RGB-D semantic segmentation has attracted increasing attention over the past few years. The depth feature encodes both the shape of a local geometry as well as the base (whereabout) of it in a larger context. RGB and depth images can be concatenated into one and inputted into a network model, reducing additional computation but resulting in some distractive information as they are multimodal. For the problem, we propose a Shape-aware Convolutional layer with Convolutional Kernel Attention (CKA-ShapeConv) for reducing the distractive information by leveraging each unique input feature to rectify the kernels. Instead of using a single convolution kernel, we aggregate N parallel convolution kernels based on input-dependent attention. Specifically, four sets of attention weights are firstly calculated from each input feature map, next N parallel convolution kernels are weighted and aggregated along different dimensions, which ensure that the generated convolution kernel is more capable of catching semantic information from the input feature map, reducing interference between RGB and depth features. Then the aggregated convolution kernel is decomposed into two components: base and shape, two new learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components. These two components can capture semantic and shape information of regions effectively, respectively. Meanwhile, our CKA-ShapeConv layer can be easily integrated into most existing backbone models with only a small amount of additional computation. Our experiments on NYUDv2 and SUN RGB-D datasets show that the proposed CKA-ShapeConv layer can improve the performance of backbone models effectively.

A dual-band decoupling strategy via artificial transmission line (TL) for closely spaced two-element multiple-input multiple-output (MIMO) antenna is proposed, which consists of two composite right-/left-handed TLs for dual-band phase shifting and a cross-shaped TL for susceptance elimination to counteract the real and imaginary part of the mutual coupling coefficient S21 at dual frequency bands, respectively. The decoupling principle and detailed design process of the dual-band decoupling scheme are presented. To validate the dual-band decoupling technique, a closely spaced dual-band MIMO antenna for 5G (sub-6G frequency band) utilization is designed, fabricated, and tested. The experimental results agree well with the simulation ones. A dual-band of 3.40 GHz-3.59 GHz and 4.79 GHz-4.99 GHz (S11&S22 < -10 dB, S12&S21 < -20 dB) has been achieved, and the mutual coupling coefficient S21 is significantly reduced 21 dB and 16.1 dB at 3.5 GHz and 4.9 GHz, respectively. In addition, the proposed dual-band decoupling scheme is antenna independent, and it is very suitable for other tightly coupled dual-band MIMO antennas.

The combination of peak-to-average power ratio (PAPR) reduction and predistortion (PD) techniques effectively reduces the nonlinear distortion of a transmission signal caused by power amplification and improves power efficiency. In this paper, assuming downlink amplify-and-forward (AF)-type relaying of multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) signals, we propose a joint method that combines a PD technique with our previously reported PAPR reduction method utilizing the null space of a MIMO channel. In the proposed method, the reported PAPR reduction method reduces the PAPR at a relay station (RS) as well as that at a base station (BS) by using only signal processing at the BS. The PD process at the BS and RS further reduces the nonlinear distortion caused by nonlinear power amplification. Computer simulation results show that the proposed method enhances the effectiveness of PD at the BS and RS and achieves further coverage enhancement compared to conventional methods.

The concept of dual function radar-communication (DFRC) provides solution to the problem of spectrum scarcity. This paper examines a multiple-input multiple-output (MIMO) DFRC system with the assistance of a reconfigurable intelligent surface (RIS). The system is capable of sensing multiple spatial directions while serving multiple users via orthogonal frequency division multiplexing (OFDM). The objective of this study is to design the radiated waveforms and receive filters utilized by both the radar and users. The mutual information (MI) is used as an objective function, on average transmit power, for multiple targets while adhering to constraints on power leakage in specific directions and maintaining each user’s error rate. To address this problem, we propose an optimal solution based on a computational genetic algorithm (GA) using bisection method. The performance of the solution is demonstrated by numerical examples and it is shown that, our proposed algorithm can achieve optimum MI and the use of RIS with the MIMO DFRC system improving the system performance.

This paper proposes a non-linear overloaded MIMO detector that outperforms the conventional soft-input maximum likelihood detector (MLD) with less computational complexity. We propose iterative log-likelihood ratio (LLR) estimation and multi stage LLR estimation for the proposed detector to achieve such superior performance. While the iterative LLR estimation achieves better BER performance, the multi stage LLR estimation makes the detector less complex than the conventional soft-input maximum likelihood detector (MLD). The computer simulation reveals that the proposed detector achieves about 0.6dB better BER performance than the soft-input MLD with about half of the soft-input MLD's complexity in a 6×3 overloaded MIMO OFDM system.

