Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems, and communication systems as they have efficient encoding and decoding algorithms. Let C(t,e) denote the cyclic code with two nonzero αt and αe, where α is a generator of 𝔽*3m. In this letter, we investigate the ternary cyclic codes with parameters [3m - 1, 3m - 1 - 2m, 4] based on some results proposed by Ding and Helleseth in 2013. Two new classes of optimal ternary cyclic codes C(t,e) are presented by choosing the proper t and e and determining the solutions of certain equations over 𝔽3m.

Artificial intelligence and the introduction of Internet of Things technologies have benefited from technological advances and new automated computer system technologies. Eventually, it is now possible to integrate them into a single offline industrial system. This is accomplished through machine-to-machine communication, which eliminates the human factor. The purpose of this article is to examine security systems for machine-to-machine communication systems that rely on identification and authentication algorithms for real-time monitoring. The article investigates security methods for quickly resolving data processing issues by using the Security operations Center’s main machine to identify and authenticate devices from 19 different machines. The results indicate that when machines are running offline and performing various tasks, they can be exposed to data leaks and malware attacks by both the individual machine and the system as a whole. The study looks at the operation of 19 computers, 7 of which were subjected to data leakage and malware attacks. AnyLogic software is used to create visual representations of the results using wireless networks and algorithms based on previously processed methods. The W76S is used as a protective element within intelligent sensors due to its built-in memory protection. For 4 machines, the data leakage time with malware attacks was 70 s. For 10 machines, the duration was 150 s with 3 attacks. Machine 15 had the longest attack duration, lasting 190 s and involving 6 malware attacks, while machine 19 had the shortest attack duration, lasting 200 s and involving 7 malware attacks. The highest numbers indicated that attempting to hack a system increased the risk of damaging a device, potentially resulting in the entire system with connected devices failing. Thus, illegal attacks by attackers using malware may be identified over time, and data processing effects can be prevented by intelligent control. The results reveal that applying identification and authentication methods using a protocol increases cyber-physical system security while also allowing real-time monitoring of offline system security.

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.

A 0.13mJ/prediction with 68.6% accuracy wired-logic deep neural network (DNN) processor is developed in a single 16-nm field-programmable gate array (FPGA) chip. Compared with conventional von-Neumann architecture DNN processors, the energy efficiency is greatly improved by eliminating DRAM/BRAM access. A technical challenge for conventional wired-logic processors is the large amount of hardware resources required for implementing large-scale neural networks. To implement a large-scale convolutional neural network (CNN) into a single FPGA chip, two technologies are introduced: (1) a sparse neural network known as a non-linear neural network (NNN), and (2) a newly developed raster-scan wired-logic architecture. Furthermore, a novel high-level synthesis (HLS) technique for wired-logic processor is proposed. The proposed HLS technique enables the automatic generation of two key components: (1) Verilog-hardware description language (HDL) code for a raster-scan-based wired-logic processor and (2) test bench code for conducting equivalence checking. The automated process significantly mitigates the time and effort required for implementation and debugging. Compared with the state-of-the-art FPGA-based processor, 238 times better energy efficiency is achieved with only a slight decrease in accuracy on the CIFAR-100 task. In addition, 7 times better energy efficiency is achieved compared with the state-of-the-art network-optimized application-specific integrated circuit (ASIC).

Owing to the COVID-19 pandemic, many academic conferences are now being held online. Our study focuses on online video conferences, where participants can watch pre-recorded embedded videos on a conference website. In online video conferences, participants must efficiently find videos that match their interests among many candidates. There are few opportunities to encounter videos that they may not have planned to watch but may be of interest to them unless participants actively visit the conference. To alleviate these problems, the introduction of a recommender system seems promising. In this paper, we implemented typical recommender systems for the online video conference with 4,000 participants and analyzed users’ behavior through A/B testing. Our results showed that users receiving recommendations based on collaborative filtering had a higher continuous video-viewing rate and spent longer on the website than those without recommendations. In addition, these users were exposed to broader videos and tended to view more from categories that are usually less likely to view together. Furthermore, the impact of the recommender system was most significant among users who spent less time on the site.

