A 150 GHz fundamental oscillator employing an inter-stage matching network based on a transmission line is presented in this letter. The proposed oscillator consists of a two-stage common-emitter amplifier loop, whose inter-stage connections are optimized to meet the oscillation condition. The oscillator is designed in a 130-nm SiGe BiCMOS process that offers fT and fMAX of 350 GHz and 450 GHz. According to simulation results, an output power of 3.17 dBm is achieved at 147.6 GHz with phase noise of -115 dBc/Hz at 10 MHz offset and figure-of-merit (FoM) of -180 dBc/Hz.

This Letter focuses on deep learning-based monkeys' head swing counting problem. Nowadays, there are very few papers on monkey detection, and even fewer papers on monkeys' head swing counting. This research tries to fill in the gap and try to calculate the head swing frequency of monkeys through deep learning, where we further extend the traditional target detection algorithm. After analyzing object detection results, we localize the monkey's actions over a period. This Letter analyzes the task of counting monkeys' head swings, and proposes the standard that accurately describes a monkey's head swing. Under the guidance of this standard, the monkeys' head swing counting accuracy in 50 test videos reaches 94.23%.

Video text detection (VTD) aims to localize text instances in videos, which has wide applications for downstream tasks. To deal with the variances of different scenes and text instances, multiple models and feature fusion strategies were typically integrated in existing VTD methods. A VTD method consisting of sophisticated components can efficiently improve detection accuracy, but may suffer from a limitation for real-time applications. This paper aims to achieve real-time VTD with an adaptive lightweight end-to-end framework. Different from previous methods that represent text in a spatial domain, we model text instances in the Fourier domain. Specifically, we propose a scale-aware Fourier Contour Embedding method, which not only models arbitrary shaped text contours of videos as compact signatures, but also adaptively select proper scales for features in a backbone in the training stage. Then, we construct VTD-FCENet to achieve real-time VTD, which encodes temporal correlations of adjacent frames with scale-aware FCE in a lightweight and adaptive manner. Quantitative evaluations were conducted on ICDAR2013 Video, Minetto and YVT benchmark datasets, and the results show that our VTD-FCENet not only obtains the state-of-the-arts or competitive detection accuracy, but also allows real-time text detection on HD videos simultaneously.

Owing to the several cases wherein abnormal sounds, called adventitious sounds, are included in the lung sounds of a patient suffering from pulmonary disease, the objective of this study was to automatically detect abnormal sounds from auscultatory sounds. To this end, we expressed the acoustic features of the normal lung sounds of healthy people and abnormal lung sounds of patients using Gaussian mixture model (GMM)-hidden Markov models (HMMs), and distinguished between normal and abnormal lung sounds. In our previous study, we constructed left-to-right GMM-HMMs with a limited number of states. Because we expressed abnormal sounds that occur intermittently and repeatedly using limited states, the GMM-HMMs could not express the acoustic features of abnormal sounds. Furthermore, because the analysis frame length and intervals were long, the GMM-HMMs could not express the acoustic features of short time segments, such as heart sounds. Therefore, the classification rate of normal and abnormal respiration was low (86.60%). In this study, we propose the construction of ergodic GMM-HMMs with a repetitive structure for intermittent sounds. Furthermore, we considered a suitable frame length and frame interval to analyze acoustic features. Using the ergodic GMM-HMM, which can express the acoustic features of abnormal sounds and heart sounds that occur repeatedly in detail, the classification rate increased (89.34%). The results obtained in this study demonstrated the effectiveness of the proposed method.

Chinese spelling correction (CSC) models detect and correct a text typo based on the misspelled character and its context. Recently, Bert-based models have dominated the research of Chinese spelling correction. However, these methods only focus on the semantic information of the text during the pretraining stage, neglecting the learning of correcting spelling errors. Moreover, when multiple incorrect characters are in the text, the context introduces noisy information, making it difficult for the model to accurately detect the positions of the incorrect characters, leading to false corrections. To address these limitations, we apply the multimodal pre-trained language model ChineseBert to the task of spelling correction. We propose a self-distillation learning-based pretraining strategy, where a confusion set is used to construct text containing erroneous characters, allowing the model to jointly learns how to understand language and correct spelling errors. Additionally, we introduce a single-channel masking mechanism to mitigate the noise caused by the incorrect characters. This mechanism masks the semantic encoding channel while preserving the phonetic and glyph encoding channels, reducing the noise introduced by incorrect characters during the prediction process. Finally, experiments are conducted on widely used benchmarks. Our model achieves superior performance against state-of-the-art methods by a remarkable gain.