The complexity and scale of Networks-on-Chip (NoCs) are growing as more processing elements and memory devices are implemented on chips. However, under strict power budgets, it is also critical to lower the power consumption of NoCs for the sake of energy efficiency. In this paper, we therefore present three novel input unit designs for on-chip routers attempting to shrink their power consumption while still conserving the network performance. The key idea behind our designs is to organize buffers in the input units with characteristics of the network traffic in mind; as in our observations, only a small portion of the network traffic are long packets (composed of multiple flits), which means, it is fair to implement hybrid, asymmetric and reconfigurable buffers so that they are mainly targeting at short packets (only having a single flit), hence the smaller power consumption and area overhead. Evaluations show that our hybrid, asymmetric and reconfigurable input unit designs can achieve an average reduction of energy consumption per flit by 45%, 52.3% and 56.2% under 93.6% (for hybrid designs) and 66.3% (for asymmetric and reconfigurable designs) of the original router area, respectively. Meanwhile, we only observe minor degradation in network latency (ranging from 18.4% to 1.5%, on average) with our proposals.

Due to the global outbreak of coronaviruses, people are increasingly wearing masks even when photographed. As a result, photos uploaded to web pages and social networking services with the lower half of the face hidden are less likely to convey the attractiveness of the photographed persons. In this study, we propose a method to complete facial mask regions using StyleGAN2, a type of Generative Adversarial Networks (GAN). In the proposed method, a reference image of the same person without a mask is prepared separately from a target image of the person wearing a mask. After the mask region in the target image is temporarily inpainted, the face orientation and contour of the person in the reference image are changed to match those of the target image using StyleGAN2. The changed image is then composited into the mask region while correcting the color tone to produce a mask-free image while preserving the person's features.

In machine learning, data augmentation (DA) is a technique for improving the generalization performance of models. In this paper, we mainly consider gradient descent of linear regression under DA using noisy copies of datasets, in which noise is injected into inputs. We analyze the situation where noisy copies are newly generated and injected into inputs at each epoch, i.e., the case of using on-line noisy copies. Therefore, this article can also be viewed as an analysis on a method using noise injection into a training process by DA. We considered the training process under three training situations which are the full-batch training under the sum of squared errors, and full-batch and mini-batch training under the mean squared error. We showed that, in all cases, training for DA with on-line copies is approximately equivalent to the l2 regularization training for which variance of injected noise is important, whereas the number of copies is not. Moreover, we showed that DA with on-line copies apparently leads to an increase of learning rate in full-batch condition under the sum of squared errors and the mini-batch condition under the mean squared error. The apparent increase in learning rate and regularization effect can be attributed to the original input and additive noise in noisy copies, respectively. These results are confirmed in a numerical experiment in which we found that our result can be applied to usual off-line DA in an under-parameterization scenario and can not in an over-parametrization scenario. Moreover, we experimentally investigated the training process of neural networks under DA with off-line noisy copies and found that our analysis on linear regression can be qualitatively applied to neural networks.

Intelligent traffic monitoring provides information support for autonomous driving, which is widely used in intelligent transportation systems (ITSs). A method for estimating vehicle moving target parameters based on millimeter-wave radars is proposed to solve the problem of low detection accuracy due to velocity ambiguity and Doppler-angle coupling in the process of traffic monitoring. First of all, a MIMO antenna array with overlapping elements is constructed by introducing them into the typical design of MIMO radar array antennas. The motion-induced phase errors are eliminated by the phase difference among the overlapping elements. Then, the position errors among them are corrected through an iterative method, and the angle of multiple targets is estimated. Finally, velocity disambiguation is performed by adopting the error-corrected phase difference among the overlapping elements. An accurate estimation of vehicle moving target angle and velocity is achieved. Through Monte Carlo simulation experiments, the angle error is 0.1° and the velocity error is 0.1m/s. The simulation results show that the method can be used to effectively solve the problems related to velocity ambiguity and Doppler-angle coupling, meanwhile the accuracy of velocity and angle estimation can be improved. An improved algorithm is tested on the vehicle datasets that are gathered in the forward direction of ordinary public scenes of a city. The experimental results further verify the feasibility of the method, which meets the real-time and accuracy requirements of ITSs on vehicle information monitoring.

