Quantization is commonly used to reduce the inference time of convolutional neural networks (CNNs). To reduce the inference time without drastically reducing accuracy, optimal bit widths need to be allocated for each layer or filter of the CNN. In conventional methods, the optimal bit allocation is obtained by using the gradient descent algorithm while minimizing the model size. However, the model size has little to no correlation with the inference time. In this paper, we present a computational-complexity metric called MAC×bit that is strongly correlated with the inference time of quantized CNNs. We propose a gradient descent-based regularization method that uses this metric for optimal bit allocation of a quantized CNN to improve the recognition accuracy and reduce the inference time. In experiments, the proposed method reduced the inference time of a quantized ResNet-18 model by 21.0% compared with the conventional regularization method based on model size while maintaining comparable recognition accuracy.

The learning with errors (LWE) problem is one of the fundamental problems in cryptography and it has many applications in post-quantum cryptography. There are two variants of the problem, the decisional-LWE problem, and the search-LWE problem. LWE search-to-decision reduction shows that the hardness of the search-LWE problem can be reduced to the hardness of the decisional-LWE problem. The efficiency of the reduction can be regarded as the gap in difficulty between the problems. We initiate a study of quantum search-to-decision reduction for the LWE problem and propose a reduction that satisfies sample-preserving. In sample-preserving reduction, it preserves all parameters even the number of instances. Especially, our quantum reduction invokes the distinguisher only 2 times to solve the search-LWE problem, while classical reductions require a polynomial number of invocations. Furthermore, we give a way to amplify the success probability of the reduction algorithm. Our amplified reduction is incomparable to the classical reduction in terms of sample complexity and query complexity. Our reduction algorithm supports a wide class of error distributions and also provides a search-to-decision reduction for the learning parity with noise problem. In the process of constructing the search-to-decision reduction, we give a quantum Goldreich-Levin theorem over ℤq where q is a prime. In short, this theorem states that, if a hardcore predicate a・s (mod q) can be predicted with probability distinctly greater than (1/q) with respect to a uniformly random a ∈ ℤqn, then it is possible to determine s ∈ ℤqn.

The study of the spread of pandemics, including COVID-19, is an emerging concern to promote self-care management through social distancing, using state-of-the-art tools and technologies. Existing technologies provide many opportunities to acquire and process large volumes of data to monitor user activities from various perspectives. However, determining disease hotspots remains an open challenge considering user activities and interactions; providing related recommendations to susceptible individuals requires attention. In this article, we propose an approach to determine disease hotspots by modeling users’ activities from both cyber- and real-world spaces. Our approach uniquely connects cyber- and physical-world activities to predict hazardous regions. The availability of such an exciting data set is a non-trivial task; therefore, we produce the data set with much hard work and release it to the broader research community to facilitate further research findings. Once the data set is generated, we model it as a directed multi-attributed and weighted graph to apply classical machine learning and graph neural networks for prediction purposes. Our contribution includes mapping user events from cyber- and physical-world aspects, knowledge extraction, dataset generation, and reasoning at various levels. Within our unique graph model, numerous elements of lifestyle parameters are measured and processed to gain deep insight into a person’s status. As a result, the proposed solution enables the authorities of any pandemic, such as COVID-19, to monitor and take measurable actions to prevent the spread of such a disease and keep the public informed of the probability of catching it.

Big data processing is a set of techniques or programming models, which can be deployed on both the cloud servers or edge nodes, to access large-scale data and extract useful information for supporting and providing decisions. Meanwhile, several typical domains of human activity in smart society, such as social networks, medical diagnosis, recommendation systems, transportation, and Internet of Things (IoT), often manage a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. As one of the convincing solutions to carry out analytics for big data, graph processing is especially applicable for these application domains. However, either the intra-device or the inter-device data processing in the edge-cloud architecture is truly prone to be attacked by the malicious Trojans covertly embedded in the counterfeit processing systems developed by some third-party vendors in numerous practical scenarios, leading to identity theft, misjudgment, privacy disclosure, and so on. In this paper, for the first time to our knowledge, we specially build a novel attack model for ubiquitous graph processing in detail, which also has easy scalability for other applications in big data processing, and discuss some common existing mitigations accordingly. Multiple activation mechanisms of Trojans designed in our attack model effectively make the attacks imperceptible to users. Evaluations indicate that the proposed Trojans are highly competitive in stealthiness with trivial extra latency.

