Recently, stochastic computing based on stochastic numbers attracts attention as an effective computation method, which realizes arithmetic operations by simple logic circuits with a tolerance of bit errors. When we input two or more identical values to a stochastic circuit, we require to duplicate a stochastic number. However, if bit streams of duplicated stochastic numbers are dependent on each other, their arithmetic operation results can be inaccurate. In this paper, we propose two stochastic number duplicators, called FSR and RRR. The stochastic numbers duplicated by the FSR and RRR duplicators have the equivalent values but have independent bit streams, effectively utilizing bit re-arrangement using randomized bit streams. Experimental evaluation results demonstrate that the RRR duplicator, in particular, obtains more accurate results even if a circuit has re-convergence paths, reducing the mean square errors by 20%-89% compared to a conventional stochastic number duplicator.

Approximate computing is a promising solution for future energy-efficient designs because it can provide great improvements in performance, area and/or energy consumption over traditional exact-computing designs for non-critical error-tolerant applications. However, the most challenging issue in designing approximate circuits is how to guarantee the pre-specified computation accuracy while achieving energy reduction and performance improvement. To address this problem, this paper starts from the state-of-the-art general approximate adder model (GeAr) and extends it for more possible approximate design candidates by relaxing the design restrictions. And then a maximum-error-distance-based performance/accuracy formulation, which can be used to select the performance/energy-accuracy optimal design from the extended design space, is proposed. Our evaluation results show the effectiveness of the proposed method in terms of area overhead, performance, energy consumption, and computation accuracy.

To deal with the reliability issue caused by soft errors, this paper proposed a low power soft error hardened latch (SHC) design using a novel Schmitt-Trigger-based C-element for reliable low power applications. Unlike state-of-the-art soft error tolerant latches that are usually based on hardware redundancy with large area overhead and high power consumption, the proposed SHC latch is implemented through double-sampling and node-checking using a novel Schmitt-Trigger-based C-element, which can help to reduce the area overhead and the corresponding power consumption as well. The evaluation results show that the total number of transistors of the proposed SHC latch is only increased by 2 when compared to the conventional unhardened C²MOS latch, while up to 20.35% and 82.96% power reduction can be achieved when compared to the conventional unhardened C²MOS latch and the existing soft error tolerant HiPeR design, respectively.

This paper proposes MRO-PUF, a new architecture for ring-oscillator-based physically unclonable functions (PUFs) with enhanced resistance against machine learning attacks. In the proposed PUF, an instantaneous output value of a ring oscillator is used as a response, whereas the most existing PUFs directly use propagation delays to determine the response. Since the response of the MRO-PUF is non-linear and discontinuous as the delay of the ring oscillator increases, the prediction of the response by machine learning attacks is difficult. Through the performance evaluation of the MRO-PUF with simulations, it achieves 15 times stronger resistance against machine learning attacks using a support vector machine compared to the existing ones such as an arbiter PUF and a bistable ring PUF. The MRO-PUF also achieves a sufficient level of the basic performance of PUFs in terms of uniqueness and robustness.

Non-volatile memories are a promising alternative to memory design but data stored in them still may be destructed due to crosstalk and radiation. The data stored in them can be restored by using error-correcting codes but they require extra bits to correct bit errors. One of the largest problems in non-volatile memories is that they consume ten to hundred times more energy than normal memories in bit-writing. It is quite necessary to reduce writing bits. Recently, a REC code (bit-write-reducing and error-correcting code) is proposed for non-volatile memories which can reduce writing bits and has a capability of error correction. The REC code is generated from a linear systematic error-correcting code but it must include the codeword of all 1's, i.e., 11…1. The codeword bit length must be longer in order to satisfy this condition. In this letter, we propose a method to generate a relaxed REC code which is generated from a relaxed error-correcting code, which does not necessarily include the codeword of all 1's and thus its codeword bit length can be shorter. We prove that the maximum flipping bits of the relaxed REC code is still limited theoretically. Experimental results show that the relaxed REC code efficiently reduce the number of writing bits.

Simulation-based verification of hardware designs, in particular, register-transfer-level (RTL) designs, has been widely used, and has been one of the major bottlenecks in design processes. One of the approaches is coverage-driven verification, of its target is improvement of some metric called coverage. In a prior work of ours, we have proposed a coverage-driven verification using both randomly generated simulation patterns and patterns generated by a SAT (satisfiability) solver, and have shown its effectiveness. In this paper, we extend this approach with an SMT (satisfiability modulo theory) solver, which can handle arithmetic relations among integer, floating-point or bit-vector variables. Experimental results show that the more arithmetic modules are included, the more an SMT-based method gets superior to the method using only a SAT solver.

