This article reviews the author’s group research achievements in analog/mixed-signal circuit and system area with introduction of how they came up with the ideas. Analog/mixed-signal circuits and systems have to be designed as well-balanced in many aspects, and coming up ideas needs some experiences and discussions with researchers. It is also heavily dependent on researchers. Here, the author’s group own experiences are presented as well as their research motivations.

Despite the increasing use of conversational artificial intelligence (AI) in online discussion environments, few studies explore the application of AI as a facilitator in forming problem-solving debates and influencing opinions in cross-venue scenarios, particularly in diverse and war-ravaged countries. This study aims to investigate the impact of AI on enhancing participant engagement and collaborative problem-solving in online-mediated discussion environments, especially in diverse and heterogeneous discussion settings, such as the five cities in Afghanistan. We seek to assess the extent to which AI participation in online conversations succeeds by examining the depth of discussions and participants' contributions, comparing discussions facilitated by AI with those not facilitated by AI across different venues. The results are discussed with respect to forming and changing opinions with and without AI-mediated communication. The findings indicate that the number of opinions generated in AI-facilitated discussions significantly differs from discussions without AI support. Additionally, statistical analyses reveal quantitative disparities in online discourse sentiments when conversational AI is present compared to when it is absent. These findings contribute to a better understanding of the role of AI-mediated discussions and offer several practical and social implications, paving the way for future developments and improvements.

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.

Code search is a task to retrieve the most relevant code given a natural language query. Several recent studies proposed deep learning based methods use multi-encoder model to parse code into multi-field to represent code. These methods enhance the performance of the model by distinguish between similar codes and utilizing a relation matrix to bridge the code and query. However, these models require more computational resources and parameters than single-encoder models. Furthermore, utilizing the relation matrix that solely relies on max-pooling disregards the delivery of word alignment information. To alleviate these problems, we propose a combined alignment model for code search. We concatenate the multi-code fields into one sequence to represent code and use one encoding model to encode code features. Moreover, we transform the relation matrix using trainable vectors to avoid information losses. Then, we combine intra-modal and cross-modal attention to assign the salient words while matching the corresponding code and query. Finally, we apply the attention weight to code/query embedding and compute the cosine similarity. To evaluate the performance of our model, we compare our model with six previous models on two popular datasets. The results show that our model achieves 0.614 and 0.687 Top@1 performance, outperforming the best comparison models by 12.2% and 9.3%, respectively.

Cross-domain data fusion is becoming a key driver in the growth of numerous and diverse applications in the Internet of Things (IoT) era. We have proposed the concept of a new information platform, Geo-Centric Information Platform (GCIP), that enables IoT data fusion based on geolocation, i.e., produces spatio-temporal content (STC), and then provides the STC to users. In this environment, users cannot know in advance “when,” “where,” or “what type” of STC is being generated because the type and timing of STC generation vary dynamically with the diversity of IoT data generated in each geographical area. This makes it difficult to directly search for a specific STC requested by the user using the content identifier (domain name of URI or content name). To solve this problem, a new content discovery method that does not directly specify content identifiers is needed while taking into account (1) spatial and (2) temporal constraints. In our previous study, we proposed a content discovery method that considers only spatial constraints and did not consider temporal constraints. This paper proposes a new content discovery method that matches user requests with content metadata (topic) characteristics while taking into account spatial and temporal constraints. Simulation results show that the proposed method successfully discovers appropriate STC in response to a user request.

This letter introduces a prime-factor Galois field Fourier transform (PF-GFFT) architecture to frequency domain decoding (FDD) of cyclic codes. Firstly, a fast FDD scheme is designed which converts the original single longer Fourier transform to a multi-dimensional smaller transform. Furthermore, a ladder-shift architecture for PF-GFFT is explored to solve the rearrangement problem of input and output data. In this regard, PF-GFFT is considered as a lower order spectral calculation scheme, which has sufficient preponderance in reducing the computational complexity. Simulation results show that PF-GFFT compares favorably with the current general GFFT, simplified-GFFT (S-GFFT), and circular shifts-GFFT (CS-GFFT) algorithms in time-consuming cost, and is nearly an order of magnitude or smaller than them. The superiority is a benefit to improving the decoding speed and has potential application value in decoding cyclic codes with longer code lengths.

In this paper, we investigate the evolution of an optical network architecture and discuss the future direction of research on optical network design and control. We review existing research on optical network design and control and present some open challenges. One of the important open challenges lies in multilayer resource optimization including IT and optical network resources. We propose an adaptive joint optimization method of IT resources and optical spectrum under time-varying traffic demand in optical networks while avoiding an increase in operation cost. We formulate the problem as mixed integer linear programming and then quantitatively evaluate the trade-off relationship between the optimality of reconfiguration and operation cost. We demonstrate that we can achieve sufficient network performance through the adaptive joint optimization while suppressing an increase in operation cost.

