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.

In this research, we investigated the digital/analog-operation utilizing ferroelectric nondoped HfO2 (FeND-HfO2) as a blocking layer (BL) in the Hf-based metal/oxide/nitride/oxide/Si (MONOS) nonvolatile memory (NVM), so called FeNOS NVM. The Al/HfN0.5/HfN1.1/HfO2/p-Si(100) FeNOS diodes realized small equivalent oxide thickness (EOT) of 4.5 nm with the density of interface states (Dit) of 5.3 × 1010 eV-1cm-2 which were suitable for high-speed and low-voltage operation. The flat-band voltage (VFB) was well controlled as 80-100 mV with the input pulses of ±3 V/100 ms controlled by the partial polarization of FeND-HfO2 BL at each 2-bit state operated by the charge injection with the input pulses of +8 V/1-100 ms.

Minutiae pattern extraction plays a crucial role in fingerprint registration and identification for electronic applications. However, the extraction accuracy is seriously compromised by the presence of contaminated ridge lines and complex background scenarios. General image processing-based methods, which rely on many prior hypotheses, fail to effectively handle minutiae extraction in complex scenarios. Previous works have shown that CNN-based methods can perform well in object detection tasks. However, the deep neural networks (DNNs)-based methods are restricted by the limitation of public labeled datasets due to legitimate privacy concerns. To address these challenges comprehensively, this paper presents a fully automated minutiae extraction method leveraging DNNs. Firstly, we create a fingerprint minutiae dataset using a semi-automated minutiae annotation algorithm. Subsequently, we propose a minutiae extraction model based on Residual Networks (Resnet) that enables end-to-end prediction of minutiae. Moreover, we introduce a novel non-maximal suppression (NMS) procedure, guided by the Generalized Intersection over Union (GIoU) metric, during the inference phase to effectively handle outliers. Experimental evaluations conducted on the NIST SD4 and FVC 2004 databases demonstrate the superiority of the proposed method over existing state-of-the-art minutiae extraction approaches.

At Crypto 2019, Gohr first adopted the neural distinguisher for differential cryptanalysis, and since then, this work received increasing attention. However, most of the existing work focuses on improving and applying the neural distinguisher, the studies delving into the intrinsic principles of neural distinguishers are finite. At Eurocrypt 2021, Benamira et al. conducted a study on Gohr’s neural distinguisher. But for the neural distinguishers proposed later, such as the r-round neural distinguishers trained with k ciphertext pairs or ciphertext differences, denoted as NDcpk_r (Gohr’s neural distinguisher is the special NDcpk_r with K = 1) and NDcdk_r , such research is lacking. In this work, we devote ourselves to study the intrinsic principles and relationship between NDcdk_r and NDcpk_r. Firstly, we explore the working principle of NDcd1_r through a series of experiments and find that it strongly relies on the probability distribution of ciphertext differences. Its operational mechanism bears a strong resemblance to that of NDcp1_r given by Benamira et al.. Therefore, we further compare them from the perspective of differential cryptanalysis and sample features, demonstrating the superior performance of NDcp1_r can be attributed to the relationships between certain ciphertext bits, especially the significant bits. We then extend our investigation to NDcpk_r, and show that its ability to recognize samples heavily relies on the average differential probability of k ciphertext pairs and some relationships in the ciphertext itself, but the reliance between k ciphertext pairs is very weak. Finally, in light of the findings of our research, we introduce a strategy to enhance the accuracy of the neural distinguisher by using a fixed difference to generate the negative samples instead of the random one. Through the implementation of this approach, we manage to improve the accuracy of the neural distinguishers by approximately 2% to 8% for 7-round Speck32/64 and 9-round Simon32/64.

This paper shows that sum-of-product expression (SOP) minimization produces the generalization ability. We show this in three steps. First, various classes of SOPs are generated. Second, minterms of SOP are randomly selected to generate partially defined functions. And, third, from the partially defined functions, original functions are reconstructed by SOP minimization. We consider Achilles heel functions, majority functions, monotone increasing cascade functions, functions generated from random SOPs, monotone increasing random SOPs, circle functions, and globe functions. As for the generalization ability, the presented method is compared with Naive Bayes, multi-level perceptron, support vector machine, JRIP, J48, and random forest. For these functions, in many cases, only 10% of the input combinations are sufficient to reconstruct more than 90% of the truth tables of the original functions.

