A ferroelectric-based (FE-based) non-volatile logic is proposed for low-power LSI. Standby currents in a logic circuit can be cut off by using FE-based non-volatile flip-flops (NVFFs), and the standby power can be reduced to zero. The FE capacitor is accessed only when the power turns on/off, performance of the NVFF is almost as same as that of the conventional flip-flop (FF) in a logic operation. The use of complementarily stored data in coupled FE capacitors makes it possible to realize wide read voltage margin, which guarantees 10 years retention at 85 degree Celsius under less than 1.5V operation. The low supply voltage and electro-static discharge (ESD) detection technique prevents data destruction caused by illegal access for the FE capacitor during standby state. Applying the proposed circuitry in CPU, the write and read operation for all FE capacitors in 1.6k-bit NVFFs are performed within 7µs and 3µs with access energy of 23.1nJ and 8.1nJ, respectively, using 130nm CMOS with Pb(Zr,Ti)O3(PZT) thin films.

In this manuscript we present a methodology for reducing the impact of the hosting room reflections in sound field rendering applications based on loudspeaker arrays. The problem is formulated in a least-squares sense. Since matrices involved in the problem are ill-conditioned, it is important to devise a suitable technique for the regularisation of the pseudo-inverse. In this work we adopt a truncated SVD method. The truncation, in particular, aims at reducing the impact of numerical errors and also errors on the knowledge of the sound speed. We include a wide set of experimental results, which validate the proposed technique.

Binaural reproduction is one of the promising approaches to present a highly realistic virtual auditory space to a listener. Generally, binaural signals are reproduced using a set of headphones that leads to a simple implementation of such a system. In contrast, binaural signals can be presented to a listener using a technique called “transaural reproduction” which employs a few loudspeakers with crosstalk cancellation for compensating acoustic transmissions from the loudspeakers to both ears of the listener. The major advantage of transaural reproduction is that a listener is able to experience binaural reproduction without wearing any device. This leads to a more natural listening environment. However, in transaural reproduction, the listener is required to be still within a very narrow sweet spot because the crosstalk canceller is very sensitive to the listener's head position and orientation. To solve this problem, dynamic transaural systems have been developed by utilizing contact type head tracking. This paper introduces the development of a dynamic transaural system with non-contact head tracking which releases the listener from any attachment, thereby preserving the advantage of transaural reproduction. Experimental results revealed that sound images presented in the horizontal and median planes were localized more accurately when the system tracked the listener's head rotation than when the listeners did not rotate their heads or when the system did not track the listener's head rotation. These results demonstrate that the system works effectively and correctly with the listener's head rotation.

A major challenge for speech emotion recognition is that when the training and deployment conditions do not use the same speech corpus, the recognition rates will obviously drop. Transfer learning, which has successfully addressed the cross-domain classification or recognition problem, is presented for cross-corpus speech emotion recognition. First, by using the maximum mean discrepancy embedding (MMDE) optimization and dimension reduction algorithms, two close low-dimensional feature spaces are obtained for source and target speech corpora, respectively. Then, a classifier function is trained using the learned low-dimensional features in the labeled source corpus, and directly applied to the unlabeled target corpus for emotion label recognition. Experimental results demonstrate that the transfer learning method can significantly outperform the traditional automatic recognition technique for cross-corpus speech emotion recognition.

Registration consistency (RC) stands out as a widely-used automatic measure from existing image registration evaluation measures. However the original RC neglects the influence brought by the image intensity variation, leading to several problems. This letter proposes a rectified registration consistency, which takes both image intensity variation and geometrical transformation into consideration. Therefore the geometrical transformation is evaluated more by decreasing the influence of intensity variation. Experiments on real image pairs demonstrated the superiority of the proposed measure over the original RC.

Sensing and reproduction of precise sound-space information is important to realize highly realistic audio communications. This study was conducted to realize high-precision sensors of 3D sound-space information for transmission to distant places and for preservation of sound data for the future. Proposed method comprises a compact and spherical object with numerous microphones. Each recorded signal from multiple microphones that are uniformly distributed on the sphere is simply weighted and summed to synthesize signals to be presented to a listener's left and right ears. The calculated signals are presented binaurally via ordinary binaural systems such as headphones. Moreover, the weight can be changed according to a human's 3D head movement. A human's 3D head movement is well known to be a crucially important factor to facilitate human spatial hearing. For accurate spatial hearing, 3D sound-space information is acquired as accurately reflecting the listener's head movement. We named the proposed method SENZI (Symmetrical object with ENchased ZIllion microphones). The results of computer simulations demonstrate that our proposed SENZI outperforms a conventional method (binaural Ambisonics). It can sense 3D sound-space with high precision over a wide frequency range.

