This paper concentrates on a class of pseudorandom sequences generated by combining q-ary m-sequences and quadratic characters over a finite field of odd order, called binary generalized NTU sequences. It is shown that the relationship among the sub-sequences of binary generalized NTU sequences can be formulated as combinatorial structures called Hadamard designs. As a consequence, the combinatorial structures generalize the group structure discovered by Kodera et al. (IEICE Trans. Fundamentals, vol.E102-A, no.12, pp.1659-1667, 2019) and lead to a finite-geometric explanation for the investigated group structure.

Type-II Z-complementary pairs (ZCPs) play an important role in suppressing asynchronous interference in a wideband wireless communication system where the minimum interfering-signal delay is large. Based on binary Golay complementary pairs (BGCPs) and interleaving technique, new construction for producing Z-optimal Type-II even-length quadriphase ZCPs (EL-QZCPs) is presented, and the resultant pairs have new lengths in the form of 2 × 2α10β26γ (α, β, γ non-negative integers), which are not included in existing known Type-II EL-QZCPs.

This paper refers to the optimal voltage pair, which minimizes the energy consumption of LSI circuits under a target delay constraint, as a Minimum Energy Point (MEP). This paper proposes an approximation-based implementation method for an MEP tracking system over a wide voltage region. This paper focuses on the MEP characteristics that the energy loss is sufficiently small even though the voltage point changes near the MEP. For example, the energy loss is less than 5% even though the estimated MEP differs by a few tens of millivolts in comparison with the actual MEP. Therefore, the complexity for determining the MEP is relaxed by approximating complex operations such as the logarithmic or the exponential functions in the MEP tracking algorithm, which leads to hardware-/software-efficient implementation. When the MEP tracking algorithm is implemented in software, the MEP estimation time is reduced from 1ms to 13µs by the proposed approximation. When implemented in hardware, the proposed method can reduce the area of an MEP estimation circuit to a quarter. Measurement results of a 32-bit RISC-V processor fabricated in a 65-nm SOTB process technology show that the energy loss introduced by the proposed approximation is less than 2% in comparison with the MEP operation. Furthermore, we show that the MEP can be tracked within about 45 microseconds by the proposed MEP tracking system.

Histogram equalization (HE) is the one of the simplest and most effective methods for contrast enhancement. It can automatically define the gray-level mapping function based on the distribution of gray-level included in the image. However, since HE does not use a spatial feature included in the input image, HE fails to produce satisfactory results for broad range of low-contrast images. The differential gray-level histogram (DH), which is contained edge information of the input image, was defined and the differential gray-level histogram equalization (DHE) has been proposed. The DHE shows better enhancement results compared to HE for many kinds of images. In this paper, we propose a generalized histogram equalization (GHE) including HE and DHE. In GHE, the histogram is created using the power of the differential gray-level, which includes the spatial features of the image. In HE, the mean brightness of the enhancement image cannot be controlled. On the other hand, GHE can control the mean brightness of the enhancement image by changing the power, thus, the mean brightness of the input image can be perfectly preserved while maintaining good contrast enhancement.

Approximate computing (AC) has recently emerged as a promising approach to the energy-efficient design of digital systems. For realizing the practical AC design, we need to verify whether the designed circuit can operate correctly under various operating conditions. Namely, the verification needs to efficiently find fatal logic errors or timing errors that violate the constraint of computational quality. This work focuses on the verification where the computational results can be observed, the computational quality can be calculated from computational results, and the constraint of computational quality is given and defined as the constraint which is set to the computational quality of designed AC circuit with given workloads. Then, this paper proposes a novel dynamic verification framework of the AC circuit. The key idea of the proposed framework is to incorporate a quality assessment capability into the Coverage-based Grey-box Fuzzing (CGF). CGF is one of the most promising techniques in the research field of software security testing. By repeating (1) mutation of test patterns, (2) execution of the program under test (PUT), and (3) aggregation of coverage information and feedback to the next test pattern generation, CGF can explore the verification space quickly and automatically. On the other hand, CGF originally cannot consider the computational quality by itself. For overcoming this quality unawareness in CGF, the proposed framework additionally embeds the Design Under Verification (DUV) component into the calculation part of computational quality. Thanks to the DUV integration, the proposed framework realizes the quality-aware feedback loop in CGF and thus quickly enhances the verification coverage for test patterns that violate the quality constraint. In this work, we quantitatively compared the verification coverage of the approximate arithmetic circuits between the proposed framework and the random test. In a case study of an approximate multiply-accumulate (MAC) unit, we experimentally confirmed that the proposed framework achieved 3.85 to 10.36 times higher coverage than the random test.

Vectorial Boolean functions play an important role in cryptography, sequences and coding theory. Both affine equivalence and EA-equivalence are well known equivalence relations between vectorial Boolean functions. In this paper, we give an exact formula for the number of affine equivalence classes, and an asymptotic formula for the number of EA-equivalence classes of vectorial Boolean functions.