This paper proposes a novel differential active self-interference canceller (DASIC) algorithm for asynchronous in-band full-duplex (IBFD) Gaussian filtered frequency shift keying (GFSK), which is designed for wireless Internet of Things (IoT). In IBFD communications, where two terminals simultaneously transmit and receive signals in the same frequency band, there is an extremely strong self-interference (SI). The SI can be mitigated by an active SI canceller (ASIC), which subtracts an interference replica based on channel state information (CSI) from the received signal. The challenging problem is the realization of asynchronous IBFD for wireless IoT in indoor environments. In the asynchronous mode, pilot contamination is induced by the non-orthogonality between asynchronous pilot sequences. In addition, the transceiver suffers from analog front-end (AFE) impairments, such as phase noise. Due to these impairments, the SI cannot be canceled entirely at the receiver, resulting in residual interference. To address the above issue, the DASIC incorporates the principle of the differential codec, which enables to suppress SI without the CSI estimation of SI owing to the differential structure. Also, on the premise of using an error correction technique, iterative detection and decoding (IDD) is applied to improve the detection capability while exchanging the extrinsic log-likelihood ratio (LLR) between the maximum a-posteriori probability (MAP) detector and the channel decoder. Finally, the validity of using the DASIC algorithm is evaluated by computer simulations in terms of the packet error rate (PER). The results clearly demonstrate the possibility of realizing asynchronous IBFD.

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.

At CT-RSA 2022, Bossert et al. proposed Pholkos family, an efficient large-state tweakable block cipher. In order to evaluate the security of differential attacks on Pholkos, they obtained the lower bounds for the number of active S-boxes for Pholkos using MILP (Mixed Integer Linear Programming) tools. Based on it, they claimed that Pholkos family is secure against differential attacks. However, they only gave rough security bounds in both of related-tweak and related-tweakey settings. To be more precise, they estimated the lower bounds of the number of active S-boxes for relatively-large number of steps by just summing those in the small number of steps. In this paper, we utilize efficient search methods based on MILP to obtain tighter lower bounds for the number of active S-boxes in a larger number of steps. For the first time, we derive the exact minimum number of active S-boxes of each variant up to the steps where the security against differential attacks can be ensured in related-tweak and related-tweakey settings. Our results indicate that Pholkos-256-128/256-256/512/1024 is secure after 4/5/3/4 steps in the related-tweak setting, and after 5/6/3/4 steps in the related-tweakey setting, respectively. Our results enable reducing the required number of steps to be secure against differential attacks of Pholkos-256-256 in related-tweak setting, and Pholkos-256-128/256 and Pholkos-1024 in the related-tweakey setting by one step, respectively.

Lightweight block ciphers have gained attention in recent years due to the increasing demand for sensor nodes, RFID tags, and various applications. In such a situation, lightweight block ciphers Piccolo and TWINE have been proposed. Both Piccolo and TWINE are designed based on the Generalized Feistel Structure. However, it is crucial to address the potential vulnerability of these structures to the impossible differential attack. Therefore, detailed security evaluations against this attack are essential. This paper focuses on conducting bit-level evaluations of Piccolo and TWINE against related-key impossible differential attacks by leveraging SAT-aided approaches. We search for the longest distinguishers under the condition that the Hamming weight of the active bits of the input, which includes plaintext and master key differences, and output differences is set to 1, respectively. Additionally, for Tweakable TWINE, we search for the longest distinguishers under the related-tweak and related-tweak-key settings. The result for Piccolo with a 128-bit key, we identify the longest 16-round distinguishers for the first time. In addition, we also demonstrate the ability to extend these distinguishers to 17 rounds by taking into account the cancellation of the round key and plaintext difference. Regarding evaluations of TWINE with a 128-bit key, we search for the first time and reveal the distinguishers up to 19 rounds. For the search for Tweakable TWINE, we evaluate under the related-tweak-key setting for the first time and reveal the distinguishers up to 18 rounds for 80-bit key and 19 rounds for 128-bit key.

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.

Fish skin color detection plays a critical role in aquaculture. However, challenges arise from image color cast and the limited dataset, impacting the accuracy of the skin color detection process. To address these issues, we proposed a novel fish skin color detection method, termed VH-YOLOv5s. Specifically, we constructed a dataset for fish skin color detection to tackle the limitation posed by the scarcity of available datasets. Additionally, we proposed a Variance Gray World Algorithm (VGWA) to correct the image color cast. Moreover, the designed Hybrid Spatial Pyramid Pooling (HSPP) module effectively performs multi-scale feature fusion, thereby enhancing the feature representation capability. Extensive experiments have demonstrated that VH-YOLOv5s achieves excellent detection results on the Plectropomus leopardus skin color dataset, with a precision of 91.7%, recall of 90.1%, mAP@0.5 of 95.2%, and mAP@0.5:0.95 of 57.5%. When compared to other models such as Centernet, AutoAssign, and YOLOX-s, VH-YOLOv5s exhibits superior detection performance, surpassing them by 2.5%, 1.8%, and 1.7%, respectively. Furthermore, our model can be deployed directly on mobile phones, making it highly suitable for practical applications.