In Natural Language Understanding, intent detection and slot filling have been widely used to understand user queries. However, current methods tend to rely on single words and sentences to understand complex semantic concepts, and can only consider local information within the sentence. Therefore, they usually cannot capture long-distance dependencies well and are prone to problems where complex intentions in sentences are difficult to recognize. In order to solve the problem of long-distance dependency of the model, this paper uses ConceptNet as an external knowledge source and introduces its extensive semantic information into the multi-intent detection and slot filling model. Specifically, for a certain sentence, based on confidence scores and semantic relationships, the most relevant conceptual knowledge is selected to equip the sentence, and a concept context map with rich information is constructed. Then, the multi-head graph attention mechanism is used to strengthen context correlation and improve the semantic understanding ability of the model. The experimental results indicate that the model has significantly improved performance compared to other models on the MixATIS and MixSNIPS multi-intent datasets.

In recent years, location-based technologies for ubiquitous environments have aimed to realize services tailored to each purpose based on information about an individual's current location. To establish such advanced location-based services, an estimation technology that can accurately recognize and predict the movements of people and objects is necessary. Although global positioning system (GPS) has already been used as a standard for outdoor positioning technology and many services have been realized, several techniques using conventional wireless sensors such as Wi-Fi, RFID, and Bluetooth have been considered for indoor positioning technology. However, conventional wireless indoor positioning is prone to the effects of noise, and the large range of estimated indoor locations makes it difficult to identify human activities precisely. We propose a method to mine user activity patterns from time-series data of user's locationss in an indoor environment using ultra-wideband (UWB) sensors. An UWB sensor is useful for indoor positioning due to its high noise immunity and measurement accuracy, however, to our knowledge, estimation and prediction of human indoor activities using UWB sensors have not yet been addressed. The proposed method consists of three steps: 1) obtaining time-series data of the user's location using a UWB sensor attached to the user, and then estimating the areas where the user has stayed; 2) associating each area of the user's stay with a nearby landmark of activity and assigning indoor activities; and 3) mining the user's activity patterns based on the user's indoor activities and their transitions. We conducted experiments to evaluate the proposed method by investigating the accuracy of estimating the user's area of stay using a UWB sensor and observing the results of activity pattern mining applied to actual laboratory members over 30-days. The results showed that the proposed method is superior to a comparison method, Time-based clustering algorithm, in estimating the stay areas precisely, and that it is possible to reveal the user's activity patterns appropriately in the actual environment.

Both group process studies and collective intelligence studies are concerned with “which of the crowds and the best members perform better.” This can be seen as a matter of democracy versus dictatorship. Having evidence of the growth potential of crowds and experts can be useful in making correct predictions and can benefit humanity. In the collective intelligence experimental paradigm, experts' or best members ability is compared with the accuracy of the crowd average. In this research (n = 620), using repeated trials of simple tasks, we compare the correct answer of a class average (index of collective intelligence) and the best member (the one whose answer was closest to the correct answer). The results indicated that, for the cognition task, collective intelligence improved to the level of the best member through repeated trials without feedback; however, it depended on the ability of the best members for the prediction task. The present study suggested that best members' superiority over crowds for the prediction task on the premise of being free from social influence. However, machine learning results suggests that the best members among us cannot be easily found beforehand because they appear through repeated trials.

A method for bandwidth enhancement of radar cross section (RCS) reduction by metasurfaces was studied. Scattering cancellation is one of common methods for reducing RCS of target scatterers. It occurs when the wave scattered by the target scatterer and the wave scattered by the canceling scatterer are the same amplitude and opposite phase. Since bandwidth of scattering cancellation is usually narrow, we proposed the bandwidth enhancement method using metasurfaces, which can control the frequency dependence of the scattering phase. We designed and fabricated a metasurface composed of a patch array on a grounded dielectric substrate. Numerical and experimental evaluations confirmed that the metasurface enhances the bandwidth of 10dB RCS reduction by 52% bandwidth ratio of the metasurface from 34% bandwidth ratio of metallic cancelling scatterers.

