Introducing a magnetic tunneling junction (MTJ) into a flip-flop enables nonvolatile power gating (PG) but large store energy to MTJ is a critical concern. We propose an optimized two-step store (TSS) control to first perform a short store with an optimal time for all nonvolatile flip-flops (NVFFs) and then perform a long store only at the failed ones for reducing the store energy. As the key technologies to realize this, we present a verify-and-retryable NVFF (VR-NVFF) circuit enabling the TSS control and an analytical expression for the optimal short-store time (${T}_{\text {short}{\_}\text{opt}}$) minimizing the store energy. To examine the effectiveness of the optimized TSS control and the validity of analytically derived${T}_{\text {short}{\_}\text{opt}}$, we implemented the TSS control on a coarse-grained reconfigurable array (CGRA)-based accelerator chip and fabricated it in a 40-nm CMOS/MTJ hybrid process technology. Results demonstrated that analytical${T}_{\text {short}{\_}\text{opt}}$showed a good agreement with the measured value (within 8% difference) under process and temperature variations. The TSS control with${T}_{\text {short}{\_}\text{opt}}$reduced the store energy to$0.32\times $of that of the conventional long-store-only technique. The break-even time (BET), which is the minimum power-gating time to get the gain in energy savings, was shortened to 0.51–$0.7\times $by the TSS control, achieving the BET of 50–$923 \mu \text{s}$in the range of 0 °C–80 °C.

While the spin-transfer torque (STT) magnetic tunnel junction (MTJ) is a promising technique for enabling nonvolatile flip-flops (NVFFs) to perform power gating to reduce leakage power without any data losses, the large store energy (the energy to make a store operation) of MTJs needs to be addressed. The nonvolatile cool mega array series is an edge-oriented coarse-grained reconfigurable accelerator that implements an improved MTJ-based NVFF with a verify-and-retryable store method that should ideally reduce the store energy under the presence of the switching time variation originating from the stochastic nature of the MTJs. However, the energy reduction effect of the method has not been formulated or evaluated thoroughly enough to make the best use of the method in actual applications. In this study, we propose an analytical model to estimate the store energy in typical operational conditions under the assumption of switching time variations following the normal distributions based on the measurements of a real chip fabricated with a 40-nm perpendicular MTJ/CMOS hybrid process. In contrast to the tedious measurement on each different condition, the proposed model allows for an instantaneous determination of the best storing method for minimizing the store energy, with an energy reduction of up to 69% compared with a conventional one-time attempt storing method. This model is expected to be used for system-level energy simulations and, ultimately, for design explorations in pursuit of energy-optimized memory.

This paper describes the development of PHITS Plugin for calculating an integral radiation dose for the estimation of the effects of radiation on robots. PHITS Plugin is an extension of Choreonoid, and it was developed to contribute to design the system of the robots and to plan remote operations. We discuss the functional requirements to compute the dose distribution for calculating the integral radiation dose during the physical simulation. Also, we demonstrate to calculate the dose distribution by utilizing multiple radiation source examples.

This paper proposes a method of identifying the scan response state based on a similarity measure between scan response preliminary obtained in various network states and an unknown scan response data in order to estimate the cause of network scan failure/delay which is necessary in detecting compromised IoT wireless devices. Existing methods for measuring similarity between two vectors fail to find similarity between two scan response data where the distribution, dimension, or magnitude of such scan response data differs, therefore, a method that can clearly measure the similarity between scan responses data is required. In particular, a similarity measure based on the average absolute deviation with a baseline scheme that can be utilized for easy identification of the network scan response scenario is proposed. Thereafter, the estimation of cause of scan failure/delay is presented. The goal of our proposed method is to detect early the state of the target (scanned) network, in order to accurately estimate the cause of scan failure/delay from the scan response data. The performance of the proposed method is evaluated using computer simulation. The evaluation results show that the proposed similarity measure achieved high similarity ratio of all unknown scan response samples to correct scenario and outperforms other conventional similarity measure methods. Also, it is confirmed that the cause of scan failure/delay could be estimated with over 90% correct estimation accuracy in various scenarios.

Advertisement (ad) recommendation services for mobile users are rapidly increasing. The conventional ways of recommending ads are based on the analysis of user's explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on a method to analyze the user's latent interest in web browsing history which categorized positive and negative behaviors. However, we think that the latent interest of users appears not only in the web space but also in the physical space. In this paper, we adapt the method of the linked pages to physical space locations using geo-tagged tweets. Based on several evaluations, we discuss the possibility to recommend ads according to the user's current location.

advertisement (ad) recommendation services for mobile users is rapidly increasing. The conventional ways of recommending ads are based on the analysis of users' explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on an analysis of the user's latent interest on web browsing history which categorized positive and negative behaviors. In this paper, we adapt the method of the linked pages to real world locations using geo-tagged tweets. By several evaluations, we discuss the possibility to recommend ads according to the user's current location.

