Integrated circuits used in automotive or aerospace applications must have high soft error tolerance. Redundant Flip Flops (FFs) are effective to improve the soft error tolerance. However, these countermeasures have large performance overheads and can be excessive for terrestrial applications. This paper proposes two types of radiation-hardened FFs named Primary Latch Transmission gate FF (PLTGFF) and Feed-Back Gate Tri-state Inverter FF (FBTIFF) for terrestrial use. By increasing the critical charge (Qcrit) at weak nodes, soft error tolerance of them were improved with low performance overheads. PLTGFF has the 5% area, 4% delay, and 10% power overheads, while FBTIFF has the 42% area, 10% delay, and 22% power overheads. They were fabricated in a 65 nm bulk process. By α-particle and spallation neutron irradiation tests, the soft error rates are reduced by 25% for PLTGFF and 50% for FBTIFF compared to a standard FF. In the terrestrial environment, the proposed FFs have better trade-offs between reliability and performance than those of multiplexed FFs such as the dual-interlocked storage cell (DICE) with larger overheads than the proposed FFs.

In this study, we introduce a lateral electric-field driving system based on continuous potential-difference driving using lateral transparent electrodes to achieve a wide viewing zone angle in electronic holographic displays. We evaluate light modulation to validate the independent driving capability of each pixel at a high resolution (pixel pitch: 1 μm). Additionally, we demonstrate the feasibility of two-dimensional driving by integrating the driving and ground electrodes.

In a space-based automatic identification system (AIS), a satellite has a wide coverage area and thus can receive AIS signals from ships in the high seas. However, wide coverage can cause multiple AIS packets to collide with each other at the satellite receiver. Furthermore, transmitted packets are affected by channel parameters, such as Doppler shifts, channel impulse response, and propagation delay time, which are remarkably different in each packet because these parameters depend on the distance and relative speed between ships and the satellite. Therefore, these parameters should be estimated and used for multiuser detection to detect collided packets separately. Nevertheless, when the received power difference between packets or desired to undesired signal power ratio (DUR) is low, the accuracy of the channel parameter estimation is degraded so severely, such that multiuser detection cannot maintain sufficient bit error rate (BER) performance. To compensate for the reduced accuracy, this paper proposes a highly accurate channel estimation method. In addition to the conventional correlation-based channel estimation, the proposed method applies the quasi-Newton and least-squares methods to estimate Doppler frequency and channel impulse response, respectively. Regarding the propagation delay time, the conventional correlation-based channel estimation is repeated for improvement. Multiuser detection based on the Viterbi algorithm is performed using the estimated channel parameters. Computer simulations were conducted under the conditions of two collision packets and Rician fading, and the results show that the proposed method can significantly improve the accuracy of the channel estimation and BER performance than the conventional method.

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.

This paper proposes overloaded MIMO spatial multiplexing that can increase the number of spatially multiplexed signal streams despite of the number of antennas on a terminal and that on a receiver. We propose extension of the channel matrix for the spatial multiplexing to achieve the superb multiplexing performance. Precoding based on the extended channel matrix plays a crucial role in carrying out such spatial multiplexing. We consider three types of QR-decomposition techniques for the proposed spatial multiplexing to improve the transmission performance. The transmission performance of the proposed spatial multiplexing is evaluated by computer simulation. The simulation reveals that the proposed overloaded MIMO spatial multiplexing can implement 6 stream-spatial multiplexing in a 2×2 MIMO system, i.e., the overloading ratio of 3.0. The superior transmission performance is achieved by the proposed overloaded MIMO spatial multiplexing with one of the QR-decomposition techniques.

To super-resolve low-resolution (LR) face image suffering from strong noise and fuzzy interference, we present a novel approach for noisy face super-resolution (SR) that is based on three-level information representation constraints. To begin with, we develop a feature distillation network that focuses on extracting pertinent face information, which incorporates both statistical anti-interference models and latent contrast algorithms. Subsequently, we incorporate a face identity embedding model and a discrete wavelet transform model, which serve as additional supervision mechanisms for the reconstruction process. The face identity embedding model ensures the reconstruction of identity information in hypersphere identity metric space, while the discrete wavelet transform model operates in the wavelet domain to supervise the restoration of spatial structures. The experimental results clearly demonstrate the efficacy of our proposed method, which is evident through the lower Learned Perceptual Image Patch Similarity (LPIPS) score and Fréchet Inception Distances (FID), and overall practicability of the reconstructed images.