The novel optical path routing architecture named flexible waveband routing networks is reviewed in this paper. The nodes adopt a two-stage path routing scheme where wavelength selective switches (WSSs) bundle optical paths and form a small number of path groups and then optical switches without wavelength selectivity route these groups to desired outputs. Substantial hardware scale reduction can be achieved as the scheme enables us to use small scale WSSs, and even more, share a WSS by multiple input cores/fibers through the use of spatially-joint-switching. Furthermore, path groups distributed over multiple bands can be switched by these optical switches and thus the adaptation to multi-band transmission is straightforward. Network-wide numerical simulations and transmission experiments that assume multi-band transmission demonstrate the validity of flexible waveband routing.

Mobility as a Service (MaaS) is a smart mobility model that integrates mobility services to deliver transportation needs through a single interface, offering users flexible and personalizd mobility. This paper presents a structural approach for developing a MaaS system architecture under Autonomous Transportation Systems (ATS), which is a new transition from the Intelligent Transportation Systems (ITS) with emerging technologies. Five primary components, including system elements, user needs, services, functions, and technologies, are defined to represent the system architecture. Based on the components, we introduce three architecture elements: functional architecture, logical architecture and physical architecture. Furthermore, this paper presents an evaluation process, links the architecture elements during the process and develops a three-layer structure for system performance evaluation. The proposed MaaS system architecture design can help the administration make services planning and implement planned services in an organized way, and support further technical deployment of mobility services.

Similarity search for data streams has attracted much attention for information recommendation. In this context, recent leading works regard the latest W items in a data stream as an evolving set and reduce similarity search for data streams to set similarity search. Whereas they consider standard sets composed of items, this paper uniquely studies similarity search for text streams and treats evolving sets whose elements are texts. Specifically, we formulate a new continuous range search problem named the CTS problem (Continuous similarity search for Text Sets). The task of the CTS problem is to find all the text streams from the database whose similarity to the query becomes larger than a threshold ε. It abstracts a scenario in which a user-based recommendation system searches similar users from social networking services. The CTS is important because it allows both the query and the database to change dynamically. We develop a fast pruning-based algorithm for the CTS. Moreover, we discuss how to speed up it with the inverted index.

This paper describes the architectural design, services, and operation and monitoring functions of Science Information NETwork 6 (SINET6), a 400-Gigabit Ethernet-based academic backbone network launched on a nationwide scale in April 2022. In response to the requirements from universities and research institutions, SINET upgraded its world-class network speed, improved its accessibility, enhanced services and security, incorporated 5G mobile functions, and strengthened international connectivity. With fully-meshed connectivity and fast rerouting, it attains nationwide high performance and high reliability. The evaluation results of network performance are also reported.

Beyond 5G is the next generation mobile communication system expected to be used from around 2030. Services in the 2030s will be composed of multiple systems provided by not only the conventional networking industry but also a wide range of industries. However, the current mobile communication system architecture is designed with a focus on networking performance and not oriented to accommodate and optimize potential systems including service management and applications, though total resource optimizations and service level performance enhancement among the systems are required. In this paper, a new concept of the Beyond 5G cross-industry service platform (B5G-XISP) is presented on which multiple systems from different industries are appropriately organized and optimized for service providers. Then, an architecture of the B5G-XISP is proposed based on requirements revealed from issues of current mobile communication systems. The proposed architecture is compared with other architectures along with use cases of an assumed future supply chain business.

In this paper, multi-input multi-output (MIMO) signal detection with random walk along a gradient descent direction using an intermediate search point is presented. As a low complexity MIMO signal detection schemes, a gradient descent algorithm with Metropolis-Hastings (MH) methods has been proposed. Random walk along a gradient descent direction speeds up the MH based search using the gradient of a least-squares cost function. However, the gradient vector may be discarded through QAM constellation quantization in some cases. For further performance improvement, this paper proposes an improved search scheme in which the gradient vector is stored for the next search iteration to generate an intermediate search point. The performance of the proposed scheme improves with higher order modulation symbols as compared with that of a conventional scheme. Numerical results obtained through computer simulation show that a bit error rate (BER) performance improves by 5dB at a BER of 10-3 for 64QAM symbols in a 16×16 MIMO system.

We are given a set S of n points in the Euclidean plane. We assume that S is in general position. A simple polygon P is an empty polygon of S if each vertex of P is a point in S and every point in S is either outside P or a vertex of P. In this paper, we consider the problem of enumerating all the empty polygons of a given point set. To design an efficient enumeration algorithm, we use a reverse search by Avis and Fukuda with child lists. We propose an algorithm that enumerates all the empty polygons of S in O(n2|ε(S)|)-time, where ε(S) is the set of empty polygons of S. Moreover, by applying the same idea to the problem of enumerating surrounding polygons of a given point set S, we propose an enumeration algorithm that enumerates them in O(n2)-delay, while the known algorithm enumerates in O(n2 log n)-delay, where a surroundingpolygon of S is a polygon such that each vertex of the polygon is a point in S and every point in S is either inside the polygon or a vertex of the polygon.