The combination of multiple-input multiple-output (MIMO) technology and sparse code multiple access (SCMA) can significantly enhance the spectral efficiency of future wireless communication networks. However, the receiver design for downlink MIMO-SCMA systems faces challenges in developing multi-user detection (MUD) schemes that achieve both low latency and low bit error rate (BER). The separated detection scheme in the MIMO-SCMA system involves performing MIMO detection first to obtain estimated signals, followed by SCMA decoding. We propose an enhanced separated detection scheme based on lightweight graph neural networks (GNNs). In this scheme, we raise the concept of coordinate point relay and full-category training, which allow for the substitution of the conventional message passing algorithm (MPA) in SCMA decoding with image classification techniques based on deep learning (DL). The features of the images used for training encompass crucial information such as the amplitude and phase of estimated signals, as well as channel characteristics they have encountered. Furthermore, various types of images demonstrate distinct directional trends, contributing additional features that enhance the precision of classification by GNNs. Simulation results demonstrate that the enhanced separated detection scheme outperforms existing separated and joint detection schemes in terms of computational complexity, while having a better BER performance than the joint detection schemes at high Eb/N0 (energy per bit to noise power spectral density ratio) values.

In this paper, we investigated the overfitting characteristics of nonlinear equalizers based on an artificial neural network (ANN) and the Volterra series transfer function (VSTF), which were designed to compensate for optical nonlinear waveform distortion in optical fiber communication systems. Linear waveform distortion caused by, e.g., chromatic dispersion (CD) is commonly compensated by linear equalizers using digital signal processing (DSP) in digital coherent receivers. However, mitigation of nonlinear waveform distortion is considered to be one of the next important issues. An ANN-based nonlinear equalizer is one possible candidate for solving this problem. However, the risk of overfitting of ANNs is one obstacle in using the technology in practical applications. We evaluated and compared the overfitting of ANN- and conventional VSTF-based nonlinear equalizers used to compensate for optical nonlinear distortion. The equalizers were trained on repeated random bit sequences (RRBSs), while varying the length of the bit sequences. When the number of hidden-layer units of the ANN was as large as 100 or 1000, the overfitting characteristics were comparable to those of the VSTF. However, when the number of hidden-layer units was 10, which is usually enough to compensate for optical nonlinear distortion, the overfitting was weaker than that of the VSTF. Furthermore, we confirmed that even commonly used finite impulse response (FIR) filters showed overfitting to the RRBS when the length of the RRBS was equal to or shorter than the length of the tapped delay line of the filters. Conversely, when the RRBS used for the training was sufficiently longer than the tapped delay line, the overfitting could be suppressed, even when using an ANN-based nonlinear equalizer with 10 hidden-layer units.

In this paper, we propose a noise-robust scream detection method with the aim of expanding the scream detection system, a sound-based security system. The proposed method uses enhanced screams using Wave-U-Net, which was effective as a noise reduction method for noisy screams. However, the enhanced screams showed different frequency components from clean screams and erroneously emphasized frequency components similar to scream in noise. Therefore, Wave-U-Net was applied even in the process of training Gaussian mixture models, which are discriminators. We conducted detection experiments using the proposed method in various noise environments and determined that the false acceptance rate was reduced by an average of 2.1% or more compared with the conventional method.