An efficient optical power splitter constructed by a metal-dielectric-metal plasmonic waveguide with a resonator structure has been analyzed. The method of solution is the finite difference time domain (FD-TD) method with the piecewise linear recursive convolution (PLRC) method. The resonator structure consists of input/output waveguides and a narrow waveguide with a T-junction. The power splitter with the resonator structure is expressed by an equivalent transmission-line circuit. We can find that the transmittance and reflectance calculated by the FD-TD method and the equivalent circuit are matched when the difference in width between the input/output waveguides and the narrow waveguide is small. It is also shown that the transmission wavelength can be adjusted by changing the narrow waveguide lengths that satisfy the impedance matching condition in the equivalent circuit.

In cyber-physical systems (CPSs) that interact between physical and information components, there are many sensors that are connected through a communication network. In such cases, the reduction of communication costs is important. Event-triggered control that the control input is updated only when the measured value is widely changed is well known as one of the control methods of CPSs. In this paper, we propose a design method of output feedback controllers with decentralized event-triggering mechanisms, where the notion of uniformly ultimate boundedness is utilized as a control specification. Using this notion, we can guarantee that the state stays within a certain set containing the origin after a certain time, which depends on the initial state. As a result, the number of times that the event occurs can be decreased. First, the design problem is formulated. Next, this problem is reduced to a BMI (bilinear matrix inequality) optimization problem, which can be solved by solving multiple LMI (linear matrix inequality) optimization problems. Finally, the effectiveness of the proposed method is presented by a numerical example.

We address a path planning problem for heterogeneous multi-robot systems under specifications consisting of temporal constraints and routing tasks such as package delivery services. The robots are partitioned into several groups based on their dynamics and specifications. We introduce a concise description of such tasks, called a work, and extend counting LTL to represent such specifications. We convert the problem into an ILP problem. We show that the number of variables in the ILP problem is fewer than that of the existing method using cLTL+. By simulation, we show that the computation time of the proposed method is faster than that of the existing method.

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.

In this paper, we investigated the overfitting characteristics of nonlinear equalizers based on an artificial neural network (ANN) and the Volterra series transfer function (VSTF), which were designed to compensate for optical nonlinear waveform distortion in optical fiber communication systems. Linear waveform distortion caused by, e.g., chromatic dispersion (CD) is commonly compensated by linear equalizers using digital signal processing (DSP) in digital coherent receivers. However, mitigation of nonlinear waveform distortion is considered to be one of the next important issues. An ANN-based nonlinear equalizer is one possible candidate for solving this problem. However, the risk of overfitting of ANNs is one obstacle in using the technology in practical applications. We evaluated and compared the overfitting of ANN- and conventional VSTF-based nonlinear equalizers used to compensate for optical nonlinear distortion. The equalizers were trained on repeated random bit sequences (RRBSs), while varying the length of the bit sequences. When the number of hidden-layer units of the ANN was as large as 100 or 1000, the overfitting characteristics were comparable to those of the VSTF. However, when the number of hidden-layer units was 10, which is usually enough to compensate for optical nonlinear distortion, the overfitting was weaker than that of the VSTF. Furthermore, we confirmed that even commonly used finite impulse response (FIR) filters showed overfitting to the RRBS when the length of the RRBS was equal to or shorter than the length of the tapped delay line of the filters. Conversely, when the RRBS used for the training was sufficiently longer than the tapped delay line, the overfitting could be suppressed, even when using an ANN-based nonlinear equalizer with 10 hidden-layer units.