The performance improvement by CMOS circuit technology is reaching its limits. Many researchers have been studying computing technologies that use emerging devices to challenge such critical issues. Nanophotonic technology is a promising candidate for tackling the issue due to its ultra-low latency, high bandwidth, and low power characteristics. Although previous research develops hardware accelerators by exploiting nanophotonic circuits for AI inference applications, there has never been considered for the acceleration of training that requires complex Floating-Point (FP) operations. In particular, the design balance between optical and electrical circuits has a critical impact on the latency, energy, and accuracy of the arithmetic system, and thus requires careful consideration of the optimal design. In this study, we design three types of Opto-Electrical Floating-point Multipliers (OEFMs): accuracy-oriented (Ao-OEFM), latency-oriented (Lo-OEFM), and energy-oriented (Eo-OEFM). Based on our evaluation, we confirm that Ao-OEFM has high noise resistance, and Lo-OEFM and Eo-OEFM still have sufficient calculation accuracy. Compared to conventional electrical circuits, Lo-OEFM achieves an 87% reduction in latency, and Eo-OEFM reduces energy consumption by 42%.

This paper proposes two VLSI implementation approaches for periods estimation hardware of periodic signals. Digital signal processing is one of the important technologies, and to estimate periods of signals are widely used in many areas such as IoT, predictive maintenance, anomaly detection, health monitoring, and so on. This paper focuses on accumulation for real-time serial-to-parallel converter (ARS) which is a simple parameter estimation method for periodic signals. ARS is simple algorithm to estimate periods of periodic signals without complex instructions such as multiplier and division. However, this algorithm is implemented only on software, suitable hardware implementation methods are not clear. Therefore, this paper proposes two VLSI implementation methods called ARS-DFF and ARS-MEM. ARS-DFF is simple and fast implementation method, but hardware scale is large. ARS-MEM reduces hardware scale by introducing an SRAM macro cell. This paper also designs both approaches using SystemVerilog and evaluates VLSI implementation. According to our evaluation results, both proposed methods can reduce the power consumption to less than 1/1000 compared to the implementation on a microprocessor.

The effects of site diversity techniques on Ku-band rain attenuation are investigated using two kinds of simultaneous BS (Broadcasting Satellite) signal observations: one was conducted among Osaka Electro-Communication University (OECU) in Neyagawa, Kyoto University in Uji, and Shigaraki MU Observatory in Koka for the past ten years, and the other was conducted among the headquarter of OECU in Neyagawa and their other premises in Shijonawate and Moriguchi for the past seven years, respectively. The site diversity effects among these sites with horizontal separations of 3-50 km are found to be largely affected by the passage direction of rain areas characterized by each rain type, such as warm, cold, and stationary fronts or typhoon and shower. The performance of the site diversity primarily depends on the effective distance between the sites projected to the rain area motions. The unavailable time percentages are theoretically shown to be reduced down to about 61-73% of the ITU-R predictions by choosing a pair of the sites aligned closest to the rain area motion in the distance of 3-50 km. Then, we propose three kinds of novel site diversity methods that choose the pair of sites based on such as rain type, rain front motion, or rain area motion at each rainfall event, respectively. As a result, the first method, which statistically accumulates the average passage directions of each rain type from long-term observations, is even useful for practical operations of the site diversity, because unavailable time percentages are reduced down to about 75-85% compared with the theoretical limit of about 61-73%. Also, the third method based on the rain area motion directly obtained from the three-site observations yields the reduction in unavailable time percentages close to this theoretical limit.