Approximate computing (AC) saves energy and improves performance by introducing approximation into computation in error-torrent applications. This work focuses on an AC strategy that accurately performs important computations and approximates others. In order to make AC circuits practical, we need to determine which computation is how important carefully, and thus need to appropriately approximate the redundant computation for maintaining the required computational quality. In this paper, we focus on the importance of computations at the flip-flop (FF) level and propose a novel importance evaluation methodology. The key idea of the proposed methodology is a two-step fault injection algorithm to extract the near-optimal set of redundant FFs in the circuit. In the first step, the proposed methodology performs the FI simulation for each FF and extracts the candidates of redundant FFs. Then, in the second step, the proposed methodology extracts the set of redundant FFs in a binary search manner. Thanks to the two-step strategy, the proposed algorithm reduces the complexity of architecture exploration from an exponential order to a linear order without understanding the functionality and behavior of the target application program. Experimental results show that the proposed methodology identifies the candidates of redundant FFs depending on the given constraints. In a case study of an image processing accelerator, the truncation for identified redundant FFs reduces the circuit area by 29.6% and saves power dissipation by 44.8% under the ASIC implementation while satisfying the PSNR constraint. Similarly, the dynamic power dissipation is saved by 47.2% under the FPGA implementation.

Stabilizer-based quantum secret sharing has two methods to reconstruct a quantum secret: The erasure correcting procedure and the unitary procedure. It is known that the unitary procedure has a smaller circuit width. On the other hand, it is unknown which method has smaller depth and fewer circuit gates. In this letter, it is shown that the unitary procedure has smaller depth and fewer circuit gates than the erasure correcting procedure which follows a standard framework performing measurements and unitary operators according to the measurements outcomes, when the circuits are designed for quantum secret sharing using the [[5, 1, 3]] binary stabilizer code. The evaluation can be reversed if one discovers a better circuit for the erasure correcting procedure which does not follow the standard framework.

This paper proposes the periodical successive over-relaxation (PSOR)-Jacobi algorithm for minimum mean squared error (MMSE) detection of multiple-input multiple-output (MIMO) signals. The proposed algorithm has the advantages of two conventional methods. One is the Jacobi method, which is an iterative method for solving linear equations and is suitable for parallel implementation. The Jacobi method is thus a promising candidate for high-speed simultaneous linear equation solvers for the MMSE detector. The other is the Chebyshev PSOR method, which has recently been shown to accelerate the convergence speed of linear fixed-point iterations. We compare the convergence performance of the PSOR-Jacobi algorithm with that of conventional algorithms via computer simulation. The results show that the PSOR-Jacobi algorithm achieves faster convergence without increasing computational complexity, and higher detection performance for a fixed number of iterations. This paper also proposes an efficient computation method of inverse matrices using the PSOR-Jacobi algorithm. The results of computer simulation show that the PSOR-Jacobi algorithm also accelerates the computation of inverse matrix.

Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been proposed as an attack method on image-classification systems using deep neural networks (DNNs). Physical AEs created by attaching stickers to traffic signs have been reported, which are a threat to traffic-sign-recognition DNNs used in advanced driver assistance systems. We previously proposed an attack method for generating a noise area on images by superimposing an electrical signal on the mobile industry processor interface and showed that it can generate a single adversarial mark that triggers a backdoor attack on the input image. Therefore, we propose a misclassification attack method n DNNs by creating AEs that include small perturbations to multiple places on the image by the fault injection. The perturbation position for AEs is pre-calculated in advance against the target traffic-sign image, which will be captured on future driving. With 5.2% to 5.5% of a specific image on the simulation, the perturbation that induces misclassification to the target label was calculated. As the experimental results, we confirmed that the traffic-sign-recognition DNN on a Raspberry Pi was successfully misclassified when the target traffic sign was captured with. In addition, we created robust AEs that cause misclassification of images with varying positions and size by adding a common perturbation. We propose a method to reduce the amount of robust AEs perturbation. Our results demonstrated successful misclassification of the captured image with a high attack success rate even if the position and size of the captured image are slightly changed.

Blockchain-based Internet of Things (IoT) applications require flexible, fast, and low-power hashing hardware to ensure IoT data integrity and maintain blockchain network confidentiality. However, existing hashing hardware poses challenges in achieving high performance and low power and limits flexibility to compute multiple hash functions with different message lengths. This paper introduces the flexible and energy-efficient crypto-processor (FECP) to achieve high flexibility, high speed, and low power with high hardware efficiency for blockchain-based IoT applications. To achieve these goals, three new techniques are proposed, namely the crypto arithmetic logic unit (Crypto-ALU), dual buffering extension (DBE), and local data memory (LDM) scheduler. The experiments on ASIC show that the FECP can perform various hash functions with a power consumption of 0.239-0.676W, a throughput of 10.2-3.35Gbps, energy efficiency of 4.44-14.01Gbps/W, and support up to 8916-bit message input. Compared to state-of-art works, the proposed FECP is 1.65-4.49 times, 1.73-21.19 times, and 1.48-17.58 times better in throughput, energy efficiency, and energy-delay product (EDP), respectively.

