The reduction of fabrication cost without reduction of transistor size is main target for future logic LSI. Previously stacked type Fe-FET NAND/NAND array with the single circuit block architecture for logic circuit had been proposed for this target. In this paper vertical hierarchical stacked type Fe-FET NAND/NAND array and its application to logic LSI have been newly proposed. The feature of the proposed scheme is multiple stacked circuit blocks architecture which operates independently. Compared with the single circuit block architecture the fabrication cost per circuit can be reduced to 36% using 16 stages circuit blocks without sacrificing high speed and low power characteristics. The proposed scheme is one of the most promising candidates for realizing low cost high speed future logic LSI.
Analysis precision of boost ratio and power efficiency in boost DC-DC converter circuit is improved by proposing adaptive equivalent circuit of output diode of the circuit. In experiment, boost ratio and power efficiency in high boost ratio circuit were 9.89 and 76.5 percent respectively with its load resistance of 20Ω driven by output voltage 10V. In experimental results, error in theoretical values of boost ratio compared with the measured values of that was reduced to -3.79% from 57.5% in the conventional circuit. In a tapped-inductor high boost ratio circuit, error in theoretical values of boost ratio was reduced to 3.54% from 31.8%. Error in theoretical values of power efficiency with the measured values of that was reduced to 5.51% from 33.2% in the conventional circuit. In a high boost ratio circuit, error in theoretical values of power efficiency was reduced to -3.32% from 17.3%. Power loss of every element in boost DC-DC converter circuits was analyzed with high precision by analysis of inductance current waveforms in those circuits. Error in theoretical values of power loss compared with measured values was reduced to equal or less than 5%.
M2M (machine-to-machine) is required and spreads rapidly in recent years. Wireless multi-hop communication is one of the communication styles to construct M2M. However, the performance estimation of such wireless multi-hop communication is not so easy because the bit error ratio and symbol rate adaptation heavily depend on various environmental conditions. Stochastic network calculus (SNC) is a theory for the performance estimation of network. However, current SNC lacks ways to take wireless random errors, branching and joining of traffic flows into calculation, these are necessary to estimate the performance of sink-tree wireless-sensor network. This paper proposed a performance estimation method for sink-tree wireless multi-hop network based on SNC. The proposed method has three attractive points. Firstly, this introduces parameters of hop number and node number considering wireless random error due to packet collision. Secondly, this treats actual sink-tree network model by considering joining of traffic. Thirdly, this separates the service curve and bounding function calculation to avoid double counting. To evaluate the accuracy of the result of the proposed method, conventional estimation methods and simulation evaluation were conducted on a typical wireless multi-hop sensor data collection network. The results validate that the proposed method achieves much higher-accuracy than conventional methods.
Near-infrared spectroscopy (NIRS) is an important technique that percutaneously and noninvasively monitors the changes in hemoglobin concentration of cerebral blood flow. We have developed headband-type NIRS devices using a light-emitting diode (LED) power source and a photodiode detector (PD) to reduce the burden in diagnosis preparation and the price of the NIRS module. The ten-channel nonreal-time headband-type NIRS device with PD and one-package three-wavelength LED as well as the two-channel real-time wireless NIRS headband that is controlled using a tablet PC were manufactured in house. The two developed headband NIRS devices were evaluated using an existing LABNIRS system using a verbal fluency task, and they exhibited the same tendency with NIRS signals of the different levels at the rest and task stages. The measurement of the real-time NIRS signals revealed that the voice and breath caused the source of low-frequency fluctuations superimposed on the NIRS signals.
Brain-computer interfaces (BCIs) are systems that control external devices by decoding information from brain activity signals. Functional near-infrared spectroscopy (fNIRS) has been used in many BCIs because of its simplicity of use and portability. However, hemodynamic changes in the scalp layer (scalp-hemodynamics) often contaminate fNIRS signals, and cause degradation of the detection accuracy of functional brain activities. Although several reduction methods have been proposed, no study has investigated their effects on fNIRS-BCI accuracy. In this study, we investigated the effects of applying scalp-hemodynamics reduction to the classification of for four tasks: ball grasping with left-, right-, or both-hands, or resting without movements. We applied a method that combined short source-detector distance channels with a general linear model. Results showed that the binary-class classification accuracy of left- or right-hand and the multi-class classification accuracy of 3-class grasping were significantly improved, suggesting that the scalp-hemodynamics reduction may provide more accurate fNIRS-BCIs.