We propose how to homomorphically evaluate arbitrary univariate and bivariate integer functions such as division. A prior work proposed by Okada et al. (WISTP'18) uses polynomial evaluations such that the scheme is still compatible with the SIMD operations in BFV and BGV schemes, and is implemented with the input domain ℤ257. However, the scheme of Okada et al. requires the quadratic numbers of plaintext-ciphertext multiplications and ciphertext-ciphertext additions in the input domain size, and although these operations are more lightweight than the ciphertext-ciphertext multiplication, the quadratic complexity makes handling larger inputs quite inefficient. In this work, first we improve the prior work and also propose a new approach that exploits the packing method to handle the larger input domain size instead of enabling the SIMD operation, thus making it possible to work with the larger input domain size, e.g., ℤ215 in a reasonably efficient way. In addition, we show how to slightly extend the input domain size to ℤ216 with a relatively moderate overhead. Further we show another approach to handling the larger input domain size by using two ciphertexts to encrypt one integer plaintext and applying our techniques for uni/bivariate function evaluation. We implement the prior work of Okada et al., our improved version of Okada et al., and our new scheme in PALISADE with the input domain ℤ215, and confirm that the estimated run-times of the prior work and our improved version of the prior work are still about 117 days and 59 days respectively while our new scheme can be computed in 307 seconds.

This paper proposes graph linear notations and an extension of them with regular expressions. Graph linear notations are a set of strings to represent labeled general graphs. They are extended with regular expressions to represent sets of graphs by specifying chosen parts for selections and repetitions of certain induced subgraphs. Methods for the conversion between graph linear notations and labeled general graphs are shown. The NP-completeness of the membership problem for graph regular expressions is proved.

In this paper, we investigate an online job scheduling problem with n jobs and k servers, where the accessibilities between the jobs and the servers are given as a bipartite graph. The scheduler is tasked with minimizing the regret, defined as the difference between the total flow time of the scheduler over T rounds and that of the best-fixed scheduling in hindsight. We propose an algorithm whose regret bounds are $O(n^2 sqrt{Tln (nk)})$ for general bipartite graphs, $O((n^2/k^{1/2}) sqrt{Tln (nk)})$ for the complete bipartite graphs, and $O((n^2/k) sqrt{T ln (nk)}$ for the disjoint star graphs, respectively. We also give a lower regret bound of $Omega((n^2/k) sqrt{T})$ for the disjoint star graphs, implying that our regret bounds are almost optimal.

In Variant 4 of the one-way trading game [El-Yaniv, Fiat, Karp, and Turpin, 2001], a player has one dollar at the beginning and wants to convert it to yen only by one-way conversion. The exchange rate is guaranteed to fluctuate between m and M, and only the maximum fluctuation ratio φ = M/m is informed to the player in advance. The performance of an algorithm for this game is measured by the competitive ratio. El-Yaniv et al. derived the best possible competitive ratio over all algorithms for this game. However, it seems that the behavior of the best possible algorithm itself has not been explicitly described. In this paper we reveal the behavior of the best possible algorithm by solving a linear optimization problem. The behavior turns out to be quite different from that of the best possible algorithm for Variant 2 in which the player knows m and M in advance.

A T-junction orthomode transducer (OMT) is a waveguide component that separates two orthogonal linear polarizations in the same frequency band. It has a common circular waveguide short-circuited at one end and two branch rectangular waveguides arranged in opposite directions near the short circuit. One of the advantages of a T-junction OMT is its short axial length. However, the two rectangular ports, which need to be orthogonal, have different levels of performance because of asymmetry. We therefore propose a uniaxially symmetrical T-junction OMT, which is configured such that the two branch waveguides are tilted 45° to the short circuit. The uniaxially symmetrical configuration enables same levels of performance for the two ports, and its impedance matching is easier compared to that for the conventional configuration. The polarization separation principle can be explained using the principles of orthomode junction (OMJ) and turnstile OMT. Based on calculations, the proposed configuration demonstrated a return loss of 25dB, XPD of 30dB, isolation of 21dB between the two branch ports, and loss of 0.25dB, with a bandwidth of 15% in the K band. The OMT was then fabricated as a single piece via 3D printing and evaluated against the calculated performance indices.