This paper presents a hippocampal cognitive prosthesis chip designed for restoring the ability to form new long-term memories due to hippocampal system damage. The system-on-chip (SOC) consists of a 16-channel micro-power low-noise amplifier (LNA), high-pass filters, analog-digital converters (ADCs), a 16-channel spike-sorter, a generalized Laguerre-Volterra model multi-input, multi-output (GLVM-MIMO) hippocampal processor, an 8-channel neural stimulator and peripheral circuits. The proposed LNA achieved a voltage gain of 50dB, input-referred noise of 3.95µVrms, and noise efficiency factor (NEF) of 3.45 with the power consumption of 3.3µW. High-pass filters with a 300-Hz bandwidth are used to filter out the unwanted local field potential (LFP). 4 12-bit successive approximation register (SAR) ADCs with a signal-to-noise-and-distortion ratio (SNDR) of 63.37dB are designed for the digitization of the neural signals. A 16-channel spike-sorter has been integrated in the chip enabling a detection accuracy of 98.3% and a classification accuracy of 93.4% with power consumption of 19µW/ch. The MIMO hippocampal model processor predict output spatio-temporal patterns in CA1 according to the recorded input spatio-temporal patterns in CA3. The neural stimulator performs bipolar, symmetrical charge-balanced stimulation with a maximum current of 310µA, triggered by the processor output. The chip has been fabricated in 40nm standard CMOS technology, occupying a silicon area of 3mm2.

Card-based cryptography realizes secure multiparty computation using physical cards. In 2018, Watanabe et al. proposed a card-based three-input majority voting protocol using three cards. In a card-based cryptographic protocol with n-bit inputs, it is known that a protocol using shuffles requires at least 2n cards. In contrast, as Watanabe et al.'s protocol, a protocol using private permutations can be constructed with fewer cards than the lower bounds above. Moreover, an n-input protocol using private permutations would not even require n cards in principle since a private permutation depending on an input can represent the input without using additional cards. However, there are only a few protocols with fewer than n cards. Recently, Abe et al. extended Watanabe et al.'s protocol and proposed an n-input majority voting protocol with n cards and n + ⌊n/2⌋ + 1 private permutations. This paper proposes an n-input majority voting protocol with ⌈n/2⌉ + 1 cards and 2n-1 private permutations, which is also obtained by extending Watanabe et al.'s protocol. Compared with Abe et al.'s protocol, although the number of private permutations increases by about n/2, the number of cards is reduced by about n/2. In addition, unlike Abe et al.'s protocol, our protocol includes Watanabe et al.'s protocol as a special case where n=3.

In this paper, a passive multiple-input multiple-output (MIMO) radar system with widely separated antennas that estimates the positions and velocities of multiple moving targets by utilizing time delay (TD) and doppler shift (DS) measurements is proposed. Passive radar systems can detect targets by using multiple uncoordinated and un-synchronized illuminators and we assume that all the measurements including TD and DS have been known by a preprocessing method. In this study, the algorithm can be divided into three stages. First, based on location information within a certain range and utilizing the DBSCAN cluster algorithm we can obtain the initial position of each target. In the second stage according to the correlation between the TD measurements of each target in a specific receiver and the DSs, we can find the set of DS measurements for each target. Therefore, the initial speed estimated values can be obtained employing the least squares (LS) method. Finally, maximum likelihood (ML) estimation of a first-order Taylor expansion joint TD and DS is applied for a better solution. Extensive simulations show that the proposed algorithm has a good estimation performance and can achieve the Cramér-Rao lower bound (CRLB) under the condition of moderate measurement errors.

Millimeter wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems generally adopt hybrid precoding combining digital and analog precoder as an alternative to full digital precoding to reduce RF chains and energy consumption. In order to balance the relationship between spectral efficiency, energy efficiency and hardware complexity, the hybrid-connected system structure should be adopted, and then the solution process of hybrid precoding can be simplified by decomposing the total achievable rate into several sub-rates. However, the singular value decomposition (SVD) incurs high complexity in calculating the optimal unconstrained hybrid precoder for each sub-rate. Therefore, this paper proposes PAST, a low complexity hybrid precoding algorithm based on projection approximate subspace tracking. The optimal unconstrained hybrid precoder of each sub-rate is estimated with the PAST algorithm, which avoids the high complexity process of calculating the left and right singular vectors and singular value matrix by SVD. Simulations demonstrate that PAST matches the spectral efficiency of SVD-based hybrid precoding in full-connected (FC), hybrid-connected (HC) and sub-connected (SC) system structure. Moreover, the superiority of PAST over SVD-based hybrid precoding in terms of complexity and increases with the number of transmitting antennas.