In order to improve the frequency band utilization and avoid mutual interference between signals, the BD3 satellite signals adopt Binary Offset Carrier (BOC) modulation. On one hand, BOC modulation has a narrow main peak width and strong anti-interference ability; on the other hand, the phenomenon of false acquisition locking caused by the multi-peak characteristic of BOC modulation itself needs to be resolved. In this context, this paper proposes a new BOC(n,n) unambiguous acquisition algorithm based on segmentation reconstruction. The algorithm is based on splitting the local BOC signal into four parts in each subcarrier period. The branch signal and the received signal are correlated with the received signal to generate four branch correlation signals. After a series of combined reconstructions, the final signal detection function completely eliminates secondary peaks. A simulation shows that the algorithm can completely eliminate the sub-peak interference for the BOC signals modulated by subcarriers with different phase. The characteristics of narrow correlation peak are retained. Experiments show that the proposed algorithm has superior performance in detection probability and peak-to-average ratio.

In this paper, the spurious modes and quality-factor (Q) values of the one-port dual-mode AlN lamb-wave resonators at 500-1000 MHz were studied by theoretical analysis and experimental verification. Through finite element analysis, we found that optimizing the width of the lateral reflection boundary at both ends of the resonator to reach the quarter wavelength (λ/4), which can improve its spectral purity and shift its resonant frequency. The designed resonators were micro-fabricated by using lithography processes on a 6-inch wafer. The measured results show that the spurious mode can be converted and dissipated, splitting into several longitudinal modes by optimizing the width of the lateral reflection boundary, which are consistent well with the theoretical analysis. Similarly, optimizing the interdigital transducer (IDT) width and number of IDT fingers can also suppress the resonator's spurious modes. In addition, it is found that there is no significant difference in the Qs value for the two modes of the dual-mode resonator with the narrow anchor and full anchor. The acoustic wave leaked from the anchor into the substrate produces a small displacement, and the energy is limited in the resonator. Compared to the resonator with Au IDTs, the resonator with Al IDTs can achieve a higher Q value due to its lower thermo-elastic damping loss. The measured results show the optimized dual-mode lamb-wave resonator can obtain Qs value of 2946.3 and 2881.4 at 730.6 MHz and 859.5 MHz, Qp values of 632.5 and 1407.6, effective electromechanical coupling coefficient (k2eff) of 0.73% and 0.11% respectively, and has excellent spectral purity simultaneously.

Register automaton (RA) is an extension of finite automaton by adding registers storing data values. RA has good properties such as the decidability of the membership and emptiness problems. Linear temporal logic with the freeze quantifier (LTL↓) proposed by Demri and Lazić is a counterpart of RA. However, the expressive power of LTL↓ is too high to be applied to automatic verification. In this paper, we propose a subclass of modal µ-calculus with the freeze quantifier, which has the same expressive power as RA. Since a conjunction ψ1 ∧ ψ2 in a general LTL↓ formula cannot be simulated by RA, the proposed subclass prohibits at least one of ψ1 and ψ2 from containing the freeze quantifier or a temporal operator other than X (next). Since the obtained subclass of LTL↓ does not have the ability to represent a cycle in RA, we adopt µ-calculus over the subclass of LTL↓, which allows recursive definition of temporal formulas. We provide equivalent translations from the proposed subclass of µ-calculus to RA and vice versa and prove their correctness.

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

This paper first introduces the structure of a shipboard equipment control cabinet and the preliminary design of electromagnetic shielding, then introduces the principle of low-frequency magnetic field shielding, and uses silicon steel sheet to shield the low-frequency magnetic field of shipboard equipment control equipment. Based on ANSYS Maxwell simulation software, the low-frequency magnetic field radiation emission of the equipment's conducted harmonic peak frequency point is simulated. Finally, according to MIL-STD-461G test standard, the low-frequency magnetic field radiation emission test is carried out, which meets the limit requirements of the standard. The low-frequency magnetic field shielding technology has practical value. The low-frequency magnetic field radiation emission simulation based on ANSYS Maxwell proposed in this paper is a useful attempt for the quantitative simulation of radiation emission.

Quadrature voltage-controlled oscillators (VCOs) with current-weight-average and voltage-weight-average phase-adjusting architectures are studied. The phase adjusting equalizes the oscillation frequency to the LC-resonant frequency. The merits of the equalization are explained by using Leeson's phase noise equation and the impulse sensitivity function (ISF). Quadrature VCOs with the phase-adjusting architectures are fabricated using 180-nm TSMC CMOS and show low-phase-noise performances compared to a conventional differential VCO. The ISF analysis and small-signal analysis also show that the drawbacks of the current-weight-average phase-adjusting and voltage-weight-average phase-adjusting architectures are current-source noise effect and large additional capacitance, respectively. A voltage-average-adjusting circuit with a source follower at its input alleviates the capacitance increase.

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

In this study, we have proposed a millimeter-wave (MMW) single-pixel imaging (SPI) system with a liquid-crystal (LC) mask cell. The LC cell functions as an electrically switchable mask based on the change in absorption properties, which depend on the orientation of the LC. We investigated the influence of noise on the measured and estimated data (reconstructed image). The proposed system exhibited moderate robustness against random noise (that were added) compared to raster scan-based and Hadamard matrix-based SPI systems. Finally, the results of some demonstrative experiments were introduced to ensure the applicability of the constructed MMW-SPI system, and steps for improving the reconstructed image quality were discussed.

