The ionospheric clutter in High Frequency Surface Wave Radar (HFSWR) is the reflection of electromagnetic waves from the ionosphere back to the receiver, which should be suppressed as much as possible for the primary purpose of target detection in HFSWR. However, ionospheric clutter contains vast quantities of ionospheric state information. By studying ionospheric clutter, some of the relevant ionospheric parameters can be inferred, especially during the period of typhoons, when the ionospheric state changes drastically affected by typhoon-excited gravity waves, and utilizing the time-frequency characteristics of ionospheric clutter at typhoon time, information such as the trend of electron concentration changes in the ionosphere and the direction of the typhoon can be obtained. The results of the processing of the radar data showed the effectiveness of this method.

The low-hit-zone (LHZ) frequency hopping sequence (FHS) sets are widely applicable in quasi-synchronous frequency hopping multiple-access (QS-FHMA) systems. In order to reduce mutual interference (MI) in the zone around the signal origin between different users, we recommend the LHZ FHS set instead of the conventional FHS set. In this letter, we propose a design of LHZ FHS sets via interleaving techniques. The obtained sequences can be confirmed that they are near-optimal in relation to the Peng-Fan-Lee bound.

This paper proposes a novel quad-band branched monopole antenna with a filter. The proposed antenna has a simple configuration in which branch-elements are added to a basic configuration consisting of a mast and dielectric wires. The antenna is characterized by performances such as wideband impedance matching, gain stabilization, and gain enhancement. Wideband impedance characteristics satisfying the voltage standing ratio of less than 2 are obtained by exciting a parallel resonance at the lowest band and multi-resonance at high bands. The filter suppressing higher order modes is used for gain stabilization, so that averaged gains above 5dBi are obtained at the quad-band. The antenna has a high gain of 11.1dBi because the branch-elements work as an end-fire array antenna at the highest band. Furthermore, it is clarified that an operating frequency is switched by using a variable bandpass filter at the lowest band. Last, a scale model of the antenna is fabricated and measured, then the effectiveness of the proposed antenna is demonstrated.

In the cellular system, the Worst Case User (WCU), whose distances to three nearest BSs are the similar, usually achieves the lowest performance. Improving user performance, especially the WCU, is a big problem for both network designers and operators. This paper works on the WCU in terms of coverage probability analysis by the stochastic geometry tool and data rate optimization with the transmission power constraint by the reinforcement learning technique under the Stretched Pathloss Model (SPLM). In analysis, only fast fading from the WCU to the serving Base Stations (BSs) is taken into the analysis to derive the lower bound coverage probability. Furthermore, the paper assumes that the Coordinated Multi-Point (CoMP) technique is only employed for the WCU to enhance its downlink signal and avoid the explosion of Intercell Interference (ICI). Through the analysis and simulation, the paper states that to improve the WCU performance under bad wireless environments, an increase in transmission power can be a possible solution. However, in good environments, the deployment of advanced techniques such as Joint Transmission (JT), Joint Scheduling (JS), and reinforcement learning is an suitable solution.

To cope with ever growing mobile data traffic, we recently proposed a concept of cellular ultra-dense radio access network (RAN). In the cellular ultra-dense RAN, a number of distributed antennas are deployed in the base station (BS) coverage area (cell) and user-clusters are formed to perform small-scale distributed multiuser multi-input multi-output (MU-MIMO) transmission and reception in each user-cluster in parallel using the same frequency resource. We also proposed a decentralized interference coordination (IC) framework to effectively mitigate both intra-cell and inter-cell interferences caused in the cellular ultra-dense RAN. The inter-cell IC adopted in this framework is the fractional frequency reuse (FFR), realized by applying the channel segregation (CS) algorithm, and is called CS-FFR in this paper. CS-FFR divides the available bandwidth into several sub-bands and allocates multiple sub-bands to different cells. In this paper, focusing on the optimization of the CS-FFR, we find by computer simulation the optimum bandwidth division number and the sub-band allocation ratio to maximize the link capacity. We also discuss the convergence speed of CS-FFR in a cellular ultra-dense RAN.