This paper describes a soft-error tolerant and margin-enhanced nMOS-pMOS reversed 6T SRAM cell. The 6T SRAM bitcell comprises pMOS access and driver transistors, and nMOS load transistors. Therefore, the nMOS and pMOS masks are reversed in comparison with those of a conventional bitcell. In scaled process technology, The pMOS transistors present advantages of small random dopant fluctuation, strain-enhanced saturation current, and small soft-error sensitivity. The four-pMOS and two-nMOS structure improves the soft-error rate plus operating margin. We conduct SPICE and neutron-induced soft-error simulations to evaluate the n-p reversed 6T SRAM bitcell in 130-nm to 22-nm processes. At the 22-nm node, a multiple-cell-upset and single-bit-upset SERs are improved by 34% and 51% over a conventional 6T cell. Additionally, the static noise margin and read cell current are 2.04× and 2.81× improved by leveraging the pMOS benefits.

The IEEE 802.11 distributed coordinated function (DCF) adopts carrier sense multiple access with collision avoidance (CSMA/CA) as its medium access control (MAC) protocol. In a wireless local area network (WLAN) stations (STAs) congested situation, the performance of the WLAN system is significantly degraded due to a collision between the STAs. In this paper, we propose a simple method that decreases the number of frame collisions. After a successful transmission, the proposed method refrains from transmission during certain time which is defined as post-inter-frame space (Post-IFS). This mechanism improves the system performance including the throughput characteristics and access delay by reducing the number of competing STAs. The length of the Post-IFS is a key factor in improving the system performance for the proposed method. If the access point (AP) can estimate the optimal value of the Post-IFS, collision-free operation similar to that in centralized control is performed. Even if the optimal Post-IFS is not estimated, the number of competing STAs and the collision probability are decreased. Computer simulations verify that the proposed method achieves 40% higher system throughput compared to the conventional CSMA/CA for a network with 50 STAs.

Independent and identically distributed (i.i.d) assumptions are commonly used in the machine learning community. However, social media data violate this assumption due to the linkages. Meanwhile, with the variety of data, there exist many samples, i.e., Universum, that do not belong to either class of interest. These characteristics pose great challenges to dealing with social media data. In this letter, we fully take advantage of Universum samples to enable the model to be more discriminative. In addition, the linkages are also taken into consideration in the means of social dimensions. To this end, we propose the algorithm Semi-Supervised Linked samples Feature Selection with Universum (U-SSLFS) to integrate the linking information and Universum simultaneously to select robust features. The empirical study shows that U-SSLFS outperforms state-of-the-art algorithms on the Flickr and BlogCatalog.

In this paper, we propose a fully automatic method for extracting carotid artery contours from ultrasound images based on an active contour approach. Several contour extraction techniques have been proposed to measure carotid artery walls for early detection of atherosclerotic disease. However, the majority of these techniques require a certain degree of user interaction that demands time and effort. Our proposal automatically detects the position of the carotid artery by identifying blood flow information related to the carotid artery, and an active contour model is employed that uses initial contours placed in the detected position. Our method also applies a global energy minimization scheme to the active contour model. Experiments on clinical cases show that the proposed method automatically extracts the carotid artery contours at an accuracy close to that achieved by manual extraction.

Let T1,T2,...,Tk be spanning trees in a graph G. If, for any two vertices u,v of G, the paths joining u and v on the k trees are mutually vertex-disjoint, then T1,T2,...,Tk are called completely independent spanning trees (CISTs for short) of G. The construction of CISTs can be applied in fault-tolerant broadcasting and secure message distribution on interconnection networks. Hasunuma (2001) first introduced the concept of CISTs and conjectured that there are k CISTs in any 2k-connected graph. Unfortunately, this conjecture was disproved by Péterfalvi recently. In this note, we give a necessary condition for k-connected k-regular graphs with ⌊k/2⌋ CISTs. Based on this condition, we provide more counterexamples for Hasunuma's conjecture. By contrast, we show that there are two CISTs in 4-regular chordal rings CR(N,d) with N=k(d-1)+j under the condition that k ≥ 4 is even and 0 ≤ j ≤ 4. In particular, the diameter of each constructed CIST is derived.

We present a way to correct light distortion of views looking into an aquarium. When we see fish in an aquarium, they appear closer and distorted due to light distortion. In order to correct the distortion, light rays travelling in the aquarium directly towards an observer should hit him/her after emerging from the aquarium. In this manuscript, those light rays are captured by a perspective camera at specific positions, not the observer position. And then it is shown that the taken images are successfully merged as a single one that is not affected by light distortion.

Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.

A new real-time sound field transmission system is presented. To construct this system, a large listening area needs to be reproduced at not less than a constant height. Additionally, the driving signals of the loudspeakers should be obtained only from received signals of microphones. Wave field reconstruction (WFR) filtering for linear arrays of microphones and loudspeakers is considered to be suitable for this kind of system. An experimental system was developed to show the feasibility of real-time sound field transmission using the WFR filter. Experiments to measure the reproduced sound field and a subjective listening test of sound localization were conducted to evaluate the proposed system. Although the reproduced sound field included several artifacts such as spatial aliasing and faster amplitude decay, the experimental results indicated that the proposed system was able to provide sound localization accuracy for virtual sound sources comparable to that for real sound sources in a large listening area.