In this study, we examined turn-taking behavior by measuring brain activity using functional magnetic resonance imaging (fMRI). We developed virtual characters that exhibited turn-taking behavior. These characters displayed facial expressions that indicated a willingness to continue talking, and regulated its timing in conversational turn-taking. We then confirmed the validity of these behaviors through behavioral experiments that used a pseudo-conversation between these virtual characters and human participants. In addition, we conducted an fMRI experiment where the participants were required to read part of a scenario in a conversation with the character. The results showed that the insular cortex and the right superior temporal gyrus were significantly activated when the character behaved properly in turn-taking. These results suggest that the activation of the right superior temporal gyrus was related to mutual understanding. The insular cortex has been shown to be related to empathy in previous studies. Thus, turn-taking may have a significant role related to mutual understanding and empathy in conversation. Our experimental design and findings may provide an objective framework for virtual character design, and contribute to the study of human conversation.
Analyzing behaviors of human eyes can be expected to be applied widely. For example, measuring number of blinks can be applied to sound the alarm for driver's sleepiness. And, eye gaze estimation can be applied to improve advertising effects and to be used for man-machine interface. In order to analyze behaviors of human eyes, detection of eye region in moving image are needed before analyzing. So this paper proposes upper and lower eyelids detection method using eye movement information of blinks and edge information of eyelids. First, this paper explains related researches and processes of proposed method. And then, this paper describes experiment and experimental result to verify the reliability or the effectiveness of proposed method. In addition, this paper describes experiments to verify the effectiveness of glasses and face scale. Finally, this paper mentions features of proposed method and conclusion based on experimental results.
Today we can take many pictures with smart phones or digital cameras, and can edit them easily by ourselves. These pictures are very useful not only for hobby, but for investigation. It is very important to check whether they are doctored or not. This paper proposes an automatic detection method of doctored JPEG images based on two different analyisis: the block noise analysis and the Double-JPEG analysis. Former can find unnatural boundaries of 8×8 DCT blocks while latter can find double saved images by other editing software. Finally SVM classifies images into doctored and undoctored groups based on the above analysis. Experimental results have shown that the detection accuracy of our method achieves 0.90 in terms of F-measure while J. He's method achieves 0.82.
In this paper, we propose a new multi-objective optimization method based on a functional specialization search strategy. The functional specialization search strategy is composed of two ideas. The first idea is to evaluate the progress status of search points and classification of search points. The second idea is to use the status to update each search point by suitable one of the Pareto-ranking or scalarization. The proposed method takes advantage of this search strategy to realize an efficient improvement both of convergence and diversity of the search points. The proposed method achieves a consistently high performance through diverse search scenarios by taking advantage of the benefits of both a Pareto ranking-based method and a scalarization-based method. The performance of the proposed method was evaluated by some numerical simulations using some typical benchmark problems with different shapes of Pareto frontiers and various the number of objectives.
The learning automaton (LA) team model has been proposed as one method for modeling multi-agent systems. It is modeled as a non-cooperative game of learning automata. In this model, each LA operates independently from each other, and there exists a Nash equilibrium, i.e. the existance of an optimal mixed strategy in the mixed strategy space of the game has been proven. However, for modelling multi-agent systems more generally, the information exchange among agents and the acquisition of cooperative behaviors such as the formation of autonomous community are required. In this paper, in order to complement the LA team model, we propose a new LA team model with some fully or partially collaborative learning behaviors. In this new model, each automaton performs reinforcement learning process in order to identify random environments exchanging information with its adjacent automata. Several computer simulations indicate the availability of the proposed model.
The lack of nursing staff needs a system to watch the elderly. Our previous work focuses on conscientiousness of the cleaning to detect the living willingness of the elderly. However, it is short of feasibility, because it requires nursing care staff label the living willingness for their movement beforehand. The proposed method acquires the movement under high living willingness with the brightness distribution sensor to figure out the conscientiousness using SOM. One-class SVM detects decline of the living willingness labeling. The experiment indicated more precise distinction is expected with recognition of movement of the crosswise direction.
In this study, we investigated about fishing gears control when surf casting by a participant. As a result, it was found that the casting distance became longer when the participant released fishing line at the period of time from the maximum to zero of a fishing rod rotation speed. Thus, the validity of a fishing support system, that automatically releases the fishing line at the optimum time decided by the rotation speed data obtained from an acceleration sensor mounted on the fishing rod, was confirmed.