This paper proposes low complexity resource allocation in frequency domain non-orthogonal multiple access where many devices access with a base station. The number of the devices is assumed to be more than that of the resource for network capacity enhancement, which is demanded in massive machine type communications (mMTC). This paper proposes two types of resource allocation techniques, all of which are based on the MIN-MAX approach. One of them seeks for nicer resource allocation with only channel gains. The other technique applies the message passing algorithm (MPA) for better resource allocation. The proposed resource allocation techniques are evaluated by computer simulation in frequency domain non-orthogonal multiple access. The proposed technique with the MPA achieves the best bit error rate (BER) performance in the proposed techniques. However, the computational complexity of the proposed techniques with channel gains is much smaller than that of the proposed technique with the MPA, whereas the BER performance of the proposed techniques with channel gains is only about 0.1dB inferior to that with the MPA in the multiple access with the overloading ratio of 1.5 at the BER of 10-4. They attain the gain of about 10dB at the BER of 10-4 in the multiple access with the overloading ration of 2.0. Their complexity is 10-16 as small as the conventional technique.

Hybrid automatic repeat request (HARQ) is an essential technology that efficiently reduces the transmission error rate. However, for ultra-reliable low latency communications (URLLC) in the 5th generation mobile communication systems and beyond, the increase in latency due to retransmission must be minimized in HARQ. In this paper, we propose a highly-efficient low-latency HARQ method built on non-orthogonal multiple access (NOMA) for URLLC while minimizing the performance loss for coexisting services (use cases) such as enhanced mobile broadband (eMBB). The proposed method can be seen as an extension of the conventional link-level non-orthogonal HARQ to the system-level protocol. This mitigates the problems of the conventional link-level non-orthogonal HARQ, which are decoding error under poor channel conditions and an increase in transmission delay due to restrictions in retransmission timing. In the proposed method, delay-sensitive URLLC packets are preferentially multiplexed with best-effort eMBB packets in the same channel using superposition coding to reduce the transmission latency of the URLLC packet while alleviating the throughput loss in eMBB. This is achieved using a weighted channel-aware resource allocator (scheduler). The inter-packet interference multiplexed in the same channel is removed using a successive interference canceller (SIC) at the receiver. Furthermore, the transmission rates for the initial transmission and retransmission are controlled in an appropriate manner for each service in order to deal with decoding errors caused by error in transmission rate control originating from a time varying channel. We show that the proposed method significantly improves the overall performance of a system that simultaneously provides eMBB and URLLC services.

In this paper, we propose a hybrid crystal oscillator which achieves both quick startup and low steady-state power consumption. At startup, a large negative resistance is realized by configuring a Pierce oscillating circuit with a multi-stage inverter amplifier, resulting in high-speed startup. During steady-state oscillation, the oscillator is reconfigured as a class-C complementary Colpitts circuit for low power consumption and low phase noise. Prototype chips were fabricated in 65nm CMOS process technology. With Pierce-type configuration, the measured startup time and startup energy of the oscillator are reduced to 1/11 and 1/5, respectively, compared with the one without Pierce-type configuration. The power consumption during steady oscillation is 30 µW.

This paper presents a divide-by-9 injection-locked frequency divider (ILFD). It can lock onto about 6-GHz input with a locking range of 3.23GHz (58%). The basic concept of the ILFD is based on employing self-gated multiple inputs into the multiple-stage ring oscillator. A wide lock range is also realized by adapting harmonic-control circuits, which can boost specific harmonics generated by mixing. The ILFD was fabricated using a 55-nm deeply depleted channel (DDC) CMOS process. It occupies an area of 0.0210mm2, and consumes a power of 14.4mW.

