The goal of Acoustic Scene Classification (ASC) is to simulate human analysis of the surrounding environment and make accurate decisions promptly. Extracting useful information from audio signals in real-world scenarios is challenging and can lead to suboptimal performance in acoustic scene classification, especially in environments with relatively homogeneous backgrounds. To address this problem, we model the sobering-up process of “drunkards” in real-life and the guiding behavior of normal people, and construct a high-precision lightweight model implementation methodology called the “drunkard methodology”. The core idea includes three parts: (1) designing a special feature transformation module based on the different mechanisms of information perception between drunkards and ordinary people, to simulate the process of gradually sobering up and the changes in feature perception ability; (2) studying a lightweight “drunken” model that matches the normal model's perception processing process. The model uses a multi-scale class residual block structure and can obtain finer feature representations by fusing information extracted at different scales; (3) introducing a guiding and fusion module of the conventional model to the “drunken” model to speed up the sobering-up process and achieve iterative optimization and accuracy improvement. Evaluation results on the official dataset of DCASE2022 Task1 demonstrate that our baseline system achieves 40.4% accuracy and 2.284 loss under the condition of 442.67K parameters and 19.40M MAC (multiply-accumulate operations). After adopting the “drunkard” mechanism, the accuracy is improved to 45.2%, and the loss is reduced by 0.634 under the condition of 551.89K parameters and 23.6M MAC.

In this paper, we present a novel photometric compensation network named CASEformer, which is built upon the Swin module. For the first time, we combine coordinate attention and channel attention mechanisms to extract rich features from input images. Employing a multi-level encoder-decoder architecture with skip connections, we establish multiscale interactions between projection surfaces and projection images, achieving precise inference and compensation. Furthermore, through an attention fusion module, which simultaneously leverages both coordinate and channel information, we enhance the global context of feature maps while preserving enhanced texture coordinate details. The experimental results demonstrate the superior compensation effectiveness of our approach compared to the current state-of-the-art methods. Additionally, we propose a method for multi-surface projection compensation, further enriching our contributions.

This paper proposes the new series highly integrated intelligent power module (IPM), which is developed to provide a ultra-compact, high performance and reliable motor drive system. Details of the key design technologies of the IPM is given and practical application issues such as electrical characteristics, system operation performance and power dissipation are discussed. Layout placement and routing have been optimized in order to reduce and balance the parasitic impedances. By implementing an innovative direct bonding copper (DBC) ceramic substrate, which can effectively dissipate heat, the IPM delivers a fully integrated power stages including two three-phase inverters, power factor correction (PFC) and rectifier in an ultra-compact 75.5mm × 30mm package, offering up to a 17.3 percent smaller space than traditional motor drive scheme.

In this paper, we propose improved Generative Adversarial Networks with attention module in Generator, which can enhance the effectiveness of Generator. Furthermore, recent work has shown that Generator conditioning affects GAN performance. Leveraging this insight, we explored the effect of different normalization (spectral normalization, instance normalization) on Generator and Discriminator. Moreover, an enhanced loss function called Wasserstein Divergence distance, can alleviate the problem of difficult to train module in practice.

At manufacturing sites, mass customization is expanding along with the increasing variety of customer needs. This situation leads to complications in production planning for the factory manager, and production plans are likely to change suddenly at the manufacturing site. Because such sudden fluctuations in production often occur, it is particularly difficult to optimize the parts supply operations in these production processes. As a solution to such problems, Industry 4.0 has expanded to promote the use of digital technologies at manufacturing sites; however, these solutions can be expensive and time-consuming to introduce. Therefore, not all factory managers are favorable toward introducing digital technology. In this study, we propose a method to support parts supply operations that decreases work stagnation and fluctuation without relying on the experience of workers who supply parts in the various production processes. Furthermore, we constructed a system that is inexpensive and easy to introduce using both LPWA and BLE communications. The purpose of the system is to level out work in in-process logistics. In an experiment, the proposed method was introduced to a manufacturing site, and we compared how the workload of the site's workers changed. The experimental results show that the proposed method is effective for workload leveling in parts supply operations.