Implementation of several wireless applications such as radar systems and source localization is possible with direction of arrival (DOA) estimation, an array signal processing technique. In the past, we proposed a DOA estimation method using deep neural networks (DNNs), which presented very good performance compared to the traditional root multiple signal classification (root-MUSIC) algorithm when the number of radio wave sources is two. However, once three radio wave sources are considered, the performance of that proposed DNN decays especially at low and high signal-to-noise ratios (SNRs). In this paper, mainly focusing on the case of three sources, we present two additional strategies based on our previous method and capable of dealing with each SNR region. The first, which supports DOA estimation at low SNRs, is a scheme that makes use of principal component analysis (PCA). By representing the DNN input data in a lower dimension with PCA, it is believed that the noise corrupting the data is greatly reduced, which leads to improved performance at such SNRs. The second, which supports DOA estimation at high SNRs, is a scheme where several DNNs specialized in radio waves with close DOA are accordingly selected to produce a more reliable angular spectrum grid in such circumstances. Finally, in order to merge both ideas together, we use our previously proposed SNR estimation technique, with which appropriate selection between the two schemes mentioned above is performed. We have verified the superiority of our methods over root-MUSIC and our previous technique through computer simulation when the number of sources is three. In addition, brief discussion on the performance of these proposed methods for the case of higher number of sources is also given.

A software-defined network (SDN) is a network that the centralized SDN controller controls multiple SDN switches. Load-balancing platforms can realize the distribution of the load of the switches between multiple controllers. The platforms allow controller processing capacity to be used efficiently. When the assignment between switches and controllers and the controller placement are changed, migration blackout time that the controller temporarily stops processing messages can occur. The migration blackout time can result in failure to meet delay requirements between switches and controllers. This paper proposes a model that determines the controller assignment and placement while minimizing the migration blackout time with the load-balancing platform. The proposed model can be used when the controllers in the network are overloaded and the controller assignment and placement need to be changed. We formulate the proposed model as a mixed-integer second-order cone programming problem. We develop a migration procedure used in the proposed model. In the procedure, each switch can be controlled by multiple controllers with a load-balancing platform. The load-balancing platform allows status messages sent from a switch to be sent to multiple controllers. This allows status messages sent from the switches to be processed in order and the migration blackout can be avoided. The proposed model is compared with a baseline model based on the previous works. In the baseline model, the migration blackout time always occurs when the controller assignment and placement are changed. Numerical results show that the migration blackout time in the proposed model becomes smaller than that in the baseline model. The results also show that the number of controllers placed in the proposed model is smaller than that in the baseline model.

Toffoli gates are an important primitive in reversible Boolean logic. In quantum computation, these Toffoli gates are composed using other elementary gates, most notably the Clifford+T basis. However, in fault-tolerant implementations of quantum circuits, the T-gate incurs extra cost relative to Clifford gates like the S-gate and CNOT gate. Relative-phase Toffoli Gates (RTOFs) have been proposed as a way to reduce this T-count at the cost of incurring a relative phase that could skew the final quantum states. In this paper, we utilize an observation that the relative phase which RTOFs introduce can be canceled by the appropriate application of less expensive S-gates instead of T-gates. It leverages alternate forms of the RTOF including incorporating S-gates into it or moving around its input bits in order to simplify the logic to erase the relative phase. We find experimentally that our method has a clear advantage in most cases, and identify several types of circuits that it could be synergistic with.

Recent studies on facial expression recognition mainly employs discrete category labels to represent emotion states. However, current intelligent emotion interaction systems require more diverse and precise emotion representation metrics, which has been proposed as Valence, Arousal, Dominance (VAD) multi-dimensional continuous emotion parameters. But there are still very less datasets and methods for VAD analysis, making it difficult to meet the needs of large-scale and high-precision emotion cognition. In this letter, we build multi-dimensional facial expression recognition method by using multi-task learning to improve recognition performance through exploiting the consistency between dimensional and categorial emotions. The evaluation results show that the multi-task learning approach improves the prediction accuracy for VAD multi-dimensional emotion. Furthermore, it applies the method to academic outcomes prediction which verifies that introducing the VAD multi-dimensional and multi-task facial expression recognition is effective in predicting academic outcomes. The VAD recognition code is publicly available on github.com/YeeHoran/Multi-task-Emotion-Recognition.