Reducing on-chip microwave crosstalk is crucial for semiconductor spin qubit integration. Toward crosstalk reduction and qubit integration, we investigate on-chip microwave crosstalk for gate electrode pad designs with (i) etched trenches between contact pads or (ii) contact pads with reduced sizes. We conclude that the design with feature (ii) is advantageous for high-density integration of semiconductor qubits with small crosstalk (below -25 dB at 6 GHz), favoring the introduction of flip-chip bonding.

We have proposed and demonstrated a device fabrication process of physically defined quantum dots utilizing electron beam lithography employing a negative-tone resist toward high-density integration of silicon quantum bits (qubits). The electrical characterization at 3.8K exhibited so-called Coulomb diamonds, which indicates successful device operation as single-electron transistors. The proposed device fabrication process will be useful due to its high compatibility with the large-scale integration process.

Human motion prediction has always been an interesting research topic in computer vision and robotics. It means forecasting human movements in the future conditioning on historical 3-dimensional human skeleton sequences. Existing predicting algorithms usually rely on extensive annotated or non-annotated motion capture data and are non-adaptive. This paper addresses the problem of few-frame human motion prediction, in the spirit of the recent progress on manifold learning. More precisely, our approach is based on the insight that achieving an accurate prediction relies on a sufficiently linear expression in the latent space from a few training data in observation space. To accomplish this, we propose Regressive Gaussian Process Latent Variable Model (RGPLVM) that introduces a novel regressive kernel function for the model training. By doing so, our model produces a linear mapping from the training data space to the latent space, while effectively transforming the prediction of human motion in physical space to the linear regression analysis in the latent space equivalent. The comparison with two learning motion prediction approaches (the state-of-the-art meta learning and the classical LSTM-3LR) demonstrate that our GPLVM significantly improves the prediction performance on various of actions in the small-sample size regime.

Question-answering tasks over structured knowledge (i.e., tables and graphs) require the ability to encode structural information. Traditional pre-trained language models trained on linear-chain natural language cannot be directly applied to encode tables and graphs. The existing methods adopt the pre-trained models in such tasks by flattening structured knowledge into sequences. However, the serialization operation will lead to the loss of the structural information of knowledge. To better employ pre-trained transformers for structured knowledge representation, we propose a novel structure-aware transformer (SATrans) that injects the local-to-global structural information of the knowledge into the mask of the different self-attention layers. Specifically, in the lower self-attention layers, SATrans focus on the local structural information of each knowledge token to learn a more robust representation of it. In the upper self-attention layers, SATrans further injects the global information of the structured knowledge to integrate the information among knowledge tokens. In this way, the SATrans can effectively learn the semantic representation and structural information from the knowledge sequence and the attention mask, respectively. We evaluate SATrans on the table fact verification task and the knowledge base question-answering task. Furthermore, we explore two methods to combine symbolic and linguistic reasoning for these tasks to solve the problem that the pre-trained models lack symbolic reasoning ability. The experiment results reveal that the methods consistently outperform strong baselines on the two benchmarks.

We propose a quantitative measurement technique of video forgery that eliminates the decision burden of subtle boundary between normal and tampered patterns. We also propose the automatic adjustment scheme of spatial and temporal target zones, which maximizes the abnormality measurement of forged videos. Evaluation shows that the proposed scheme provides manifest detection capability against both inter-frame and intra-frame forgeries.

Authenticated Key Exchange (AKE) is a cryptographic protocol to share a common session key among multiple parties. Usually, PKI-based AKE schemes are designed to guarantee secrecy of the session key and mutual authentication. However, in practice, there are many cases where mutual authentication is undesirable such as in anonymous networks like Tor and Riffle, or difficult to achieve due to the certificate management at the user level such as the Internet. Goldberg et al. formulated a model of anonymous one-sided AKE which guarantees the anonymity of the client by allowing only the client to authenticate the server, and proposed a concrete scheme. However, existing anonymous one-sided AKE schemes are only known to be secure in the random oracle model. In this paper, we propose generic constructions of anonymous one-sided AKE in the random oracle model and in the standard model, respectively. Our constructions allow us to construct the first post-quantum anonymous one-sided AKE scheme from isogenies in the standard model.