Salient ship detection plays an important role in ensuring the safety of maritime transportation and navigation. However, due to the influence of waves, special weather, and illumination on the sea, existing saliency methods are still unable to achieve effective ship detection in a complex marine environment. To solve the problem, this paper proposed a novel saliency method based on an attention nested U-Structure (AU2Net). First, to make up for the shortcomings of the U-shaped structure, the pyramid pooling module (PPM) and global guidance paths (GGPs) are designed to guide the restoration of feature information. Then, the attention modules are added to the nested U-shaped structure to further refine the target characteristics. Ultimately, multi-level features and global context features are integrated through the feature aggregation module (FAM) to improve the ability to locate targets. Experiment results demonstrate that the proposed method could have at most 36.75% improvement in F-measure (Favg) compared to the other state-of-the-art methods.

We propose a target detection network (RMF-Net) based on the multi-scale strategy to solve the problems of large differences in the detection scale and mutual occlusion, which result in inaccurate locations. A multi-layer feature fusion module and multi-expansion dilated convolution pyramid module were designed based on the ResNet-101 residual network. The ability of the network to express the multi-scale features of the target could be improved by combining the shallow and deep features of the target and expanding the receptive field of the network. Moreover, RoI Align pooling was introduced to reduce the low accuracy of the anchor frame caused by multiple quantizations for improved positioning accuracy. Finally, an AD-IoU loss function was designed, which can adaptively optimise the distance between the prediction box and real box by comprehensively considering the overlap rate, centre distance, and aspect ratio between the boxes and can improve the detection accuracy of the occlusion target. Ablation experiments on the RMF-Net model verified the effectiveness of each factor in improving the network detection accuracy. Comparative experiments were conducted on the Pascal VOC2007 and Pascal VOC2012 datasets with various target detection algorithms based on convolutional neural networks. The results demonstrated that RMF-Net exhibited strong scale adaptability at different occlusion rates. The detection accuracy reached 80.4% and 78.5% respectively.

Fine-grained image classification is a difficult problem, and previous studies mainly overcome this problem by locating multiple discriminative regions in different scales and then aggregating complementary information explored from the located regions. However, locating discriminative regions introduces heavy overhead and is not suitable for real-world application. In this paper, we propose the recursive multi-scale channel-spatial attention module (RMCSAM) for addressing this problem. Following the experience of previous research on fine-grained image classification, RMCSAM explores multi-scale attentional information. However, the attentional information is explored by recursively refining the deep feature maps of a convolutional neural network (CNN) to better correspond to multi-scale channel-wise and spatial-wise attention, instead of localizing attention regions. In this way, RMCSAM provides a lightweight module that can be inserted into standard CNNs. Experimental results show that RMCSAM can improve the classification accuracy and attention capturing ability over baselines. Also, RMCSAM performs better than other state-of-the-art attention modules in fine-grained image classification, and is complementary to some state-of-the-art approaches for fine-grained image classification. Code is available at https://github.com/Dichao-Liu/Recursive-Multi-Scale-Channel-Spatial-Attention-Module.

This paper demonstrates 300GHz terahertz wireless communication using CMOS transmitter (TX) and receiver (RX) modules targeting sixth-generation (6G). To extend communication distance, CMOS modules with WR-3.4 waveguide interface and a high-gain antenna of 40dBi Cassegrain antenna are designed, achieving 36Gbps throughput at a 1m communication distance. Besides, in order to support orthogonal frequency-division multiplexing (OFDM), a self-heterodyne architecture is introduced, which effectively cancels the phase noise in multi-carrier modulation. As a proof-of-concept (PoC), the paper successfully demonstrates real-time video transfer at a 10m communication distance using fifth-generation (5G) based OFDM at the 300GHz frequency band.

Exploring the structural information as prior to facial images is a key issue of face super-resolution (SR). Although deep convolutional neural networks (CNNs) own powerful representation ability, how to accurately use facial structural information remains challenges. In this paper, we proposed a new residual fusion network to utilize the multi-scale structural information for face SR. Different from the existing methods of increasing network depth, the bottleneck attention module is introduced to extract fine facial structural features by exploring correlation from feature maps. Finally, hierarchical scales of structural information is fused for generating a high-resolution (HR) facial image. Experimental results show the proposed network outperforms some existing state-of-the-art CNNs based face SR algorithms.