This paper proposes a semi-supervised source separation method for stereophonic music signals containing multiple recorded or processed signals, where synthesized music is focused on the stereophonic music. As the synthesized music signals are often generated as linear combinations of many individual source signals and their respective mixing gains, phase or phase difference information between inter-channel signals, which represent spatial characteristics of recording environments, cannot be utilized as acoustic clues for source separation. Non-negative Tensor Factorization (NTF) is an effective technique which can be used to resolve this problem by decomposing amplitude spectrograms of stereo channel music signals into basis vectors and activations of individual music source signals, along with their corresponding mixing gains. However, it is difficult to achieve sufficient separation performance using this method alone, as the acoustic clues available for separation are limited. To address this issue, this paper proposes a Cepstral Distance Regularization (CDR) method for NTF-based stereo channel separation, which involves making the cepstrum of the separated source signals follow Gaussian Mixture Models (GMMs) of the corresponding the music source signal. These GMMs are trained in advance using available samples. Experimental evaluations separating three and four sound sources are conducted to investigate the effectiveness of the proposed method in both supervised and semi-supervised separation frameworks, and performance is also compared with that of a conventional NTF method. Experimental results demonstrate that the proposed method yields significant improvements within both separation frameworks, and that cepstral distance regularization provides better separation parameters.

Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.

This paper discusses the collection of sensor data for power distribution systems. In current power distribution systems, this is usually performed solely by the Remote Terminal Unit (RTU) which is located at the root of a power distribution network. The recent rise of distributed power sources, such as photovoltaic generators, raises the demand to increase the frequency of data collection because the output of these distributed generators varies quickly depending on the weather. Increasing data collection frequency in turn requires shortening the time required for data collection. The paper proposes the use of aggregation points for this purpose. An aggregation point can collect sensor data concurrently with other aggregation points as well as with the RTU. The data collection time can be shortened by having the RTU receive data from aggregation points, instead of from all sensors. This approach then poses the problem of finding the optimal location of aggregation points. To solve this problem, the paper proposes a Mixed Integer Linear Problem (MILP) formulation of the problem. The MILP problem can then be solved with off-the-shelf mathematical optimization software. The results of experiments show that the proposed approach is applicable to rather large scale power distribution systems.

Location information is meaningful information for future ITS (Intelligent Transport Systems) world. Especially, the accuracy of the information is required because the accuracy decides the quality of ITS services. For realization of high precision positioning, Kinematic positioning technique has been attracting attention. The Kinematic positioning requires the configuration of many positioning parameters. However, the configuration is difficult because optimal parameter differs according to user's environment. In this paper, we will propose an estimation method of optimal parameter according to the environment. Further, we will propose an elimination method of unreliable positioning results. Hereby, we can acquire extensively only the reliable positioning results. By using the actual vehicle traveling data, the ability and the applicable range of the proposed method will be shown. The result will show that our proposed method improves the acquision rate of reliable positioning results and mitigates the acquision rate of the unreliable positioning results.

We propose an efficient training method for memristor neural networks. The proposed method is suitable for the mini-batch-based training, which is a common technique for various neural networks. By integrating the two processes of gradient calculation in the backpropagation algorithm and weight update in the write operation to the memristors, the proposed method accelerates the training process and also eliminates the external computing resources required in the existing method, such as multipliers and memories. Through numerical experiments, we demonstrated that the proposed method achieves twice faster convergence of the training process than the existing method, while retaining the same level of the accuracy for the classification results.

In X-ray computed tomography, scattered X-rays are generally removed by using a post-patient collimator located in front of the detector. In this paper, we show that the scattered X-rays have the potential to improve the estimation accuracy of the attenuation coefficient in computed tomography. In order to clarify the problem, we simplified the geometry of the computed tomography into a thin cylinder composed of a homogeneous material so that only one attenuation coefficient needs to be estimated. We then conducted a Monte Carlo numerical experiment on improving the estimation accuracy of attenuation coefficient by measuring the scattered X-rays with several dedicated toroidal detectors around the cylinder in addition to the primary X-rays. We further present a theoretical analysis to explain the experimental results. We employed a model that uses a T-junction (i.e., T-junction model) to divide the photon transport into primary and scattered components. This division is processed with respect to the attenuation coefficient. Using several T-junction models connected in series, we modeled the case of several scatter detectors. The estimation accuracy was evaluated according to the variance of the efficient estimator, i.e., the Cramer-Rao lower bound. We confirmed that the variance decreases as the number of scatter detectors increases, which implies that using scattered X-rays can reduce the irradiation dose for patients.