Drug discovery, characterized by its time-consuming and costly nature, demands approximately 9 to 17 years and around two billion dollars for development. Despite the extensive investment, about 90% of drugs entering clinical trials face withdrawal, with compound toxicity accounting for 30% of these instances. Ethical concerns and the discrepancy in mechanisms between humans and animals have prompted regulatory restrictions on traditional animal-based toxicity prediction methods. In response, human pluripotent stem cell-based approaches have emerged as an alternative. This paper investigates the scalability challenges inherent in utilizing pluripotent stem cells due to the costly nature of RNAseq and the lack of standardized protocols. To address these challenges, we propose applying Mixup data augmentation, a successful technique in deep learning, to kernel SVM, facilitated by Stochastic Dual Coordinate Ascent (SDCA). Our novel approach, Exact SDCA, leverages intermediate class labels generated through Mixup, offering advancements in both efficiency and effectiveness over conventional methods. Numerical experiments reveal that Exact SDCA outperforms Approximate SDCA and SGD in attaining optimal solutions with significantly fewer epochs. Real data experiments further demonstrate the efficacy of multiplexing gene networks and applying Mixup in toxicity prediction using pluripotent stem cells.

The Doppler velocity enhanced 79 GHz band millimeter wave (MMW) radar imaging approach is presented here, assuming a human body imaging or recognition application. There are numerous situations in which the spatial resolution is insufficient, due to aperture angle limitations, especially for vehicle mounted MMW radar systems. As the 79 GHz band MMW radar has a definitive advantage for higher Doppler velocity resolution even with a short coherent processing interval (CPI), this study introduces the Doppler velocity decomposed imaging scheme, focusing on micro-Doppler variations of the walking human model. The real experimental data show that our proposed approach provides further improvement for accurate and high resolution radar imaging.

Current resonance type of LLC converter is widely used owing to their low switching losses; however, the problem is that they have a large transformer loss. We examine the reduction of AC resistance of the transformer winding and high coupling between the primary and secondary windings of the transformer, as a method for reducing the copper loss. In this case, it is necessary to consider the effects of the increase in stray capacitance between the primary and secondary windings of the transformer. This paper describes the influence of the loss due to the capacitance generated between the transformer windings when a noise filter is connected to the LLC converter. Furthermore, we propose a new method for reducing loss by connecting a bridge-capacitor between the primary and secondary sides of the transformer. The results of the new method are shown, and compared with those of the simulations to demonstrate effectiveness.

This paper focuses on top-k query in cluster-based multi-hop wireless sensor networks (WSNs) employing wake-up receivers. We aim to design wake-up control that enables a sink to collect top-k data set, i.e., k highest readings of sensor nodes within a network, efficiently in terms of energy consumption and delay. Considering a tree-based clustered WSN, we propose a cluster-based wake-up control, which conducts activations and data collections of different clusters sequentially while the results of data collections at a cluster, i.e., the information on provisional top-k data set, are exploited for reducing unnecessary data transmissions at the other clusters. As a wake-up control employed in each cluster, we consider two different types of control: countdown content-based wake-up (CDCoWu) and identity-based wake-up (IDWu). CDCoWu selectively activates sensor nodes storing data belonging to top-k dataset while IDWu individually wakes up all sensor nodes within a cluster. Based on the observation that the best control depends on the number of cluster members, we introduce a hybrid mechanism of wake-up control, where a wake-up control employed at each cluster is selected between CDCoWu and IDWu based on its number of cluster members. Our simulation results show that the proposed hybrid wake-up control achieves smaller energy consumption and data collection delay than the control solely employing conventional CDCoWu or IDWu.

