Convolutional neural network (CNN) is widely used in acoustic scene classification (ASC) tasks. In most cases, local convolution is utilized to gather time-frequency information between spectrum nodes. It is challenging to adequately express the non-local link between frequency domains in a finite convolution region. In this paper, we propose a dual-path convolutional neural network based on band interaction block (DCNN-bi) for ASC, with mel-spectrogram as the model’s input. We build two parallel CNN paths to learn the high-frequency and low-frequency components of the input feature. Additionally, we have created three band interaction blocks (bi-blocks) to explore the pertinent nodes between various frequency bands, which are connected between two paths. Combining the time-frequency information from two paths, the bi-blocks with three distinct designs acquire non-local information and send it back to the respective paths. The experimental results indicate that the utilization of the bi-block has the potential to improve the initial performance of the CNN substantially. Specifically, when applied to the DCASE 2018 and DCASE 2020 datasets, the CNN exhibited performance improvements of 1.79% and 3.06%, respectively.

To achieve precise measurement without landing, the high-mobility vehicle-mounted theodolite needs to be leveled quickly with high precision and ensure sufficient support stability before work. After the measurement, it is also necessary to ensure that the high-mobility vehicle-mounted theodolite can be quickly withdrawn. Therefore, this paper proposes a hierarchical automatic leveling strategy and establishes a two-stage electromechanical automatic leveling mechanism model. Using coarse leveling of the first-stage automatic leveling mechanism and fine leveling of the second-stage automatic leveling mechanism, the model realizes high-precision and fast leveling of the vehicle-mounted theodolites. Then, the leveling control method based on repeated positioning is proposed for the first-stage automatic leveling mechanism. To realize the rapid withdrawal for high-mobility vehicle-mounted theodolites, the method ensures the coincidence of spatial movement paths when the structural parts are unfolded and withdrawn. Next, the leg static balance equation is constructed in the leveling state, and the support force detection method is discussed in realizing the stable support for vehicle-mounted theodolites. Furthermore, a mathematical model for “false leg” detection is established furtherly, and a “false leg” detection scheme based on the support force detection method is analyzed to significantly improve the support stability of vehicle-mounted theodolites. Finally, an experimental platform is constructed to perform the performance test for automatic leveling mechanisms. The experimental results show that the leveling accuracy of established two-stage electromechanical automatic leveling mechanism can reach 3.6″, and the leveling time is no more than 2 mins. The maximum support force error of the support force detection method is less than 15%, and the average support force error is less than 10%. In contrast, the maximum support force error of the drive motor torque detection method reaches 80.12%, and its leg support stability is much less than the support force detection method. The model and analysis method proposed in this paper can also be used for vehicle-mounted radar, vehicle-mounted laser measurement devices, vehicle-mounted artillery launchers and other types of vehicle-mounted equipment with high-precision and high-mobility working requirements.

This paper proposes a scheme for reducing pilot interference in cell-free massive multiple-input multiple-output (MIMO) systems through scalable access point (AP) selection and efficient pilot allocation using the Grey Wolf Optimizer (GWO). Specifically, we introduce a bidirectional large-scale fading-based (B-LSFB) AP selection method that builds high-quality connections benefiting both APs and UEs. Then, we limit the number of UEs that each AP can serve and encourage competition among UEs to improve the scalability of this approach. Additionally, we propose a grey wolf optimization based pilot allocation (GWOPA) scheme to minimize pilot contamination. Specifically, we first define a fitness function to quantify the level of pilot interference between UEs, and then construct dynamic interference relationships between any UE and its serving AP sets using a weighted fitness function to minimize pilot interference. The simulation results shows that the B-LSFB strategy achieves scalability with performance similar to large-scale fading-based (LSFB) AP selection. Furthermore, the grey wolf optimization-based pilot allocation scheme significantly improves per-user net throughput with low complexity compared to four existing schemes.

Boolean functions with a few Walsh spectral values have important applications in sequence ciphers and coding theory. In this paper, we first construct a class of Boolean functions with at most five-valued Walsh spectra by using the secondary construction of Boolean functions, in particular, plateaued functions are included. Then, we construct three classes of Boolean functions with five-valued Walsh spectra using Kasami functions and investigate the Walsh spectrum distributions of the new functions. Finally, three classes of minimal linear codes with five-weights are obtained, which can be used to design secret sharing scheme with good access structures.

