, there have been many constructions of Boolean functions achieving the maximum algebraic immunity. In this paper, we present several constructions of rotation symmetric Boolean functions with maximum algebraic immunity on an odd number of variables which are not symmetric, via a study of invertible cyclic matrices over the binary field. In particular, we generalize the existing results and introduce a new method to construct all the rotation symmetric Boolean functions that differ from the majority function on two orbits. Moreover, we prove that their nonlinearities are upper bounded by .

To improve the robustness and transparency of spread spectrum (SS) based watermarking, this paper presents a new informed embedding strategy, which we call selective host-interference cancellation. We show that part of the host-interference in SS-based watermarking is beneficial to blind watermark extraction or detection, and can be utilized rather than removed. Utilizing this positive effect of the host itself can improve the watermark robustness without significantly sacrificing the media fidelity. The proposed strategy is realized by selectively applying improved SS (ISS) modulation to traditional SS watermarking. Theoretically, the error probability of the new method under additive white Gaussian noise attacks is several orders of magnitude lower than that of ISS for high signal-to-watermark ratios, and the required minimum watermark power is reduced by 3dB. Experiments were conducted on real audio signals, and the results show that our scheme is robust against most of common attacks even in high-transparency or high-payload applications.

The RFID tag system has received a lot of attention for ubiquitous computing. An RFID tag is attached to an object. With the unique ID of the RFID tag, a user identifies the object provided with the RFID tag and derives appropriate information about the object. One important application in the RFID technology is localizing RFID tags, which can be very useful in acquiring the position information concerning the RFID tags. It can be applied to navigation systems and positional detection systems for mobile robots. This paper proposes a new adaptive multi-range-sensing method for 3D localization of passive RFID tags by using a probabilistic approach. In this method, a mobile object (human, robot, etc.) with an RFID reader estimates the positions of RFID tags with multiple communication ranges dynamically. The effectiveness of the proposed method was demonstrated in experiments.

We present a new framework of the data-reusing (DR) adaptive algorithms by incorporating a constraint on noise, referred to as a noise constraint. The motivation behind this work is that the use of the statistical knowledge of the channel noise can contribute toward improving the convergence performance of an adaptive filter in identifying a noisy linear finite impulse response (FIR) channel. By incorporating the noise constraint into the cost function of the DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive algorithms are derived. Experimental results clearly indicate their superior performance over the conventional DR ones.

In this letter, we describe a low power current to time converter for wireless sensor networks. The proposed circuit has some advantages of high linearity and wide measurement range. From the evaluation using HSPICE with 0.18 µm CMOS device parameters, the output differential error for the input current variation is approximately 0.1 µs/nA under the condition that the current is varied from 100 nA to 500 nA. The idle power consumption is approximately zero.

A power analysis attack is a well-known side-channel attack but the efficiency of the attack is frequently degraded by the existence of power components, irrelative to the encryption included in signals used for the attack. To enhance the performance of the power analysis attack, we propose a preprocessing method based on extracting encryption-related parts from the measured power signals. Experimental results show that the attacks with the preprocessed signals detect correct keys with much fewer signals, compared to the conventional power analysis attacks.

In this letter, we first present a new construction method for uncorrelated binary periodic Complementary sequence sets (CSS). Next, the uncorrelated periodic CSSs are used as pilot sequences for multiple-input multiple-output (MIMO) channel estimation. Later on, we propose a low-complexity periodic correlator. Finally, simulation results verify the optimality of pilot sequences for MIMO channel estimation.

A partial transmit sequence with a clipping (PTS-Clipping) method can reduce considerably high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal. However, exhaustive searching operations are needed in order to find optimal phase factors. Signal distortions also occur because the clipping is a nonlinear operation. In this letter, we propose a new partial transmit sequence (PTS) scheme using a phase factor selection algorithm with preset thresholds. The proposed scheme achieves considerable savings for determining the optimum phase factors without the signal distortions.

We propose a method for halftoning grayscale images by drawing weighted centroidal Voronoi tessellations (WCVTs) with black lines on white image planes. Based on the fact that CVT approaches a uniform hexagonal lattice asymptotically, we derive a relationship of darkness between input grayscale images and the corresponding halftone images. Then the derived relationship is used for adjusting the contrast of the halftone images. Experimental results show that the generated halftone images can reproduce the original tone in the input images faithfully.