The war between cyber attackers and security analysts is gradually intensifying. Owing to the ease of obtaining and creating support tools, recent malware continues to diversify into variants and new species. This increases the burden on security analysts and hinders quick analysis. Identifying malware families is crucial for efficiently analyzing diversified malware; thus, numerous low-cost, general-purpose, deep-learning-based classification techniques have been proposed in recent years. Among these methods, malware images that represent binary features as images are often used. However, no models or architectures specific to malware classification have been proposed in previous studies. Herein, we conduct a detailed analysis of the behavior and structure of malware and focus on PE sections that capture the unique characteristics of malware. First, we validate the features of each PE section that can distinguish malware families. Then, we identify PE sections that contain adequate features to classify families. Further, we propose an ensemble learning-based classification method that combines features of highly discriminative PE sections to improve classification accuracy. The validation of two datasets confirms that the proposed method improves accuracy over the baseline, thereby emphasizing its importance.

Public key authenticated encryption with keyword search (PAEKS) has been proposed, where a sender's secret key is required for encryption, and a trapdoor is associated with not only a keyword but also the sender. This setting allows us to prevent information leakage of keyword from trapdoors. Liu et al. (ASIACCS 2022) proposed a generic construction of PAEKS based on word-independent smooth projective hash functions (SPHFs) and PEKS. In this paper, we propose a new generic construction of PAEKS, which is more efficient than Liu et al.'s in the sense that we only use one SPHF, but Liu et al. used two SPHFs. In addition, for consistency we considered a security model that is stronger than Liu et al.'s. Briefly, Liu et al. considered only keywords even though a trapdoor is associated with not only a keyword but also a sender. Thus, a trapdoor associated with a sender should not work against ciphertexts generated by the secret key of another sender, even if the same keyword is associated. That is, in the previous definitions, there is room for a ciphertext to be searchable even though the sender was not specified when the trapdoor is generated, that violates the authenticity of PAKES. Our consistency definition considers a multi-sender setting and captures this case. In addition, for indistinguishability against chosen keyword attack (IND-CKA) and indistinguishability against inside keyword guessing attack (IND-IKGA), we use a stronger security model defined by Qin et al. (ProvSec 2021), where an adversary is allowed to query challenge keywords to the encryption and trapdoor oracles. We also highlight several issues associated with the Liu et al. construction in terms of hash functions, e.g., their construction does not satisfy the consistency that they claimed to hold.

We propose how to homomorphically evaluate arbitrary univariate and bivariate integer functions such as division. A prior work proposed by Okada et al. (WISTP'18) uses polynomial evaluations such that the scheme is still compatible with the SIMD operations in BFV and BGV schemes, and is implemented with the input domain ℤ257. However, the scheme of Okada et al. requires the quadratic numbers of plaintext-ciphertext multiplications and ciphertext-ciphertext additions in the input domain size, and although these operations are more lightweight than the ciphertext-ciphertext multiplication, the quadratic complexity makes handling larger inputs quite inefficient. In this work, first we improve the prior work and also propose a new approach that exploits the packing method to handle the larger input domain size instead of enabling the SIMD operation, thus making it possible to work with the larger input domain size, e.g., ℤ215 in a reasonably efficient way. In addition, we show how to slightly extend the input domain size to ℤ216 with a relatively moderate overhead. Further we show another approach to handling the larger input domain size by using two ciphertexts to encrypt one integer plaintext and applying our techniques for uni/bivariate function evaluation. We implement the prior work of Okada et al., our improved version of Okada et al., and our new scheme in PALISADE with the input domain ℤ215, and confirm that the estimated run-times of the prior work and our improved version of the prior work are still about 117 days and 59 days respectively while our new scheme can be computed in 307 seconds.