In this paper, we present a framework for composing discrete-event simulation models from a large amount of airspace traffic data without using any specific waypoints. The framework consists of two parts. In the first part, abstracted route graphs that indicate representative routes in the airspace are composed. We propose two methods for extracting important routes in the form of graphs based on combination of various technologies such as space partition, trajectory clustering, and skeleton extraction. In the second part, discrete-event simulation models are composed based on statistical information on flight time along each edge of the abstracted route graph. The composed simulation models have intermediate granularity between micro models, such as multi-agent simulation, and macro models, such as queuing models, and therefore they should be classified as mesoscopic models. Finally, we show numerical results to evaluate the accuracy of the simulation model.

The Sparkle permutation family is used as an underlying building block of the authenticated encryption scheme Schwaemm, and the hash function Esch which are a part of one of finalists in the National Institute of Standards and Technology (NIST) lightweight cryptography standardization process. In this paper, we present distinguishing attacks on 6-round Sparkle384 and 7-round Sparkle512. We used divide-and-conquer approach and the fact that Sparkle permutations are keyless, as a different approach from designers’ long trail strategy. Our attack on Sparkle384 requires much lower time complexity than existing best one; our attack on Sparkle512 is best in terms of the number of attacked rounds, as far as we know. However, our results do not controvert the security claim of Sparkle designers.

In this study, we devise several seat selection screens for a movie theater ticket reservation system that applies nudges to achieve spatial crowd smoothing without relying on economic incentives. We design three types of nudges that achieve the following: (i) render seats in less-crowded areas noticeable; (ii) present social norms; and (iii) suggest seats in less-crowded areas to people who have selected seats in crowded areas. Results of verification experiment show that (ii) the presentation of social norms is generally effective in avoiding congestion regardless of the ticket sales and (ii) the text of the presented social norms is more effective in avoiding congestion when it contains motivational sentences than when it is verbally expressed. Furthermore, the results indicate that (i) rendering seats in less-crowded areas more conspicuous and (iii) suggesting seats in less-crowded areas to those who select seats in more crowded areas may be effective in avoiding congestion, depending on the ticket sales. Consequently, the feasibility of spatial crowd smoothing without relying on economic incentives for the seat selection screen of a ticket reservation system that applies nudges is demonstrated.

Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.

This article focuses on improving the BiSeNet v2 bilateral branch image segmentation network structure, enhancing its learning ability for spatial details and overall image segmentation accuracy. A modified network called “BiconvNet” is proposed. Firstly, to extract shallow spatial details more effectively, a parallel concatenated strip and dilated (PCSD) convolution module is proposed and used to extract local features and surrounding contextual features in the detail branch. Continuing on, the semantic branch is reconstructed using the lightweight capability of depth separable convolution and high performance of ConvNet, in order to enable more efficient learning of deep advanced semantic features. Finally, fine-tuning is performed on the bilateral guidance aggregation layer of BiSeNet v2, enabling better fusion of the feature maps output by the detail branch and semantic branch. The experimental part discusses the contribution of stripe convolution and different sizes of empty convolution to image segmentation accuracy, and compares them with common convolutions such as Conv2d convolution, CG convolution and CCA convolution. The experiment proves that the PCSD convolution module proposed in this paper has the highest segmentation accuracy in all categories of the Cityscapes dataset compared with common convolutions. BiConvNet achieved a 9.39% accuracy improvement over the BiSeNet v2 network, with only a slight increase of 1.18M in model parameters. A mIoU accuracy of 68.75% was achieved on the validation set. Furthermore, through comparative experiments with commonly used autonomous driving image segmentation algorithms in recent years, BiConvNet demonstrates strong competitive advantages in segmentation accuracy on the Cityscapes and BDD100K datasets.