H.264/AVC estimates the prediction mode from the modes of neighboring blocks in order to reduce the quantity of bits needed to represent the best mode. The estimated mode is known as Most Probable Mode (MPM). When QP changes, the probability of MPM increases. In this paper, we propose an adaptive mode signaling method based on the probability of MPM. According to the probability of MPM, a conventional MPM method and the proposed merging method are adaptively used to signal the intra prediction mode. Experimental results show that the proposed method achieves an average bit savings of 0.727% and an average PSNR gain of 0.041dB. In the low bit rate condition, the proposed method achieves an average bit savings of 1.795%, and a PSNR gain of 0.103dB, compared to H.264/AVC.

There has been increasing demand to research the measuring method to characterize the batch consistency of contact rivets. An automated test equipment has been described that makes it possible to measure the electrical contact resistance with high efficiency. The relationship between contact force and contact resistance during the loading and unloading process was measured explicitly using AgPd alloy, stainless steel and sapphire substrate material with Au coatings as sphere indenters separately. To explain the phenomena of contact resistance decreasing more slowly than the traditional theoretical results during loading, the indenter with coating and rivet are modeled by using the commercial FEM software COMSOL Multiphysics. Besides the constriction resistance, the transition region Au coating resistance and the bulk resistance of the substrate are deduced from the simulated current lines profiles and iso-potentials. The difference of electrical conductivity between indenter material and gold coating is the reason for the occurrence of the transition region.

Sound field reproduction systems seek to realistically convey 3D spatial audio by re-creating the sound pressure inside a region enclosing the listener. High-order Ambisonics (HOA), a sound field reproduction technology, is notable for defining a scalable encoding format that characterizes the sound field in a system-independent way. Sound fields sampled with a particular microphone array and encoded into the HOA format can be reproduced using any sound presentation device, typically a loudspeaker array, by using a HOA decoder. The HOA encoding format is based on the spherical harmonic decomposition; this makes it easier to design a decoder for large arrays of loudspeakers uniformly distributed over all directions. In practice, it is seldom possible to cover all directions with loudspeakers placed at regular angular intervals. An irregular array, one where the angular separation between adjacent loudspeakers is not constant, does not perform as well as a regular one when reproducing HOA due to the uneven sampling of the spherical harmonics. This paper briefly introduces the techniques used in HOA and advances a new approach to design HOA decoders for irregular loudspeaker arrays. The main difference between conventional methods and our proposal is the use of a new error metric: the radial derivative of the reconstruction error. Minimizing this metric leads to a smooth reproduction, accurate over a larger region than that achieved by conventional HOA decoders. We evaluate our proposal using the computer simulation of two 115-channel loudspeaker arrays: a regular and an irregular one. We find that our proposal results in a larger listening region when used to decode HOA for reproduction using the irregular array. On the other hand, applying our method matches the high-quality reproduction that can be attained with the regular array and conventional HOA decoders.

Recently, video surveillance systems have been widely introduced in various places, and protecting the privacy of objects in the scene has been as important as ensuring security. Masking each moving object with a background subtraction method is an effective technique to protect its privacy. However, the background subtraction method is heavily affected by sunshine change, and a redundant masking by over-extraction is inevitable. Such superfluous masking disturbs the quality of video surveillance. In this paper, we propose a moving object masking method combining background subtraction and machine learning based on Real AdaBoost. This method can reduce the superfluous masking while maintaining the reliability of privacy protection. In the experiments, we demonstrate that the proposed method achieves about 78-94% accuracy for classifying superfluous masking regions and moving objects.

This letter describe target classification from the synthesized active sonar returns from targets. A fractional Fourier transform is applied to the sonar returns to extract shape variation in the fractional Fourier domain depending on the highlight points and aspects of the target. With the proposed features, four different targets are classified using two neural network classifiers.

In this paper, we introduced a novel Kernel-Reliability-based K-Means (KRKM) clustering algorithm for categorizing an unknown dataset under noisy condition. Compared with the conventional clustering algorithms, the proposed KRKM algorithm will measure both the reliability and the similarity for classifying data into its neighbor clusters by the dynamic kernel functions, where the noisy data will be rejected by being given low reliability. The reliability for classifying data is measured by a dynamic kernel function whose window size will be determined by the triangular relationship from this data to its two nearest clusters. The similarity from a data item to its neighbor clusters is measured by another adaptive kernel function which takes into account not only the similarity from data to clusters but also that between its two nearest clusters. The main contribution of this work lies in introducing the dynamic kernel functions to evaluate both the reliability and similarity for clustering, which makes the proposed algorithm more efficient in dealing with very strong noisy data. Through various experiments, the efficiency and effectiveness of proposed algorithm have been confirmed.