As characterizing videos simultaneously from spatial and temporal cues have been shown crucial for video processing, with the shortage of temporal information of soft assignment, the vector of locally aggregated descriptor (VLAD) should be considered as a suboptimal framework for learning the spatio-temporal video representation. With the development of attention mechanisms in natural language processing, in this work, we present a novel model with VLAD following spatio-temporal self-attention operations, named spatio-temporal self-attention weighted VLAD (ST-SAWVLAD). In particular, sequential convolutional feature maps extracted from two modalities i.e., RGB and Flow are receptively fed into the self-attention module to learn soft spatio-temporal assignments parameters, which enabling aggregate not only detailed spatial information but also fine motion information from successive video frames. In experiments, we evaluate ST-SAWVLAD by using competitive action recognition datasets, UCF101 and HMDB51, the results shcoutstanding performance. The source code is available at:https://github.com/badstones/st-sawvlad.

Recently, the ECG-based diagnosis system based on wearable devices has attracted more and more attention of researchers. Existing studies have achieved high classification accuracy by using deep neural networks (DNNs), but there are still some problems, such as: imprecise heart beat segmentation, inadequate use of medical knowledge, the same treatment of features with different importance. To address these problems, this paper: 1) proposes an adaptive segmenting-reshaping method to acquire abundant useful samples; 2) builds a set of hand-crafted features and deep features on the inner-beat, beat and inter-beat scale by integrating enough medical knowledge. 3) introduced a modified channel attention module (CAM) to augment the significant channels in deep features. Following the Association for Advancement of Medical Instrumentation (AAMI) recommendation, we classified the dataset into four classes and validated our algorithm on the MIT-BIH database. Experiments show that the accuracy of our model reaches 96.94%, a 3.71% increase over that of a state-of-the-art alternative.

Smaller antenna structures for long-range radar transmitters and receivers operating in the 77-GHz band for automotive application have been achieved by using antennas with a horn, lens, and microstrip antenna. The transmitter (Tx) antenna height was reduced while keeping the antenna gain high and the antenna substrate small by developing an antenna structure composed of two differential horn and lens antennas in which the diameter and focus distance of the lenses were half those in the previous design. The microstrip antennas are directly connected to the differential outputs of a monolithic microwave integrated circuit. A Tx antenna fabricated using commercially available materials was 14mm high and had an output-aperture of 18×44mm. It achieved an antenna gain of 23.5dBi. The antenna substrate must be at least 96mm2. The antenna had a flat beam with half-power elevation and azimuth beamwidths of 4.5° and 21°, respectively. A receiver (Rx) antenna array composed of four sets of horn and lens antennas with an output-aperture of 9×22mm and a two-by-two array configuration was fabricated for application in a newly proposed small front-end module with azimuth direction of arrival (DOA) estimation. The Rx antenna array had an antenna coupling of less than -31dB in the 77-GHz band, which is small enough for DOA estimation by frequency-modulated continuous wave radar receivers even though the four antennas are arranged without any separation between their output-apertures.

Silica-LiNbO3 (LN) hybrid modulators have a hybrid configuration of versatile passive silica-based planar lightwave circuits (PLCs) and simple LN phase modulators arrays. By combining the advantages the two components, these hybrid modulators offer large-scale, highly-functionality modulators with low losses for advanced modulation formats. However, the reliability evaluation necessary to implement them in real transmissions has not been reported yet. In terms of reliability characteristics, there are issues originating from the difference in thermal expansion coefficients between silica PLC and LN. To resolve these issues, we propose design guidelines for hybrid modulators to mitigate the degradation induced by the thermal expansion difference. We fabricated several tens of silica-LN dual polarization quadrature phase shift keying (DP-QPSK) modulators based on the design guidelines and evaluated their reliability. The experiment results show that the modules have no degradation after a reliability test based on GR-468, which confirms the validity of the design guidelines for highly reliable silica-LN hybrid modulators. We can apply the guidelines for hybrid modules that realize heterogeneous device integration using materials with different coefficients of thermal expansion.

We propose waveguide to microstrip line transitions for automotive millimeter wave radar modules. The transitions perpendicularly connect one waveguide and one or two microstrip lines. The configuration is simple because it consists of a waveguide and a dielectric substrate with copper foils. Additionally the transitions do not need via holes on the substrate. It leads to lower costs and improved reliability. We have already proposed a via-less transition by using multi-stage impedance transformers. The impedance transformers are used for suppressing undesirable radiation from the transition as well as impedance matching. In this paper, we propose a new transition with the microstrip lines on the long axis of the waveguide while most transitions place the microstrip lines on the minor axis (electric field direction) of the waveguide. Though our transition uses bend structures of microstrip lines, which basically cause radiation, our optimized configuration can keep small radiation. We also design a transition with a single microstrip line. The proposed transition with 2 microstrip lines can be modified to the 1 microstrip line version with minimum radiation loss. Electromagnetic simulations confirm the small radiation levels expected. Additionally we fabricate the transitions with back to back structure and determine the transmission and radiation performance. We also fabricates the transition for a patch array antenna. We confirm that the undesirable radiation from the proposed transition is small and the radiation pattern of the array antenna is not worsen by the transition.