Spatial modulation (SM) is a type of multiple-input multiple-output (MIMO) technology that provides several benefits over traditional MIMO systems. SM-MIMO is characterized by its unique transmission principle, which results in lower costs, enhanced spectrum utilization, and reduced inter-channel interference. To optimize channel estimation performance over frequency-selective channels in the spatial modulation system, cross Z-complementary pairs (CZCPs) have been proposed as training sequences. The zero correlation zone (ZCZ) properties of CZCPs for auto-correlation sums and cross-correlation sums enable them to achieve optimal channel estimation performance. In this paper, we systematically construct CZCPs based on binary Golay complementary pairs and binary Golay complementary pairs via Turyn’s method. We employ a special matrix operation and concatenation method to obtain CZCPs with new lengths 2M + N and 2(M + L), where M and L are the lengths of binary GCP, and N is the length of binary GCP via Turyn’s method. Further, we obtain the perfect CZCP with new length 4N and extend the lengths of CZCPs.

In recent years, measuring biomagnetic fields in the Earth’s field by differential measurements of scalar-mode OPMs have been actively attempted. In this study, the sensitivity of the scalar-mode OPMs under the geomagnetic environment in the laboratory was studied by numerical simulation. Although the noise level of the scalar-mode OPM in the laboratory environment was calculated to be 104 pT/$\sqrt{\mathrm{Hz}}$, the noise levels using the first-order and the second-order differential configurations were found to be 529 fT/cm/$\sqrt{\mathrm{Hz}}$ and 17.2 fT/cm2/$\sqrt{\mathrm{Hz}}$, respectively. This result indicated that scalar-mode OPMs can measure very weak magnetic fields such as MEG without high-performance magnetic shield roomns. We also studied the operating conditions by varying repetition frequency and temperature. We found that scalar-mode OPMs have an upper limit of repetition frequency and temperature, and that the repetition frequency should be set below 4 kHz and the temperature should be set below 120°C.

The automatic defect detection for fabric images is an essential mission in textile industry. However, there are some inherent difficulties in the detection of fabric images, such as complexity of the background and the highly uneven scales of defects. Moreover, the trade-off between accuracy and speed should be considered in real applications. To address these problems, we propose a novel model based on YOLOv4 to detect defects in fabric images, called Feature Augmentation YOLO (FA-YOLO). In terms of network structure, FA-YOLO adds an additional detection head to improve the detection ability of small defects and builds a powerful Neck structure to enhance feature fusion. First, to reduce information loss during feature fusion, we perform the residual feature augmentation (RFA) on the features after dimensionality reduction by using 1×1 convolution. Afterward, the attention module (SimAM) is embedded into the locations with rich features to improve the adaptation ability to complex backgrounds. Adaptive spatial feature fusion (ASFF) is also applied to output of the Neck to filter inconsistencies across layers. Finally, the cross-stage partial (CSP) structure is introduced for optimization. Experimental results based on three real industrial datasets, including Tianchi fabric dataset (72.5% mAP), ZJU-Leaper fabric dataset (0.714 of average F1-score) and NEU-DET steel dataset (77.2% mAP), demonstrate the proposed FA-YOLO achieves competitive results compared to other state-of-the-art (SoTA) methods.

This study investigates the efficacy of a partial decomposition approach in concept map recomposition tasks to reduce cognitive load while maintaining the benefits of traditional recomposition approaches. Prior research has demonstrated that concept map recomposition, involving the rearrangement of unconnected concepts and links, can enhance reading comprehension. However, this task often imposes a significant burden on learners’ working memory. To address this challenge, this study proposes a partial recomposition approach where learners are tasked with recomposing only a portion of the concept map, thereby reducing the problem space. The proposed approach aims at lowering the cognitive load while maintaining the benefits of traditional recomposition task, that is, learning effect and motivation. To investigate the differences in cognitive load, learning effect, and motivation between the full decomposition (the traditional approach) and partial decomposition (the proposed approach), we have conducted an experiment (N=78) where the participants were divided into two groups of “full decomposition” and “partial decomposition”. The full decomposition group was assigned the task of recomposing a concept map from a set of unconnected concept nodes and links, while the partial decomposition group worked with partially connected nodes and links. The experimental results show a significant reduction in the embedded cognitive load of concept map recomposition across different dimensions while learning effect and motivation remained similar between the conditions. On the basis of these findings, educators are recommended to incorporate partially disconnected concept maps in recomposition tasks to optimize time management and sustain learner motivation. By implementing this approach, instructors can conserve cognitive resources and allocate saved energy and time to other activities that enhance the overall learning process.