The purpose of Facial Beauty Prediction (FBP) is to automatically assess facial attractiveness based on human aesthetics. Most neural network-based prediction methods do not consider the ranking information in the task. For scoring tasks like facial beauty prediction, there is abundant ranking information both between images and within images. Reasonable utilization of these information during training can greatly improve the performance of the model. In this paper, we propose a novel end-to-end Convolutional Neural Network (CNN) model based on ranking information of images, incorporating a Rank Module and an Adaptive Weight Module. We also design pairwise ranking loss functions to fully leverage the ranking information of images. Considering training efficiency and model inference capability, we choose ResNet-50 as the backbone network. We conduct experiments on the SCUT-FBP5500 dataset and the results show that our model achieves a new state-of-the-art performance. Furthermore, ablation experiments show that our approach greatly contributes to improving the model performance. Finally, the Rank Module with the corresponding ranking loss is plug-and-play and can be extended to any CNN model and any task with ranking information. Code is available at https://github.com/nehcoah/Rank-Info-Net.

Modern high-level programming languages have a wide variety of grammar and can implement the required functionality in different ways. The authors believe that a large amount of code that implements the same functionality in different ways exists even in open source software where the source code is publicly available, and that by collecting such code, a useful data set can be constructed for various studies in software engineering. In this study, we construct a dataset of pairs of Java methods that have the same functionality but different structures from approximately 314 million lines of source code. To construct this dataset, the authors used an automated test generation technique, EvoSuite. Test cases generated by automated test generation techniques have the property that the test cases always succeed. In constructing the dataset, using this property, test cases generated from two methods were executed against each other to automatically determine whether the behavior of the two methods is the same to some extent. Pairs of methods for which all test cases succeeded in cross-running test cases are manually investigated to be functionally equivalent. This paper also reports the results of an accuracy evaluation of code clone detection tools using the constructed dataset. The purpose of this evaluation is assessing how accurately code clone detection tools could find the functionally equivalent methods, not assessing the accuracy of detecting ordinary clones. The constructed dataset is available at github (https://github.com/YoshikiHigo/FEMPDataset).

The magnetic field resolution of the tunnel magneto-resistive (TMR) sensors has been improving and it reaches below 1.0 pT/Hz0.5 at low frequency. The real-time measurement of the magnetocardiography (MCG) and the measurement of the magnetoencephalography (MEG) have been demonstrated by developed TMR sensors. Although the MCG and MEG have been applied to diagnosis of diseases, the conventional MCG/MEG system using superconducting quantum interference devices (SQUIDs) cannot measure the signal by touching the body, the body must be fixed, and maintenance costs are huge. The MCG/MEG system with TMR sensors operating at room temperature have the potential to solve these problems. In addition, it has the great advantage that it does not require a special magnetic shielded room. Further developments are expected to progress to maximize these unique features of TMR sensors.

The subject must be motionless for conventional radar-based non-contact vital signs measurements. Additionally, the measurement range is limited by the design of the radar module itself. Although the accuracy of measurements has been improving, the prospects for their application could have been faster to develop. This paper proposed a novel radar-based adaptive tracking method for measuring the heart rate of the moving monitored person. The radar module is fixed on a circular plate and driven by stepping motors to rotate it. In order to protect the user’s privacy, the method uses radar signal processing to detect the subject’s position to control a stepping motor that adjusts the radar’s measurement range. The results of the fixed-route experiments revealed that when the subject was moving at a speed of 0.5 m/s, the mean values of RMSE for heart rate measurements were all below 2.85 beat per minute (bpm), and when moving at a speed of 1 m/s, they were all below 4.05 bpm. When subjects walked at random routes and speeds, the RMSE of the measurements were all below 6.85 bpm, with a mean value of 4.35 bpm. The average RR interval time of the reconstructed heartbeat signal was highly correlated with the electrocardiography (ECG) data, with a correlation coefficient of 0.9905. In addition, this study not only evaluated the potential effect of arm swing (more normal walking motion) on heart rate measurement but also demonstrated the ability of the proposed method to measure heart rate in a multiple-people scenario.

This letter studies the secrecy outage probability (SOP) and the secrecy diversity order of Alamouti STBC with decision feedback (DF) detection over the time-selective fading channels. For given temporal correlations, we have derived the exact SOPs and their asymptotic approximations for all possible combinations of detection schemes including joint maximum likehood (JML), zero-forcing (ZF), and DF at Bob and Eve. We reveal that the SOP is mainly influenced by the detection scheme of the legitimate receiver rather than eavesdropper and the achievable secrecy diversity order converges to two and one for JML only at Bob (i.e., JML-JML/ZF/DF) and for the other cases (i.e., ZF-JML/ZF/DF, DF-JML/ZF/DF), respectively. Here, p-q combination pair indicates that Bob and Eve adopt the detection method p ∈ {JML, ZF, DF} and q ∈ {JML, ZF, DF}, respectively.

