We proposed a new method for evaluating the deterioration of messenger wires by using terahertz waves. We use terahertz time-domain spectroscopy to measure several twisted wire samples with different levels of deterioration. We find that each twisted wire sample had a different distribution of reflection intensity which was due to the wires' twist structure. We show that it is possible to assess the degradation from the straight lines present in the reflection intensity distribution image. Furthermore, it was confirmed that our method can be applied to wire covered with resin.

In video-text retrieval task, mainstream framework consists of three parts: video encoder, text encoder and similarity calculation. MMT (Multi-modal Transformer) achieves remarkable performance for this task, however, it faces the problem of insufficient training dataset. In this paper, an efficient multimodal aggregation network for video-text retrieval is proposed. Different from the prior work using MMT to fuse video features, the NetVLAD is introduced in the proposed network. It has fewer parameters and is feasible for training with small datasets. In addition, since the function of CLIP (Contrastive Language-Image Pre-training) can be considered as learning language models from visual supervision, it is introduced as text encoder in the proposed network to avoid overfitting. Meanwhile, in order to make full use of the pre-training model, a two-step training scheme is designed. Experiments show that the proposed model achieves competitive results compared with the latest work.

This letter focuses on the cross-corpus speech emotion recognition (SER) task, in which the training and testing speech signals in cross-corpus SER belong to different speech corpora. Existing algorithms are incapable of effectively extracting common sentiment information between different corpora to facilitate knowledge transfer. To address this challenging problem, a novel convolutional auto-encoder and adversarial domain adaptation (CAEADA) framework for cross-corpus SER is proposed. The framework first constructs a one-dimensional convolutional auto-encoder (1D-CAE) for feature processing, which can explore the correlation among adjacent one-dimensional statistic features and the feature representation can be enhanced by the architecture based on encoder-decoder-style. Subsequently the adversarial domain adaptation (ADA) module alleviates the feature distributions discrepancy between the source and target domains by confusing domain discriminator, and specifically employs maximum mean discrepancy (MMD) to better accomplish feature transformation. To evaluate the proposed CAEADA, extensive experiments were conducted on EmoDB, eNTERFACE, and CASIA speech corpora, and the results show that the proposed method outperformed other approaches.

A surrogate-based electromagnetic (EM) optimization using neural networks (NNs) is presented for computationally efficient microwave bandpass filter (BPF) design. This paper first describes the forward problem (EM analysis) and the inverse problems (EM design), and the two fundamental issues in BPF designs. The first issue is that the EM analysis is a time-consuming task, and the second one is that EM design highly depends on the structural optimization performed with the help of EM analysis. To accelerate the optimization design, two surrogate models of forward and inverse models are introduced here, which are built with the NNs. As a result, the inverse model can instantaneously guess initial structural parameters with high accuracy by simply inputting synthesized coupling-matrix elements into the NN. Then, the forward model in conjunction with optimization algorithm enables designers to rapidly find optimal structural parameters from the initial ones. The effectiveness of the surrogate-based EM optimization is verified through the structural designs of a typical fifth-order microstrip BPF with multiple couplings.

Although end-to-end based speech recognition research for Mandarin-English code-switching has attracted increasing interests, it remains challenging due to data scarcity. Meta-learning approach is popular with low-resource modeling using high-resource data, but it does not make full use of low-resource code-switching data. Therefore we propose a two-fold cross-validation training framework combined with meta-learning approach. Experiments on the SEAME corpus demonstrate the effects of our method.

This paper presents improvement of port-to-port isolation characteristics of a linearly dual-polarized dual-band and wideband multi-ring microstrip antenna (MR-MSA) fed by two L-probes. The linearly dual-polarized dual-band and wideband MR-MSA consists of two circular ring patches and two L-probes arranged in a multi-layered dielectric substrate. By using a thick substrate for the L-probe and arranging two ring patches as radiation elements, the proposed antenna operates wideband and dual-band characteristics. Furthermore, by arranging two L-probes at the orthogonal positions, the proposed antenna can radiate dual linear polarizations. In this paper, for improving port-to-port isolation characteristics of the linearly dual-polarized dual-band and wideband MR-MSA fed by two L-probes, a via connected to the ground plane at the center of the radiation elements is arranged. The fractional bandwidths below -10dB reflection obtained by the simulation of the MR-MSA with the via were 17.0% and 14.4%. Furthermore, the simulated isolation characteristics were more than 21.0dB and 17.0dB in the two bands. Improvement of the isolation characteristics between two ports as well as the dual-band and wideband performance of the proposed MR-MSA with the via were confirmed by the simulation and the measurement.