The mean, median, and mode are usually calculated from univariate observations as the most basic representative values of a random variable. To measure the spread of the distribution, the standard deviation, interquartile range, and modal interval are also calculated. When we analyze continuous relations between a pair of random variables from bivariate observations, regression analysis is often used. By minimizing appropriate costs evaluating regression errors, we estimate the conditional mean, median, and mode. The conditional standard deviation can be estimated if the bivariate observations are obtained from a Gaussian process. Moreover, the conditional interquartile range can be calculated for various distributions by the quantile regression that estimates any conditional quantile (percentile). Meanwhile, the study of the modal interval regression is relatively new, and spline regression models, known as flexible models having the optimality on the smoothness for bivariate data, are not yet used. In this paper, we propose a modal interval regression method based on spline quantile regression. The proposed method consists of two steps. In the first step, we divide the bivariate observations into bins for one random variable, then detect the modal interval for the other random variable as the lower and upper quantiles in each bin. In the second step, we estimate the conditional modal interval by constructing both lower and upper quantile curves as spline functions. By using the spline quantile regression, the proposed method is widely applicable to various distributions and formulated as a convex optimization problem on the coefficient vectors of the lower and upper spline functions. Extensive experiments, including settings of the bin width, the smoothing parameter and weights in the cost function, show the effectiveness of the proposed modal interval regression in terms of accuracy and visual shape for synthetic data generated from various distributions. Experiments for real-world meteorological data also demonstrate a good performance of the proposed method.

Mixed-Polarity Multiple-Control Toffoli (MPMCT) gates are generally used to implement large control logic functions for quantum computation. A logic circuit consisting of MPMCT gates needs to be mapped to a quantum computing device that invariably has a physical limitation, which means we need to (1) decompose the MPMCT gates into one- or two-qubit gates, and then (2) insert SWAP gates so that all the gates can be performed on Nearest Neighbor Architectures (NNAs). Up to date, the above two processes have only been studied independently. In this work, we investigate that the total number of gates in a circuit can be decreased if the above two processes are considered simultaneously as a single step. We developed a method that inserts SWAP gates while decomposing MPMCT gates unlike most of the existing methods. Also, we consider the effect on the latter part of a circuit carefully by considering the qubit placement when decomposing an MPMCT gate. Experimental results demonstrate the effectiveness of our method.

For a long time, the compensation effect of hearing aid is mainly evaluated subjectively, and there are fewer studies of objective evaluation. Furthermore, a pure speech signal is generally required as a reference in the existing objective evaluation methods, which restricts the practicality in a real-world environment. Therefore, this paper presents a non-intrusive speech quality evaluation method for hearing aid, which combines the audiogram and weighted frequency information. The proposed model mainly includes an audiogram information extraction network, a frequency information extraction network, and a quality score mapping network. The audiogram is the input of the audiogram information extraction network, which helps the system capture the information related to hearing loss. In addition, the low-frequency bands of speech contain loudness information and the medium and high-frequency components contribute to semantic comprehension. The information of two frequency bands is input to the frequency information extraction network to obtain time-frequency information. When obtaining the high-level features of different frequency bands and audiograms, they are fused into two groups of tensors that distinguish the information of different frequency bands and used as the input of the attention layer to calculate the corresponding weight distribution. Finally, a dense layer is employed to predict the score of speech quality. The experimental results show that it is reasonable to combine the audiogram and the weight of the information from two frequency bands, which can effectively realize the evaluation of the speech quality of the hearing aid.

To improve the performance of underwater acoustic communications, this letter proposes a polar coding scheme with adaptive channel equalization, which can reduce the amount of feedback information. Furthermore, a hybrid automatic repeat request (HARQ) mechanism is provided to mitigate the impact of estimation errors. Simulation results show that the proposed scheme outperforms the turbo equalization in bit error rate. Computational complexity analysis is also provided for comparison.

A method of radar-based contactless vital-sign sensing and electrocardiogram (ECG) signal reconstruction using deep learning is proposed. A radar system is an effective tool for contactless vital-sign sensing because it can measure a small displacement of the body surface without contact. However, most of the conventional methods have limited evaluation indices and measurement conditions. A method of measuring body-surface-displacement signals by using frequency-modulated continuous-wave (FMCW) radar and reconstructing ECG signals using a convolutional neural network (CNN) is proposed. This study conducted two experiments. First, we trained a model using the data obtained from six subjects breathing in a seated condition. Second, we added sine wave noise to the data and trained the model again. The proposed model is evaluated with a correlation coefficient between the reconstructed and actual ECG signal. The results of first experiment show that their ECG signals are successfully reconstructed by using the proposed method. That of second experiment show that the proposed method can reconstruct signal waveforms even in an environment with low signal-to-noise ratio (SNR).

It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.