We report on a method for reconstructing the spectrum of incident light from a single image captured by a snapshot multispectral camera. The camera has a dielectric multilayer multispectral filter array (MSFA) integrated onto a CMOS image sensor. Sparse estimation algorithm was applied to reconstruct the spectrum. Using Gaussian functions with various bandwidths and central wavelengths as the bases matrix, the algorithm has been shown to be highly accurate for estimating the spectra of both narrowband monochromatic and broadband fluorescent light emitting diodes (LEDs), regardless of the wavelength band.

In this paper, a novel Enhanced Spatial Modulation-based Orthogonal Time Frequency Space (ESM-OTFS) is proposed to maximize the benefits of enhanced spatial modulation (ESM) and orthogonal time frequency space (OTFS) transmission. The primary objective of this novel modulation is to enhance transmission reliability, meeting the demanding requirements of high transmission rates and rapid data transfer in future wireless communication systems. The paper initially outlines the system model and specific signal processing techniques employed in ESM-OTFS. Furthermore, a novel detector based on sparse signal estimation is presented specifically for ESM-OTFS. The sparse signal estimation is performed using a fully factorized posterior approximation using Variational Bayesian Inference that leads to a low complexity solution without any matrix inversions. Simulation results indicate that ESM-OTFS surpasses traditional spatial modulation-based OTFS, and the newly introduced detection algorithm outperforms other linear detection methods.

In this paper, we propose a novel communication scheme that combines reconfigurable intelligent surface with transmitted adaptive space shift keying (RIS-TASSK), where the number of active antennas is not fixed. In each time slot, the desired candidate antenna or antenna combination will be selected from all available antenna combinations for conveying information bits. Besides, an antenna selection method based on channel gains is proposed for RIS-TASSK to improve the bit error rate (BER) performance and decrease the complexity, respectively. By comparing with the RIS-aided transmitted space shift keying and RIS-aided transmitted generalized space shift keying schemes, the simulation and theoretical results show that the proposed scheme has better BER performance and appropriate complexity.

A state-space approach for adaptive second-order IIR notch digital filters is explored. A simplified iterative algorithm is derived from the gradient-descent method to minimize the mean-squared output of an adaptive notch digital filter. The stability and parameter-estimation bias are then analyzed by employing a first-order linear dynamical system. As a consequence, it is clarified that the resulting parameter estimate is unbiased. Finally, a numerical example is presented to demonstrate the validity and effectiveness of the adaptive state-space notch digital filter and bias analysis of parameter estimation.

We propose a novel peak-to-average power ratio (PAPR) reduction method based on a peak cancellation (PC) signal vector that considers the variance in the average signal power among transmitter antennas for massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) signals using the null space in a MIMO channel. First, we discuss the conditions under which the PC signal vector achieves a sufficient PAPR reduction effect after its projection onto the null space of the MIMO channel. The discussion reveals that the magnitude of the correlation between the PC signal vector before projection and the transmission signal vector should be as low as possible. Based on this observation and the fact that to reduce the PAPR it is helpful to suppress the variation in the transmission signal power among antennas, which may be enhanced by beamforming (BF), we propose a novel method for generating a PC signal vector. The proposed PC signal vector is designed so that the signal power levels of all the transmitter antennas are limited to be between the maximum and minimum power threshold levels at the target timing. The newly introduced feature in the proposed method, i.e., increasing the signal power to be above the minimum power threshold, contributes to suppressing the transmission signal power variance among antennas and to improving the PAPR reduction capability after projecting the PC signal onto the null space in the MIMO channel. This is because the proposed method decreases the magnitude of the correlation between the PC signal vectors before its projection and the transmission signal vectors. Based on computer simulation results, we show that the PAPR reduction performance of the proposed method is improved compared to that for the conventional method and the proposed method reduces the computational complexity compared to that for the conventional method for achieving the same target PAPR.