In this study, we present a method for controlling the surface shape of transparent liquids that generate caustics due to luminance distribution of an arbitrary image. Our method consists of the process of caustics design and the process of liquid surface control. While designing caustics, an arbitrary grayscale image is treated as the input image and the surface shape of transparent liquid that generates caustics is calculated based on the luminance distribution of that input image. We utilize the Poisson's equation to obtain a continuous liquid surface. A driving force based on the current height field and the target height field is introduced as the external force. This force that configures the current liquid surface into the target shape in the previous process is calculated and the change in the water surface and the caustics due to this force is verified using fluid simulation.

Capacitive power transfer (CPT) with resonance coupling has various advantages compared with inductive power transfer (IPT) with resonance coupling. There are some difficulties of the circuit design because the circuits in CPT with resonance coupling such as SS and SP topologies are not sufficiently analyzed. Therefore, this paper analyzes the SS and SP topologies based on power factor consideration and presents the design methods for resonant conditions and target load to achieve higher efficiency. Furthermore, theoretical formulas and characteristics of the transmission efficiency and output power in SS and SP topologies of CPT are investigated. The feasibility of theoretical formulas is verified with simulation and experiment.

Event-triggered control is one of the design paradigms for resource-constrained networked control systems. In this paper, we consider an event-triggered approach for perturbed time-varying nonlinear systems and show a trigger condition for the system to be uniformly ultimately bounded. We show that, by simulation, the proposed event-triggered control can reduce frequency of updating a state compared to time-triggered control in which a control input is updated at every iteration time.

We propose a system for transmitting a human performer's body and facial movements to a puppet with audiovisual feedback to the performer. The system consists of a head-mounted display (HMD) that shows the performer the video recording of the puppet's view, a microphone for voice capture, and photoreflectors for detecting the mouth movements of the human performer. In conventional puppetry, there is also the need for practice in the manipulation of the puppets to achieve good performance. The proposed telepresence system addresses these issues by enabling the human performer to manipulate the puppet through their own body and facial movements. The proposed system is expected to contribute to the development of new applications of puppetry and expand the interactivity of puppetry and the scope of entertainment.

We propose Human Coded Orchestra, a new approach to enable a group of individuals to sing in harmony by using computed directional speakers. The possibilities of musical performance by an untrained group have been explored in the fields of science and art. However, previous work has rarely proceeded beyond simple rhythm-based music and failed to achieve musical complexity. Human Coded Orchestra employs a number of directional speakers, each set at a different pitch, enabling them to deliver different pitches to each participant to sing to, according to their positions. Experiments demonstrated that participants succeeded in singing in harmony extemporaneously, and they reported that they enjoyed both the experience of singing and the feeling that they were able to participate in an activity with others. Our system does not require preparation on the part of singers, which opens up the possibility of practical application in the area of interactive performance.

In this paper, we focus on the bandwidth allocation issue in wireless access networks, which are made up of Ethernet Passive Optical Network (EPON) and Worldwide Interoperability for Microwave Access (WiMAX) networks, i.e., Fiber-Wire (FiWi) networks. Since the bandwidth allocation scheme largely determines the performance of the entire wireless access network, in the past decades, researchers have dedicated much effort to design bandwidth allocation algorithms based on different criteria in order to satisfy various performance requirements. Various types of bandwidth allocation scheme based on Max-Min Fairness (MMF) or Proportional Fairness (PF) criteria have been developed to increase not only system throughput but also user fairness. However, in general, there is a tradeoff relationship between maximizing system throughput and increasing the fairness among users in throughput, and the users satisfaction in their Quality of Service (QoS) cannot always be maximized by adopting fair bandwidth allocation methods. To cope with this issue, we propose a bandwidth allocation method which improves the QoS satisfaction of all users while maintaining the system throughput similar to standard schemes, such as MMF and PF. In our method, users satisfaction is quantified by using utility functions which can be different among users according to their applications and services. By transferring portions of bandwidth from fully filled users to others so as not to decrease the system throughput, the proposed scheme is able to eventually converge to a compromised point. The results of performance evaluation through computer simulations have demonstrated that our proposed scheme can successfully enhance the performance of wireless access networks.

This paper proposes an algorithm to estimate human intentions during walking. Not only walk start or stop but walking cycle is considered as the intentions in this paper. The algorithm is embedded into a walking support system, a wearable robot "Robot Suit HAL-3", for paraplegia patients. The estimation of patients' intentions is indispensable for effective and comfortable motion support, but the biological signals such as myoelectricity which is used for the support by HAL-3 cannot be measured properly. The proposed algorithm, therefore, estimates patients' intentions from other channels such as a floor reaction force and a body posture. The effectiveness of this algorithm is investigated through experiments with two types of patients. One has a sensory paralysis on both legs, especially a left leg has severe trouble. The other has troubles in sensory and motor ability on both legs. We show HAL-3 supports patients' walk comfortably, estimating patient intentions.