6G research has been extensively conducted by individual organizations as well as pre-competitive joint research initiatives. One of the joint initiatives is the Hexa-X European 6G flagship project. This paper shares the up-to-date deliverables through which Hexa-X is envisioning the 6G era. The Hexa-X deliverables presented in this paper encompass the overall 6G vision, use cases and technical enablers. The latest deliverables on tenets of 6G architectural design and central pillars of technical enablers are presented. In conclusion, the authors encourage joint research and PoC collaboration with Japanese industry, academia and research initiatives for the potential technical enablers presented in this paper, aimed at global harmonization towards 6G standards.

This paper proposes a multiple code block transmission scheme using hierarchical modulation (HM) for a broadcast channel in the orthogonal frequency division multiplexing (OFDM) downlink. We investigate the average bit error rate (BER) performance of two-layer HM using 16 quadrature amplitude modulation (QAM) and three-layer HM using 64QAM in multipath Rayleigh fading channels. In multiple code block transmission using HM, the basic information bits are demodulated and decoded to all users within a cell that satisfy the bit error rate (BER) requirement. Hence, we investigate non-uniform QAM constellations to find one that suppresses the loss in the average BER of the basic information bits for HM to a low level compared to that using the original constellation in which only the basic information bits are transmitted while simultaneously minimizing the loss in the average BER of the secondary and tertiary information bits from the original constellations in which the information bits of the respective layers are transmitted alone. Based on the path loss equations in the Urban Macro and Rural Macro scenarios, we also investigate the maximum distance from a base station (BS) for the information bits of each layer to attain the required average received signal-to-noise power ratio (SNR) that achieves the average BER of 10-3.

Recently, many AI-aided layout design systems are developed to reduce tedious manual intervention based on deep learning. However, most methods focus on a specific generation task. This paper explores a challenging problem to obtain multiple layout design generation (LDG), which generates floor plan or urban plan from a boundary input under a unified framework. One of the main challenges of multiple LDG is to obtain reasonable topological structures of layout generation with irregular boundaries and layout elements for different types of design. This paper formulates the multiple LDG task as an image-to-image translation problem, and proposes a conditional generative adversarial network (GAN), called LDGAN, with adaptive modules. The framework of LDGAN is based on a generator-discriminator architecture, where the generator is integrated with conditional convolution constrained by the boundary input and the attention module with channel and spatial features. Qualitative and quantitative experiments were conducted on the SCUT-AutoALP and RPLAN datasets, and the comparison with the state-of-the-art methods illustrate the effectiveness and superiority of the proposed LDGAN.

It is very important to design an embedded real-time system as a fault-tolerant system to ensure dependability. In particular, when a power failure occurs, restart processing after power restoration is required in a real-time system using a conventional processor. Even if power is restored quickly, the restart process takes a long time and causes deadline misses. In order to design a fault-tolerant real-time system, it is necessary to have a processor that can resume operation in a short time immediately after power is restored, even if a power failure occurs at any time. Since current embedded real-time systems are required to execute many tasks, high schedulability for high throughput is also important. This paper proposes a non-stop microprocessor architecture to achieve a fault-tolerant real-time system. The non-stop microprocessor is designed so as to resume normal operation even if a power failure occurs at any time, to achieve little performance degradation for high schedulability even if checkpoint creations and restorations are performed many times, to control flexibly non-volatile devices through software configuration, and to ensure data consistency no matter when a checkpoint restoration is performed. The evaluation shows that the non-stop microprocessor can restore a checkpoint within 5µsec and almost hide the overhead of checkpoint creations. The non-stop microprocessor with such capabilities will be an essential component of a fault-tolerant real-time system with high schedulability.

In industry, automatic speech recognition has come to be a competitive feature for embedded products with poor hardware resources. In this work, we propose a tiny end-to-end speech recognition model that is lightweight and easily deployable on edge platforms. First, instead of sophisticated network structures, such as recurrent neural networks, transformers, etc., the model we propose mainly uses convolutional neural networks as its backbone. This ensures that our model is supported by most software development kits for embedded devices. Second, we adopt the basic unit of MobileNet-v3, which performs well in computer vision tasks, and integrate the features of the hidden layer at different scales, thus compressing the number of parameters of the model to less than 1 M and achieving an accuracy greater than that of some traditional models. Third, in order to further reduce the CPU computation, we directly extract acoustic representations from 1-dimensional speech waveforms and use a self-supervised learning approach to encourage the convergence of the model. Finally, to solve some problems where hardware resources are relatively weak, we use a prefix beam search decoder to dynamically extend the search path with an optimized pruning strategy and an additional initialism language model to capture the probability of between-words in advance and thus avoid premature pruning of correct words. In our experiments, according to a number of evaluation categories, our end-to-end model outperformed several tiny speech recognition models used for embedded devices in related work.

Many countries are facing the aging problem caused by the growth of the elderly population. Nursing home (NH) is a common solution to long-term care for the elderly. This paper develops a simulator to model elder behavior in an NH, which considers public areas where elders interact and imitates their general, group, and special activities. Elders have their preferences to decide activities taken by them. The simulator takes account of the movement of elders and abnormal events. Based on the simulator, two seeking methods are proposed for caregivers to search lost elders efficiently, which helps them fast find out elders who may incur accidents.