In this paper, the Towards High Performance Voxel-based 3D Object Detection (Voxel-RCNN) three-dimensional (3D) point cloud object detection model is used as the benchmark network. Aiming at the problems existing in the current mainstream 3D point cloud voxelization methods, such as the backbone and the lack of feature expression ability under the bird’s-eye view (BEV), a high-performance voxel-based 3D object detection network (Reinforced Voxel-RCNN) is proposed. Firstly, a 3D feature extraction module based on the integration of inverted residual convolutional network and weight normalization is designed on the 3D backbone. This module can not only well retain more point cloud feature information, enhance the information interaction between convolutional layers, but also improve the feature extraction ability of the backbone network. Secondly, a spatial feature-semantic fusion module based on spatial and channel attention is proposed from a BEV perspective. The mixed use of channel features and semantic features further improves the network’s ability to express point cloud features. In the comparison of experimental results on the public dataset KITTI, the experimental results of this paper are better than many voxel-based methods. Compared with the baseline network, the 3D average accuracy and BEV average accuracy on the three categories of Car, Cyclist, and Pedestrians are improved. Among them, in the 3D average accuracy, the improvement rate of Car category is 0.23%, Cyclist is 0.78%, and Pedestrians is 2.08%. In the context of BEV average accuracy, enhancements are observed: 0.32% for the Car category, 0.99% for Cyclist, and 2.38% for Pedestrians. The findings demonstrate that the algorithm enhancement introduced in this study effectively enhances the accuracy of target category detection.

This work focuses on training dataset enhancement of informative relational triplets for Scene Graph Generation (SGG). Due to the lack of effective supervision, the current SGG model predictions perform poorly for informative relational triplets with inadequate training samples. Therefore, we propose two novel training dataset enhancement modules: Feature Space Triplet Augmentation (FSTA) and Soft Transfer. FSTA leverages a feature generator trained to generate representations of an object in relational triplets. The biased prediction based sampling in FSTA efficiently augments artificial triplets focusing on the challenging ones. In addition, we introduce Soft Transfer, which assigns soft predicate labels to general relational triplets to make more supervisions for informative predicate classes effectively. Experimental results show that integrating FSTA and Soft Transfer achieve high levels of both Recall and mean Recall in Visual Genome dataset. The mean of Recall and mean Recall is the highest among all the existing model-agnostic methods.

Wafer-level performance prediction techniques have been increasingly gaining attention in production LSI testing due to their ability to reduce measurement costs without compromising test quality. Despite the availability of several efficient methods, the site-to-site variation commonly observed in multi-site testing for radio frequency circuits remains inadequately addressed. In this manuscript, we propose a wafer-level performance prediction approach for multi-site testing that takes into account the site-to-site variation. Our proposed method is built on the Gaussian process, a widely utilized wafer-level spatial correlation modeling technique, and enhances prediction accuracy by extending hierarchical modeling to leverage the test site information test engineers provide. Additionally, we propose a test-site sampling method that maximizes cost reduction while maintaining sufficient estimation accuracy. Our experimental results, which employ industrial production test data, demonstrate that our proposed method can decrease the estimation error to 1/19 of that a conventional method achieves. Furthermore, our sampling method can reduce the required measurements by 97% while ensuring satisfactory estimation accuracy.

Point cloud video contains not only color information but also spatial position information and usually has large volume of data. Typical rate distortion optimization algorithms based on Human Visual System only consider the color information, which limit the coding performance. In this paper, a Coding Tree Unit (CTU) level quantization parameter (QP) adjustment algorithm based on JND and spatial complexity is proposed to improve the subjective and objective quality of Video-Based Point Cloud Compression (V-PCC). Firstly, it is found that the JND model is degraded at CTU level for attribute video due to the pixel filling strategy of V-PCC, and an improved JND model is designed using the occupancy map. Secondly, a spatial complexity detection metric is designed to measure the visual importance of each CTU. Finally, a CTU-level QP adjustment scheme based on both JND levels and visual importance is proposed for geometry and attribute video. The experimental results show that, compared with the latest V-PCC (TMC2-18.0) anchors, the BD-rate is reduced by -2.8% and -3.2% for D1 and D2 metrics, respectively, and the subjective quality is improved significantly.