As one of privacy-enhancing authentications suitable for decentralized environments, ring signatures have intensively been researched. In ring signatures, each user can choose any ad-hoc set of users (specified by public keys) called a ring, and anonymously sign a message as one of the users. However, in applications of anonymous authentications, users may misbehave the service due to the anonymity, and thus a mechanism to exclude the anonymous misbehaving users is required. However, in the existing ring signature scheme, a trusted entity to open the identity of the user is needed, but it is not suitable for the decentralized environments. On the other hand, as another type of anonymous authentications, a decentralized blacklistable anonymous credential system is proposed, where anonymous misbehaving users can be detected and excluded by a blacklist. However, the DL-based instantiation needs O(N) proof size for the ring size N. In the research line of the DL-based ring signatures, an efficient scheme with O(log N) signature size, called DualRing, is proposed. In this paper, we propose a DL-based blacklistable ring signature scheme extended from DualRing, where in addition to the short O(log N) signature size for N, the blacklisting mechanism is realized to exclude misbehaving users. Since the blacklisting mechanism causes additional costs in our scheme, the signature size is O(log N+l), where l is the blacklist size.

The application of millimeter wave (mmWave) directional transmission technology in high-speed railway (HSR) scenarios helps to achieve the goal of multiple gigabit data rates with low latency. However, due to the high mobility of trains, the traditional initial access (IA) scheme with high time consumption is difficult to guarantee the effectiveness of the beam alignment. In addition, the high path loss at the coverage edge of the millimeter wave remote radio unit (mmW-RRU) will also bring great challenges to the stability of IA performance. Fortunately, the train trajectory in HSR scenarios is periodic and regular. Moreover, the cell-free network helps to improve the system coverage performance. Based on these observations, this paper proposes an efficient IA scheme based on location and history information in cell-free networks, where the train can flexibly select a set of mmW-RRUs according to the received signal quality. We specifically analyze the collaborative IA process based on the exhaustive search and based on location and history information, derive expressions for IA success probability and delay, and perform the numerical analysis. The results show that the proposed scheme can significantly reduce the IA delay and effectively improve the stability of IA success probability.

Ising machines can find optimum or quasi-optimum solutions of combinatorial optimization problems efficiently and effectively. It is known that, when a good initial solution is given to an Ising machine, we can finally obtain a solution closer to the optimal solution. However, several Ising machines cannot directly accept an initial solution due to its computational nature. In this paper, we propose a method to give quasi-initial solutions into Ising machines that cannot directly accept them. The proposed method gives the positive or negative external magnetic field coefficients (magnetic field controlling term) based on the initial solutions and obtains a solution by using an Ising machine. Then, the magnetic field controlling term is re-calculated every time an Ising machine repeats the annealing process, and hence the solution is repeatedly improved on the basis of the previously obtained solution. The proposed method is applied to the capacitated vehicle routing problem with an additional constraint (constrained CVRP) and the max-cut problem. Experimental results show that the total path distance is reduced by 5.78% on average compared to the initial solution in the constrained CVRP and the sum of cut-edge weight is increased by 1.25% on average in the max-cut problem.

Statistical wafer-level characteristic variation modeling offers an attractive method for reducing the measurement cost in large-scale integrated (LSI) circuit testing while maintaining test quality. In this method, the performance of unmeasured LSI circuits fabricated on a wafer is statistically predicted based on a few measured LSI circuits. Conventional statistical methods model spatially smooth variations in the wafers. However, actual wafers can exhibit discontinuous variations that are systematically caused by the manufacturing environment, such as shot dependence. In this paper, we propose a modeling method that considers discontinuous variations in wafer characteristics by applying the knowledge of manufacturing engineers to a model estimated using Gaussian process regression. In the proposed method, the process variation is decomposed into systematic discontinuous and global components to improve estimation accuracy. An evaluation performed using an industrial production test dataset indicates that the proposed method effectively reduces the estimation error for an entire wafer by over 36% compared with conventional methods.

Linear codes are widely studied due to their important applications in secret sharing schemes, authentication codes, association schemes and strongly regular graphs, etc. In this paper, firstly, a class of three-weight linear codes is constructed by selecting a new defining set, whose weight distributions are determined by exponential sums. Results show that almost all the constructed codes are minimal and thus can be used to construct secret sharing schemes with sound access structures. Particularly, a class of projective two-weight linear codes is obtained and based on which a strongly regular graph with new parameters is designed.