In this letter, the almost binary sequence (sequence with a single zero element) is considered as a special class of binary sequence. Four new bounds on the cross-correlation of balanced (almost) binary sequences with period Q ≡ 1(mod 4) under the precondition of out-of-phase autocorrelation values {-1} or {1, -3} are firstly presented. Then, seven new pairs of balanced (almost) binary sequences of period Q with ideal or optimal autocorrelation values and meeting the lower cross-correlation bounds are proposed by using cyclotomic classes of order 4. These new bounds of (almost) binary sequences with period Q achieve smaller maximum out-of-phase autocorrelation values and cross-correlation values.

We propose a technique based on Brillouin optical time domain analysis for measuring loss and crosstalk in few-mode fibers (FMFs). The proposed technique extracts the loss and crosstalk of a specific mode in FMFs from the Brillouin gains and Brillouin gain coefficients measured under two different conditions in terms of the frequency difference between the pump and probe lights. The technique yields the maximum loss and crosstalk at a splice point by changing the electrical field injected into an FMF as the pump light. Experiments demonstrate that the proposed technique can measure the maximum loss and crosstalk of the LP11 mode at a splice point in a two-mode fiber.

Blockchain is a distributed ledger technology used for trading digital assets, such as cryptocurrency, and trail records that need to be audited by third parties. The use cases of blockchain are expanding beyond cryptocurrency management. In particular, the token economy, in which tokenized assets are exchanged across different blockchain ledgers, is gaining popularity. Cross-chain technologies such as atomic swap have emerged as security technologies to realize this new use case of blockchain. However, existing approaches of cross-chain technology have unresolved issues, such as application limitations on different blockchain platforms owing to the incompatibility of the communication interface and crypto algorithm and inability to handle a complex business logic such as the escrow trade. In this study, the ConnectionChain is proposed, which enables the execution of an extended smart contract using abstracted operation on interworking ledgers. Moreover, field experimental results using the system prototype are presented and explained.

Cross-project defect prediction (CPDP) is a hot research topic in recent years. The inconsistent data distribution between source and target projects and lack of labels for most of target instances bring a challenge for defect prediction. Researchers have developed several CPDP methods. However, the prediction performance still needs to be improved. In this paper, we propose a novel approach called Joint Domain Adaption and Pseudo-Labeling (JDAPL). The network architecture consists of a feature mapping sub-network to map source and target instances into a common subspace, followed by a classification sub-network and an auxiliary classification sub-network. The classification sub-network makes use of the label information of labeled instances to generate pseudo-labels. The auxiliary classification sub-network learns to reduce the distribution difference and improve the accuracy of pseudo-labels for unlabeled instances through loss maximization. Network training is guided by the adversarial scheme. Extensive experiments are conducted on 10 projects of the AEEEM and NASA datasets, and the results indicate that our approach achieves better performance compared with the baselines.

In this letter, we propose a novel transferable sparse regression (TSR) method, for cross-database facial expression recognition (FER). In TSR, we firstly present a novel regression function to regress the data into a latent representation space instead of a strict binary label space. To further alleviate the influence of outliers and overfitting, we impose a row sparsity constraint on the regression term. And a pairwise relation term is introduced to guide the feature transfer learning. Secondly, we design a global graph to transfer knowledge, which can well preserve the cross-database manifold structure. Moreover, we introduce a low-rank constraint on the graph regularization term to uncover additional structural information. Finally, several experiments are conducted on three popular facial expression databases, and the results validate that the proposed TSR method is superior to other non-deep and deep transfer learning methods.

We present a novel circularly polarized ring microstrip antenna and its design. The shorting pins discretely disposed on the inner edge of the ring microstrip antenna are introduced as a new degree of freedom for improving the resonance frequency control. The number and diameter of the shorting pins control the resonance frequency; the resonance frequency can be almost constant with respect to the inner/outer diameter ratio, which expands the use of the ring microstrip antenna. The dual-band antenna where the proposed antenna includes another ring microstrip antenna is designed and measured, and simulated results agree well with the measured one.

Recently, edge servers located closer than the cloud have become expected for the purpose of processing the large amount of sensor data generated by IoT devices such as robots. Research has been proposed to improve responsiveness as a cache server by applying KVS (Key-Value Store) to the edge as a method for obtaining high responsiveness. Above all, a hybrid-KVS server that uses both DRAM and NVMM (Non-Volatile Main Memory) devices is expected to achieve both responsiveness and reliability. However, its effectiveness has not been verified in actual applications, and its effectiveness is not clear in terms of its relationship with the cloud. The purpose of this study is to evaluate the effectiveness of hybrid-KVS servers using the SLAM (Simultaneous Localization and Mapping), which is a widely used application in robots and autonomous driving. It is appropriate for applying an edge server and requires responsiveness and reliability. SLAM is generally implemented on ROS (Robot Operating System) middleware and communicates with the server through ROS middleware. However, if we use hybrid-KVS on the edge with the SLAM and ROS, the communication could not be achieved since the message objects are different from the format expected by KVS. Therefore, in this research, we propose a mechanism to apply the ROS memory object to hybrid-KVS by designing and implementing the data serialization function to extend ROS. As a result of the proposed fogcached-ros and evaluation, we confirm the effectiveness of low API overhead, support for data used by SLAM, and low latency difference between the edge and cloud.