This study examines the threat of information leakage when digital ICs, which process sensitive information such as cryptographic operations and handling of personal and confidential information, are mounted on printed circuit boards with split ground (GND) planes. We modeled the mechanism of generating such information leakage and proposed a methodology to control it. It is known that the GND plane of a printed circuit board on which digital integrated circuits are mounted should be solid and undivided to ensure signal integrity, power integrity, and electromagnetic compatibility. However, in actual designs, printed circuit boards may have split GND planes to isolate analog and digital circuits, isolate high-voltage and low-voltage circuits, or integrate multi-function electronic control units. Such split GND planes can increase the risk of electromagnetic information leakage. We, therefore, investigated a side-channel attack standard evaluation board, SASEBO-G, which has been reported to leak cryptographic information superimposed on common-mode currents, known as one of the major causes of electromagnetic emanation. Our experimental results showed that the split GND planes were the dominant cause of common-mode (CM) information leakage. Next, we constructed an equivalent circuit model of the dominant leakage mechanism and confirmed that the behavior of side-channel information leakage superimposed in the simulated CM current was consistent with the measured results. We also confirmed that to mitigate side-channel information leakage in CM caused by the potential difference between the split GND planes, the impedance should be reduced in the information leakage band by connecting the GND planes with capacitors, and the like. In addition, the RF band coupling between cables should be weakened if the cables are connected to the split GND planes.

IoT and AI-related services and applications are becoming more common and it is expected that cyber-physical systems will enrich our daily lives in the near future. In such a situation, a huge amount of data is exchanged throughout the world in a real time fashion, which will increasingly push the need for optical network evolution. Considering these trends, intensive efforts on research and development have recently been dedicated to realize All-Photonics Network (APN) that is a key element of Innovative Optical and Wireless Network (IOWN). For materializing APN vision, one of the fundamental challenges is to expand network capacity in a highly cost- and energy-efficient manner. Meanwhile, many detailed studies have greatly improved the performance of multi-band optical networks. Thanks to such efforts, multi-band networking has been becoming feasible as a promising capacity-scaling solution, which can be a key enabler for APN. This paper reviews the recent advances in the multi-band optical network from various aspects. Specifically, we take a quick look at the studies enabling multi-band transmission in terms of devices design of transceiver and amplifier, quality-of-transmission estimation, and launch power optimization. Then, we focus on progress in optical switch technology, optical node configuration, path provisioning, and network analysis, aiming to deploy multi-band networks nationwide. We also refer to several emerging technologies, which include our efforts related to wavelength-selective band switching technology. This paper gives an opportunity to understand the trends and potential of multi-band optical networking.

In recent years, the declining birthrate and aging population have become serious problems in Japan. To solve these problems, we have developed a system based on edge AI. This system predicts the future heart rate during walking in real time and provides feedback to improve the quality of exercise and extend healthy life expectancy. In this paper, we predicted the heart rate in real time based on the proposed system and provided feedback. Experiments were conducted without and with the predicted heart rate, and a comparison was made to demonstrate the effectiveness of the predicted heart rate.

In this paper, a single-layer circular polarizer for linear polarized horn antenna is proposed. The multiple reflected waves between the aperture and array provide desired phase differences between vertical and horizontal polarizations. The measured gain of the fabricated antenna is 14.4 dBic and the half power beamwidths of the vertical polarization are 28 and 24 deg. and those of the horizontal polarization are 31 and 23 degrees in the vertical and horizontal planes. The polarizer has a low impact on the gain and beamwidth of the primary horn antenna and their changes are within 1.7 dB and 10 degrees. The 3 dB fractional bandwidth of the axial ratio is measured to be 1.4%.

In this paper, a precise design method of high-order bandpass filters (BPFs) with complicated coupling topologies is proposed, and is demonstrated through the design of an 11-pole BPF using TM010 mode dielectric resonators (DRs). A novel Z-shaped coupling structure is proposed which avoids the mixed use of TM010 and TM01δ modes and enables the tuning and assembling of the filter much easier. The coupling topology of the BPF includes three cascade triplets (CTs) of DRs, and both the capacitive and inductive couplings in the CTs are designed independently tunable, which produce consequently three controllable transmission zeros on both sides of the passband of filter. A procedure of mapping the coupling matrix of BPF to its physical dimensions is developed, and an iterative optimization of these physical dimensions is implemented to achieve best performance. The design of the 11-pole BPF is shown highly precise by the excellent agreement between the electromagnetic simulated response of the filter and the desired target specifications.