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.

In an open-bid auction, a bidder can know the budgets of other bidders. Thus, a sealed-bid auction that hides bidding prices is desirable. However, in previous sealed-bid auction protocols, it has been difficult to provide a “fund binding” property, which would guarantee that a bidder has funds more than or equal to the bidding price and that the funds are forcibly withdrawn when the bidder wins. Thus, such protocols are vulnerable to a false bidding. As a solution, many protocols employ a simple deposit method in which each bidder sends a deposit to a smart contract, which is greater than or equal to the bidding price, before the bidding phase. However, this deposit reveals the maximum bidding price, and it is preferable to hide this information. In this paper, we propose a sealed-bid auction protocol that provides a fund binding property. Our protocol not only hides the bidding price and a maximum bidding price, but also provides a fund binding property, simultaneously. For hiding the maximum bidding price, we pay attention to the fact that usual Ethereum transactions and transactions for sending funds to a one-time address have the same transaction structure, and it seems that they are indistinguishable. We discuss how much bidding transactions are hidden. We also employ DECO (Zhang et al., CCS 2020) that proves the validity of the data to a verifier in which the data are taken from a source without showing the data itself. Finally, we give our implementation which shows transaction fees required and compare it to a sealed-bid auction protocol employing the simple deposit method.

An optical mode multiplexer was newly designed and fabricated using LiNbO3 waveguides. The multiplexer consists of an asymmetric directional coupler capable of achieving the phase-matching condition by the voltage adjustment. The mode conversion efficiency between TM0 and TM1 modes was quantitatively measured to be 0.86 at maximum.

To reduce the common mode voltage (CMV), suppress the CMV spikes, and improve the steady-state performance, a simplified reactive torque model predictive control (RT-MPC) for induction motors (IMs) is proposed. The proposed prediction model can effectively reduce the complexity of the control algorithm with the direct torque control (DTC) based voltage vector (VV) preselection approach. In addition, the proposed CMV suppression strategy can restrict the CMV within ±Vdc/6, and does not require the exclusion of non-adjacent non-opposite VVs, thus resulting in the system showing good steady-state performance. The effectiveness of the proposed design has been tested and verified by the practical experiment. The proposed algorithm can reduce the execution time by an average of 26.33% compared to the major competitors.

In this study, the edge diffraction of a TM-polarized electromagnetic plane wave by two-dimensional dielectric wedges has been analyzed. An asymptotic solution for the radiation field has been derived from equivalent electric and magnetic currents which can be determined by the geometrical optics (GO) rays. This method may be regarded as an extended version of physical optics (PO). The diffracted field has been represented in terms of cotangent functions whose singularity behaviors are closely related to GO shadow boundaries. Numerical calculations are performed to compare the results with those by other reference solutions, such as the hidden rays of diffraction (HRD) and a numerical finite-difference time-domain (FDTD) simulation. Comparisons of the diffraction effect among these results have been made to propose additional lateral waves in the denser media.

With the development of drone technology, concerns have arisen about the possibility of drones being equipped with threat payloads for terrorism and other crimes. A drone detection system that can detect drones carrying payloads is needed. A drone’s propeller rotation frequency increases with payload weight. Therefore, a method for estimating propeller rotation frequency will effectively detect the presence or absence of a payload and its weight. In this paper, we propose a method for classifying the payload weight of a drone by estimating its propeller rotation frequency from radar images obtained using a millimeter-wave fast-chirp-modulation multiple-input and multiple-output (MIMO) radar. For each drone model, the proposed method requires a pre-prepared reference dataset that establishes the relationships between the payload weight and propeller rotation frequency. Two experimental measurement cases were conducted to investigate the effectiveness of our proposal. In case 1, we assessed four drones (DJI Matrice 600, DJI Phantom 3, DJI Mavic Pro, and DJI Mavic Mini) to determine whether the propeller rotation frequency of any drone could be correctly estimated. In case 2, experiments were conducted on a hovering Phantom 3 drone with several payloads in a stable position for calculating the accuracy of the payload weight classification. The experimental results indicated that the proposed method could estimate the propeller rotation frequency of any drone and classify payloads in a 250 g step with high accuracy.