In this paper, a planar balun having simple and compact features with slit ground was proposed. The operating frequency can be designed by the length and position of the defected ground slits. The 20 dB bandwidth of the common mode rejection ratio of the measuring balun was over 90%.

In conclusion, an initialization method has been introduced and studied to improve the SET speed in PCM. Before experiment verification, a two-dimensional finite analysis is used, and the results illustrate the proposed method is feasible to improve SET speed. Next, the R-I performances of the discrete PCM device and the resistance distributions of a 64 M bits PCM test chip with and without the initialization have been studied and analyzed, which confirms that the writing speed has been greatly improved. At the same time, the resistance distribution for the repeated initialization operations suggest that a large number of PCM cells have been successfully changed to be in an intermediate state, which is thought that only a shorter current pulse can make the cells SET successfully in this case. Compared the transmission electron microscope (TEM) images before and after initialization, it is found that there are some small grains appeared after initialization, which indicates that the nucleation process of GST has been carried out, and only needs to provide energy for grain growth later.

In this paper, for the purpose of clarifying the desired ITS information and communication systems considering both safety and social feasibility to prevention overengineering, using a microscopic traffic flow simulator, we discuss the required information acquisition rate of three types of safety driving support systems, that is, the sensor type and the communication type, the sensor and communication fusion type. Performances are evaluated from the viewpoint of preventing overengineering performance using the “TsRm evaluation method” that considers a vehicle approaching within the range of R meters within T seconds as the vehicle with a high possibility of collision, and that evaluates only those vehicles. The results show that regarding the communication radius and the sensing range, overengineering performance may be estimated when all vehicles in the evaluation area are used for evaluations without considering each vehicle's location, velocity and acceleration as in conventional evaluations. In addition, it is clarified that the sensor and communication fusion type system is advantageous by effectively complementing the defects of the sensor type systems and the communication type systems.

The microfabrication technique based on synchrotron radiation (SR) direct etching process has recently been applied to construct PTFE microstructures. This paper proposes a PTFE substrate integrated waveguide (PTFE SIW). It is expected that the PTFE SIW contributes to the improvement of the structural strength. A rectangular through-hole is introduced taking the advantage of the SR direct etching process. First, a PTFE SIW for the Q-band is designed. Then, a cruciform 3-dB directional coupler consisting of the PTFE SIW is designed and fabricated by the SR direct etching process. The validity of the PTFE SIW coupler is confirmed by measuring the frequency characteristics of the S-parameters. The mechanical strength of the PTFE SIW and the peeling strength of its Au film are also additionally investigated.

As an important type of science and technology service resource, energy consumption data play a vital role in the process of value chain integration between home appliance manufacturers and the state grid. Accurate electricity consumption prediction is essential for demand response programs in smart grid planning. The vast majority of existing prediction algorithms only exploit data belonging to a single domain, i.e., historical electricity load data. However, dependencies and correlations may exist among different domains, such as the regional weather condition and local residential/industrial energy consumption profiles. To take advantage of cross-domain resources, a hybrid energy consumption prediction framework is presented in this paper. This framework combines the long short-term memory model with an encoder-decoder unit (ED-LSTM) to perform sequence-to-sequence forecasting. Extensive experiments are conducted with several of the most commonly used algorithms over integrated cross-domain datasets. The results indicate that the proposed multistep forecasting framework outperforms most of the existing approaches.

This paper presents the design and measurement of wideband multi-ring microstrip antennas fed by an L-probe for single- and dual-band operation. The proposed antennas consist of one or two square ring patches and an L-probe arranged in a multi-layered dielectric substrate. By using a thick substrate for the L-probe and optimizing the distances between the L-probe and the patches, wideband performance is successfully achieved. The optimal substrate thickness of the L-probe and patches to obtain good wideband performance were determined, and prototype antennas for single- and dual-band operation were fabricated and tested. The measured fractional bandwidths corresponding to reflection coefficients below -10dB were 46.1% for the single-band antenna and 20.3% and 15.7% for the dual-band antenna. The measured gains of the test antennas in the above bandwidths were 0-6.9dBi for the single-band antenna and 3.0-8.6dBi for the dual-band antenna. Although the E-plane radiation patterns were slightly tilted against the frequency, stable broadside radiation was confirmed. The proposed antennas exhibited excellent performance as wideband planar antennas for single- and dual-band operation. The proposed wideband antennas can be easily extended to a dual linearly polarized antenna by using another L-probe in the orthogonal position.

In the letter, two classes of optimal codebooks and asymptotically optimal codebooks in regard to the Levenshtein bound are presented, which are based on mutually unbiased bases (MUB) and approximately mutually unbiased bases (AMUB), respectively.