For any m strings of total length n, we propose an O(mn log n)-time, O(n)-space algorithm that finds a maximal common subsequence of all the strings, in the sense that inserting any character in it no longer yields a common subsequence of them. Such a common subsequence could be treated as indicating a nontrivial common structure we could find in the strings since it is NP-hard to find any longest common subsequence of the strings.

With the rapid increase in the amount of data communication in 5G networks, there is a strong demand to reduce the power of the entire network, so the use of highly power-efficient millimeter-wave (mm-wave) networks is being considered. However, while mm-wave communication has high power efficiency, it has strong straightness, so it is difficult to secure stable communication in an environment with blocking. Especially when considering use cases such as autonomous driving, continuous communication is required when transmitting streaming data such as moving images taken by vehicles, it is necessary to compensate the blocking problem. For this reason, the authors examined an optimum radio access technology (RAT) selection scheme which selects mm-wave communication when mm-wave can be used and select wide-area macro-communication when mm-wave may be blocked. In addition, the authors implemented the scheme on a prototype device and conducted field tests and confirmed that mm-wave communication and macro communication were switched at an appropriate timing.

A novel displacement sensor was proposed based on a frequency delta-sigma modulator (FDSM) employing a microwave oscillator. To demonstrate basic operation, we fabricated a stylus surface profiler using a cylindrical cavity resonator, where one end of the cavity is replaced by a thin metal diaphragm with a stylus probe tip. Good surface profile was successfully obtained with this device. A 10 nm depth trench was clearly observed together with a 10 µm trench in a single scan without gain control. This result clearly demonstrates an extremely wide dynamic range of the FDSM displacement sensors.

Although security and privacy technologies are incorporated into every device and service, the complexity of these concepts confuses non-expert users. Prior research has shown that non-expert users ask strangers for advice about digital media use online. In this study, to clarify the security and privacy concerns of non-expert users in their daily lives, we investigated security- and privacy-related question posts on a Question-and-Answer (Q&A) site for non-expert users. We conducted a thematic analysis of 445 question posts. We identified seven themes among the questions and found that users asked about cyberattacks the most, followed by authentication and security software. We also found that there was a strong demand for answers, especially for questions related to privacy abuse and account/device management. Our findings provide key insights into what non-experts are struggling with when it comes to privacy and security and will help service providers and researchers make improvements to address these concerns.

The use of reports in action has grown significantly in recent decades as data has become digitized. However, traditional statistical methods no longer work due to the uncontrollable expansion and complexity of raw data. Therefore, it is crucial to clean and analyze financial data using modern machine learning methods. In this study, the quarterly reports (i.e. 10Q filings) of publicly traded companies in the United States were analyzed by utilizing data mining methods. The study used 8905 quarterly reports of companies from 2019 to 2022. The proposed approach consists of two phases with a combination of three different machine learning methods. The first two methods were used to generate a dataset from the 10Q filings with extracting new features, and the last method was used for the classification problem. Doc2Vec method in Gensim framework was used to generate vectors from textual tags in 10Q filings. The generated vectors were clustered using the K-means algorithm to combine the tags according to their semantics. By this way, 94000 tags representing different financial items were reduced to 20000 clusters consisting of these tags, making the analysis more efficient and manageable. The dataset was created with the values corresponding to the tags in the clusters. In addition, PriceRank metric was added to the dataset as a class label indicating the price strength of the companies for the next financial quarter. Thus, it is aimed to determine the effect of a company's quarterly reports on the market price of the company for the next period. Finally, a Convolutional Neural Network model was utilized for the classification problem. To evaluate the results, all stages of the proposed hybrid method were compared with other machine learning techniques. This novel approach could assist investors in examining companies collectively and inferring new, significant insights. The proposed method was compared with different approaches for creating datasets by extracting new features and classification tasks, then eventually tested with different metrics. The proposed approach performed comparatively better than the other machine learning methods to predict future price strength based on past reports with an accuracy of 84% on the created 10Q filings dataset.