Cloud gaming systems allow users to play games that require high-performance computational capability on their mobile devices at any location. However, playing games through cloud gaming systems increases the Round-Trip Time (RTT) due to increased network delay. To simulate a local gaming experience for cloud users, we must minimize RTTs, which include network delays. The speculative video transmission pre-generates and encodes video frames corresponding to all possible user inputs and sends them to the user before the user’s input. The speculative video transmission mitigates the network, whereas a simple solution significantly increases the video traffic. This paper proposes tile-wise delta detection for traffic reduction of speculative video transmission. More specifically, the proposed method determines a reference video frame from the generated video frames and divides the reference video frame into multiple tiles. We calculate the similarity between each tile of the reference video frame and other video frames based on a hash function. Based on calculated similarity, we determine redundant tiles and do not transmit them to reduce traffic volume in minimal processing time without implementing a high compression ratio video compression technique. Evaluations using commercial games showed that the proposed method reduced 40-50% in traffic volume when the SSIM index was around 0.98 in certain genres, compared with the speculative video transmission method. Furthermore, to evaluate the feasibility of the proposed method, we investigated the effectiveness of network delay reduction with existing computational capability and the requirements in the future. As a result, we found that the proposed scheme may mitigate network delay by one to two frames, even with existing computational capability under limited conditions.

In software defined network (SDN), packet processing is commonly implemented using match-action model, where packets are processed based on matched actions in match action table. Due to the limited FPGA on-board resources, it is an important challenge to achieve large-scale high throughput based on exact matching (EM), while solving hash conflicts and out-of-order problems. To address these issues, this study proposed an FPGA-based EM table that leverages shared rule tables across multiple pipelines to eliminate memory replication and enhance overall throughput. An out-of-order reordering function is used to ensure packet sequencing within the pipelines. Moreover, to handle collisions and increase load factor of hash table, multiple hash table blocks are combined and an auxiliary CAM-based EM table is integrated in each pipeline. To the best of our knowledge, this is the first time that the proposed design considers the recovery of out-of-order operations in multi-channel EM table for high-speed network packets processing application. Furthermore, it is implemented on Xilinx Alveo U250 field programmable gate arrays, which has a million rules and achieves a processing speed of 200 million operations per second, theoretically enabling throughput exceeding 100 Gbps for 64-Byte size packets.

The detection of hardware trojans has been extensively studied in the past. In this article, we propose a side-channel analysis technique that uses a wrapper-based feature selection technique for hardware trojan detection. The whale optimization algorithm is modified to carefully extract the best feature subset. The aim of the proposed technique is multiobjective: improve the accuracy and minimize the number of features. The power consumption traces measured from AES-128 trojan circuits are used as features in this experiment. The stabilizing property of the feature selection method helps to bring a mutual trade-off between the precision and recall parameters thereby minimizing the number of false negatives. The proposed hardware trojan detection scheme produces a maximum of 10.3% improvement in accuracy and reduction up to a single feature by employing the modified whale optimization technique. Thus the evaluation results conducted on various trust-hub cryptographic benchmark circuits prove to be efficient from the existing state-of-art methods.

Automatic detection of prohibited items is vital in helping security staff be more efficient while improving the public safety index. However, prohibited item detection within X-ray security inspection images is limited by various factors, including the imbalance distribution of categories, diversity of prohibited item scales, and overlap between items. In this paper, we propose to leverage the Poisson blending algorithm with the Canny edge operator to alleviate the imbalance distribution of categories maximally in the X-ray images dataset. Based on this, we improve the cascade network to deal with the other two difficulties. To address the prohibited scale diversity problem, we propose the Re-BiFPN feature fusion method, which includes a coordinate attention atrous spatial pyramid pooling (CA-ASPP) module and a recursive connection. The CA-ASPP module can implicitly extract direction-aware and position-aware information from the feature map. The recursive connection feeds the CA-ASPP module processed multi-scale feature map to the bottom-up backbone layer for further multi-scale feature extraction. In addition, a Rep-CIoU loss function is designed to address the overlapping problem in X-ray images. Extensive experimental results demonstrate that our method can successfully identify ten types of prohibited items, such as Knives, Scissors, Pressure, etc. and achieves 83.4% of mAP, which is 3.8% superior to the original cascade network. Moreover, our method outperforms other mainstream methods by a significant margin.

Self-dithered digital delta-sigma modulators (DDSMs) are commonly used in fractional-N frequency synthesizers due to their ability to eliminate unwanted spurs from the synthesizer’s spectra without requiring additional hardware. However, when operating with a low-bit input, self-dithered DDSMs can still suffer from spurious tones at certain inputs. In this paper, we propose a self-dithered MASH 1-1-1-1 structure to mitigate the spur issue in the self-dithered MASH DDSMs. The proposed self-dithered MASH 1-1-1-1 suppresses the spurs with shaped dithering and achieves 4th order noise shaping.