A low-complexity signal detection approach based on the Kaczmarz algorithm (KA) is proposed to iteratively realize minimum mean square error (MMSE) detection for uplink massive multiple-input multiple-output (MIMO) systems. While KA is used for straightforward matrix inversion, the MMSE detection requires the computation of the Gram matrix with high complexity. In order to avoid the Gram matrix computation, an equivalent augmented matrix is applied to KA-based MMSE detection. Moreover, promising initial estimation and an approximate method to compute soft-output information are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results demonstrate that the proposed approach outperforms the recently proposed Neumann series, conjugate gradient, and Gauss-Seidel methods in complexity and error-rate performance. Meanwhile, the FPGA implementation results confirm that our proposed method can efficiently compute the approximate inverse with low complexity.

8B/10B is an encoding technique largely used in different communication protocols, with several advantages such as zero DC bias. In the last years transmission rates have grown rapidly, thus the need of encoders with better performance in terms of throughput, area and power consumption raised rapidly. In this article we will present and discuss the architecture of two symbols parallel encoder, comparing it with a classical pipelined solution.

This letter proposes a novel modulation classification method for overlapped sources named LRGP involving multinomial logistic regression (MLR) and multi-gene genetic programming (MGGP). MGGP based feature engineering is conducted to transform the cumulants of the received signals into highly discriminative features and a MLR based classifier is trained to identify the combination of the modulation formats of the overlapped sources instead of signal separation. Extensive simulations demonstrate that LRGP yields superior performance compared with existing methods.

This letter proposes a numerical method for approximating the location of and dynamics on a class of chaotic saddles. In contrast to the conventional strategy of maximizing the escape time, our proposal is to impose a zero-expansion condition along transversely repelling directions of chaotic saddles. This strategy exploits the existence of skeleton-forming unstable periodic orbits embedded in chaotic saddles, and thus can be conveniently implemented as a variant of subspace Newton-type methods. The algorithm is examined through an illustrative and another standard example.

In this paper, the problem on secure communication based on chaos synchronization is investigated. The dual channel information transmitting technology is proposed to increase the security of secure communication system. Based on chaos synchronization, a new digital secure communication scheme is presented for a class of master-slave systems. Finally some numerical simulation examples are given to demonstrate the effectiveness of the given results.

This paper studies the secrecy throughput performance of the three-node wireless-powered networks and proposes two secure transmission schemes, namely the half-duplex maximal ratio combining (HD&MRC) scheme and the full-duplex jamming scheme based on time switching simultaneous wireless information and power transfer (FDJ&TS-SWIPT). The closed-form expressions of the secrecy throughput are derived, and intuitive comparison of the two schemes is provided. It is illustrated that the HD&MRC scheme only applies to the low and medium signal-to-noise ratio (SNR) regime. On the contrary, the suitable SNR regime of the FDJ&TS-SWIPT is much wider. It is depicted that FDJ&TS-SWIPT combing with current passive self-interference cancellation (SIC) algorithm outperforms HD&MRC significantly, especially when a medium or high transmit SNR is provided. Numerical simulations are conducted for verifying the validity of the analysis.

In this letter, we consider a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system, in which the confidential message intended for the information receiver (IR) should be kept secret from the energy receiver (ER). Our goal is to design the optimal transmit covariance matrix so as to maximize the secrecy energy efficiency (SEE) of the system while guaranteeing the secrecy rate, energy harvesting and transmit power constraints. To deal with the original non-convex optimization problem, we propose an alternating optimization (AO)- based algorithm and also prove its convergence. Simulation results show that the proposed algorithm outperforms conventional design methods in terms of SEE.

In this letter, we study a wireless powered communication network (WPCN) with non-orthogonal multiple access (NOMA), where the user clustering scheme that groups each two users in a cluster is adopted to guarantee the system performance. The two users in a cluster transmit data simultaneously via NOMA, while time division multiple access (TDMA) is used among clusters. We aim to maximize the system throughput by finding the optimal cluster permutation and the optimal time allocation, which can be obtained by solving the optimization problems corresponding to all cluster permutations. The closed-form solution of each optimization problem is obtained by exploiting its constraint structures. However, the complexity of this exhaustive method is quite high, we further propose a sub-optimal clustering scheme with low complexity. The simulation results demonstrate the superiority of the proposed scheme.

Individuals can be identified by features extracted from an electrocardiogram (ECG). However, irregular palpitations due to stress or exercise decrease the identification accuracy due to distortion of the ECG waveforms. In this letter, we propose a human identification scheme based on the frequency spectrums of an ECG, which can successfully extract features and thus identify individuals even while exercising. For the proposed scheme, we demonstrate an accuracy rate of 99.8% in a controlled experiment with exercising subjects. This level of accuracy is achieved by determining the significant features of individuals with a random forest classifier. In addition, the effectiveness of the proposed scheme is verified using a publicly available ECG database. We show that the proposed scheme also achieves a high accuracy with this public database.