This paper proposes a non-linear overloaded MIMO detector that outperforms the conventional soft-input maximum likelihood detector (MLD) with less computational complexity. We propose iterative log-likelihood ratio (LLR) estimation and multi stage LLR estimation for the proposed detector to achieve such superior performance. While the iterative LLR estimation achieves better BER performance, the multi stage LLR estimation makes the detector less complex than the conventional soft-input maximum likelihood detector (MLD). The computer simulation reveals that the proposed detector achieves about 0.6dB better BER performance than the soft-input MLD with about half of the soft-input MLD's complexity in a 6×3 overloaded MIMO OFDM system.

Photo animation is to transform photos of real-world scenes into anime style images, which is a challenging task in AIGC (AI Generated Content). Although previous methods have achieved promising results, they often introduce noticeable artifacts or distortions. In this paper, we propose a novel double-tail generative adversarial network (DTGAN) for fast photo animation. DTGAN is the third version of the AnimeGAN series. Therefore, DTGAN is also called AnimeGANv3. The generator of DTGAN has two output tails, a support tail for outputting coarse-grained anime style images and a main tail for refining coarse-grained anime style images. In DTGAN, we propose a novel learnable normalization technique, termed as linearly adaptive denormalization (LADE), to prevent artifacts in the generated images. In order to improve the visual quality of the generated anime style images, two novel loss functions suitable for photo animation are proposed: 1) the region smoothing loss function, which is used to weaken the texture details of the generated images to achieve anime effects with abstract details; 2) the fine-grained revision loss function, which is used to eliminate artifacts and noise in the generated anime style image while preserving clear edges. Furthermore, the generator of DTGAN is a lightweight generator framework with only 1.02 million parameters in the inference phase. The proposed DTGAN can be easily end-to-end trained with unpaired training data. Extensive experiments have been conducted to qualitatively and quantitatively demonstrate that our method can produce high-quality anime style images from real-world photos and perform better than the state-of-the-art models.

In this paper, we investigate optimal design of millimeter-wave (mmWave) multiuser line-of-sight multiple-input-multiple-output (LOS MIMO) systems using hybrid arrays of subarrays based on hybrid block diagonalization (BD) precoding and combining scheme. By introducing a general 3D geometric channel model, the optimal subarray separation products of the transmitter and receiver for maximizing sum-rate is designed in terms of two regular configurations of adjacent subarrays and interleaved subarrays for different users, respectively. We analyze the sensitivity of the optimal design parameters on performance in terms of a deviation factor, and derive expressions for the eigenvalues of the multiuser equivalent LOS MIMO channel matrix, which are also valid for non-optimal design. Simulation results show that the interleaved subarrays can support longer distance communication than the adjacent subarrays given the appropriate fixed subarray deployment.

This study investigates the improvement of the channel capacity of 5-GHz-band multiple-input multiple-output (MIMO) communication using microwave-guided modes propagating along a polyvinyl chloride (PVC) pipe wall for a buried pipe inspection robot. We design a planar Yagi-Uda antenna to reduce transmission losses in communication with PVC pipe walls as propagation paths. Coupling efficiency between the antenna and a PVC pipe is improved by attaching a PVC adapter with the same curvature as the PVC pipe's inner wall to the Yagi-Uda antenna to eliminate any gap between the antenna and the inner wall of the PVC pipe. The use of a planar Yagi-Uda antenna with a PVC adaptor decreases the transmission loss of a 5-GHz-band microwave signal propagating along a 1-m-lomg straight PVC pipe wall by 7dB compared to a dipole antenna. The channel capacity of a 2×2 MIMO system using planar Yagi-Uda antennas is more than twice that of the system using dipole antennas.

In a multi-agent system, it is important to consider a design method of cooperative actions in order to achieve a common goal. In this paper, we propose two novel multi-agent reinforcement learning methods, where the control specification is described by linear temporal logic formulas, which represent a common goal. First, we propose a simple solution method, which is directly extended from the single-agent case. In this method, there are some technical issues caused by the increase in the number of agents. Next, to overcome these technical issues, we propose a new method in which an aggregator is introduced. Finally, these two methods are compared by numerical simulations, with a surveillance problem as an example.