A multibeam massive multiple input multiple output (MIMO) configuration employs beam selection with high power in the analog part and executes a blind algorithm such as the independent component analysis (ICA), which does not require channel state information in the digital part. Two-dimensional (2-D) multibeams are considered in actual power losses and beam steering errors regarding the multibeam patterns. However, the performance of these 2-D beams depends on the beam pattern of the multibeams, and they are not optimal multibeam patterns suitable for multibeam massive MIMO configurations. In this study, we clarify the performance difference due to the difference of the multibeam pattern and consider the multibeam pattern suitable for the system condition. Specifically, the optimal multibeam pattern was determined with the element spacing and beamwidth of the element directivity as parameters, and the effectiveness of the proposed method was verified via computer simulations.

In recent years, there has been significant interest in information-theoretic security techniques that encrypt physical layer signals. We have proposed chaos modulation, which has both physical layer security and channel coding gain, as one such technique. In the chaos modulation method, the channel coding gain can be increased using a turbo mechanism that exchanges the log-likelihood ratio (LLR) with an external concatenated code using the max-log approximation. However, chaos modulation, which is a type of Gaussian modulation, does not use fixed mapping, and the distance between signal points is not constant; therefore, the accuracy of the max-log approximated LLR degrades under poor channel conditions. As a result, conventional methods suffer from performance degradation owing to error propagation in turbo decoding. Therefore, in this paper, we propose a new LLR clipping method that can be optimally applied to chaos modulation by limiting the confidence level of LLR and suppressing error propagation. For effective clipping on chaos modulation that does not have fixed mappings, the average confidence value is obtained from the extrinsic LLR calculated from the demodulator and decoder, and clipping is performed based on this value, either in the demodulator or the decoder. Numerical results indicated that the proposed method achieves the same performance as the one using the exact LLR, which requires complicated calculations. Furthermore, the security feature of the proposed system is evaluated, and we observe that sufficient security is provided.

We consider a regulation problem for an uncertain chain of integrators with an unknown time-varying delay in the input. To deal with uncertain parameters and unknown delay, we propose an adaptive event-triggered controller with a dynamic gain. We show that the system is globally regulated and interexecution times are lower bounded. Moreover, we show that these lower bounds can be enlarged by adjusting a control parameter. An example is given for clear illustration.

This paper proposes a low-complexity variational Bayesian inference (VBI)-based method for massive multiple-input multiple-output (MIMO) downlink channel estimation. The temporal correlation at the mobile user side is jointly exploited to enhance the channel estimation performance. The key to the success of the proposed method is the column-independent factorization imposed in the VBI framework. Since we separate the Bayesian inference for each column vector of signal-of-interest, the computational complexity of the proposed method is significantly reduced. Moreover, the temporal correlation is automatically uncoupled to facilitate the updating rule derivation for the temporal correlation itself. Simulation results illustrate the substantial performance improvement achieved by the proposed method.

Expectation propagation (EP) is a powerful algorithm for signal recovery in compressed sensing. This letter proposes correction of a variance message before denoising to improve the performance of EP in the high signal-to-noise ratio (SNR) regime for finite-sized systems. The variance massage is replaced by an observation-dependent consistent estimator of the mean-square error in estimation before denoising. Massive multiple-input multiple-output (MIMO) is considered to verify the effectiveness of the proposed correction. Numerical simulations show that the proposed variance correction improves the high SNR performance of EP for massive MIMO with a few hundred transmit and receive antennas.

With the advent of 5G and external pressure to reduce greenhouse gas emissions, wireless transceivers with low power consumption are strongly desired for future cellular systems. At the same time, increased modulation order due to the evolution of cellular systems will force power amplifiers to operate at much larger output power back-off to prevent EVM degradation. This paper begins with an analysis of load modulation and asymmetrical Doherty amplifiers. Measurement results will show an apparent 60% efficiency plateau for modulated signals with a large peak-to-average power ratio (PAPR). To exceed this efficiency limitation, the second part of this paper focuses on a new amplification topology based on the amalgamation between Doherty and outphasing. Measurement results of the proposed Doherty-outphasing power amplifier (DOPA) will confirm the feasibility of the approach with a modulated efficiency greater than 70% measured at 10 dB output power back-off.

Video inpainting is a task of filling missing regions in videos. In this task, it is important to efficiently use information from other frames and generate plausible results with sufficient temporal consistency. In this paper, we present a video inpainting method jointly using affine transformation and deformable convolutions for frame alignment. The former is responsible for frame-scale rough alignment and the latter performs pixel-level fine alignment. Our model does not depend on 3D convolutions, which limits the temporal window, or troublesome flow estimation. The proposed method achieves improved object removal results and better PSNR and SSIM values compared with previous learning-based methods.