Advanced metering infrastructure (AMI) is a kind of wireless sensor network that provides two-way communication between smart meters and city utilities in the neighborhood area of the smart grid. And the routing protocol for low-power and lossy network (RPL) is being considered for use in AMI networks. However, there still exist several problems that need to be solved, especially with respect to QoS guarantees. To address these problems, an improved algorithm of RPL based on triangle module operator named as TMO is proposed. TMO comprehensively evaluates routing metrics: end-to-end delay, number of hops, expected transmission count, node remaining energy, and child node count. Moreover, TMO uses triangle module operator to fuse membership functions of these routing metrics. Then, the node with minimum rank value will be selected as preferred parent (the next hop). Consequently, the QoS of RPL-based AMI networks can be guaranteed effectively. Simulation results show that TMO offers a great improvement over several the most popular schemes for RPL like ETXOF, OF-FL and additive composition metric manners in terms of network lifetime, average end-to-end delay, average packet loss ratio, average hop count from nodes to root, etc.

This paper presents a SiC intelligent power module (IPM) which features low power loss. It is designed specifically for high performance low power motor drive applications including fans, refrigerator and air conditioner compressor drives, where energy efficiency is a major concern. The IPM utilizes 600 V planar-type SiC metal oxide semiconductor field effect transistors (MOSFETs) as the power switching devices to deliver immensely low conduction and switching losses. Moreover, 600 V SiC schottky barrier diodes (SBDs) are adopted as the freewheeling diodes. In comparison with conventional silicon fast recovery diodes (FRDs), SiC SBDs exhibit practically no reverse recovery loss and can further diminish the power loss of the IPM. Besides, combined with these SiC power devices the proposed IPM is able to operate at a higher temperature up to 175°C while maintaining very low leakage current. Experimental results indicate that the power loss of the proposed IPM is between 2.2∼17 W at different compressor frequencies from 10 to 70 Hz, which can realize up to 32%∼53% improvement when compared to state-of-the-art conventional Si-based insulated gate bipolar transistor (IGBT) IPM.

Most modern field programmable gate arrays (FPGAs) use a lookup table (LUT) as their basic logic cell. LUT resource requirements increase as O(2k) with an increasing number of inputs, k, so LUTs with more than six inputs negatively affect the overall FPGA performance. To address this problem, we propose a scalable logic module (SLM), which is a logic cell with less configuration memory, by using partial functions of the Shannon expansion for logics that appear frequently. In addition, we develop a technology mapping tool for SLM. The key feature of our tool is to combine a function decomposition process with traditional cut-based mapping. Experimental results show that an SLM-based FPGA with our mapping method uses much fewer configuration memory bits and has a smaller area than conventional LUT-based FPGAs.

Although SDN provides desirable characteristics such as the manageability, flexibility and extensibility of the networks, it has a considerable disadvantage in its reliability due to its centralized architecture. To protect SDN-enabled networks under large-scale, unexpected link failures, we propose ResilientFlow that deploys distributed modules called Control Channel Maintenance Module (CCMM) for every switch and controllers. The CCMMs makes switches able to maintain their own control channels, which are core and fundamental part of SDN. In this paper, we design, implement, and evaluate the ResilientFlow.

TPM-embedded devices can be used as authentication tokens by issuing certificates to signing keys generated by TPM. TPM generates Attestation Identity Key (AIK) and Binding Key (BK) that are RSA keys. AIK is used to identify TPM. BK is used to encrypt data so that specific TPM can decrypt it. TPM can use for device authentication by linking a SSL client certificate to TPM. This paper proposes a method of an AIK certificate issuance with OpenID and a method of the SSL client certificate issuance to specific TPM using AIK and BK. In addition, the paper shows how to implement device authentication system using the SSL client certificate related to TPM.