In this article, we investigate the depth distribution and the depth spectra of linear codes over the ring R=F2+uF2+u2F2, where u3=1. By using homomorphism of abelian groups from R to F2 and the generator matrices of linear codes over R, the depth spectra of linear codes of type 8k14k22k3 are obtained. We also give the depth distribution of a linear code C over R. Finally, some examples are presented to illustrate our obtained results.

Recently, wearable wireless devices or terminals have become hot a topic not only in research but also in business. Implantable wireless devices can temporarily be utilized to monitor a patient's condition in an emergency situation or to identify people in highly secured places. Unlike conventional wireless devices, wearable or implantable devices are used on or in the human body. In this sense, body-centric wireless communications (BCWCs) have become a very active area of research. Radio-frequency or microwave medical devices used for cancer treatment systems and surgical operation have completely different functions, but they are used on or in the human body. In terms of research techniques, such medical devices have a lot of similarities to BCWCs. The antennas to be used in the vicinity of the human body should be safe, small and robust. Also, their interaction with the human body should be well considered. This review paper describes some of the wearable antennas as well as implantable antennas that have been studied in our laboratory.

A 12×16-element corporate-feed slot array is presented. The corporate-feed circuit for the 12×16-elemtent array consists of cross-junctions and asymmetric T-junctions, whereas the conventional one is limited to arrays of 2m×2n slots by its use of symmetric T-junctions. Simulations of the 12×16-element array show a 7.6% bandwidth for reflection less than -14dB. A 31.7-dBi gain with an antenna efficiency of 82.6% is obtained at the design frequency of 61.5GHz. The 12×16-element array is fabricated by diffusion bonding of laminated thin metal plates. Measurements indicate 31.1-dBi gain with 71.9% antenna efficiency at 61.5GHz.

In this paper, we present a weighted-polarization wearable multiple-input multiple-output (MIMO) antenna that is based on radio-frequency (RF) signal processing to realize ultra-high-speed and high-capacity mobile communications. The proposed antenna is comprised of three orthogonal dipoles, two of which can be selected according to a weight function in different usage scenarios. The weight function is determined by considering the variation in the cross-polarization power ratio (XPR) and the antenna inclination angle which depend on the radio-propagation environment and human motion. To confirm the suitability of the proposed antenna, we perform preliminary experiments to evaluate the channel capacity of a weighted-polarization wearable MIMO antenna with an arm-swinging dynamic phantom. The measured and analytical results are in good agreement, which verifies the effectiveness of the proposed antenna. We demonstrate that the proposed antenna is suitable for realizing gigabit mobile communications in future wearable MIMO applications.

This paper shows accuracy of using azimuth-variable path-loss fitting in white-space (WS) boundary-estimation. We perform experiments to evaluate this method, and demonstrate that the required number of sensors can be significantly reduced. We have proposed a WS boundary-estimation framework that utilizes sensors to not only obtain spectrum sensing data, but also to estimate the boundaries of the incumbent radio system (IRS) coverage. The framework utilizes the transmitter position information and pathloss fitting. The pathloss fitting describes the IRS coverage by approximating the well-known pathloss prediction formula from the received signal power data, which is measured using sensors, and sensor-transmitter separation distances. To enhance its accuracy, we have further proposed a pathloss-fitting method that employs azimuth variables to reflect the azimuth dependency of the IRS coverage, including the antenna directivity of the transmitter and propagation characteristics.

To investigate electrostatic discharge (ESD) immunity testing for wearable electronic devices, the worst scenario i.e., an ESD event occurs when the body-mounted device approaches a grounded conductor is focused in this paper. Discharge currents caused by air discharges from a charged human through a hand-held metal bar or through a semi-sphere metal attached to the head, arm or waist in lieu of actual wearable devices are measured. As a result, it is found that at a human charge voltage of 1kV, the peak current from the semi-sphere metal is large in order of the attachment of the waist (15.4A), arm (12.8A) and head (12.2A), whereas the peak current (10.0A) from the hand-held metal bar is the smallest. It is also found that the discharge currents through the semi-sphere metals decrease to zero at around 50ns regardless of the attachment positions, although the current through the hand-held metal bar continues to flow at over 90ns. These discharge currents are further characterized by the discharge resistance, the charge storage capacitance and the discharge time constant newly derived from the waveform energy, which are validated from the body impedance measured through the hand-held and body-mounted metals. The above finding suggests that ESD immunity test methods for wearable devices require test specifications entirely different from the conventional ESD immunity testing.

In the dynamic heterogeneous cellular network, spectrum allocation deeply impacts the quality of service and performance of network. In this paper, spectrum allocation is formulated as a dynamic programming problem. A two-level framework is proposed by jointly considering users' dynamic service selection and provider's spectrum allocation. In the first level, the users' service selection is modeled as an evolutionary game, and an evolutionary equilibrium is obtained. In the second level, the service provider allocates the spectral resources to macrocells and femtocells according to the users' strategies, so as to maximize its profits. By jointly considering the service selection and spectrum allocation, the equilibriums of the dynamic network are found. The stability of the equilibriums is analyzed and proven. The proposed two-level framework is validated by the numerical simulation.

The key factors in overcoming for weak global navigation satellite systems (GNSS) signal acquisition are sensitivity and dwell time. In the conventional MAX/TC criteria, a preset threshold value is used to determine whether the signal exists. Thus the threshold is calculated carefully to balance the sensitivity and the dwell time. Affected by various environment noise and interference, the acquisition circuit will enter verifying mode frequently to eliminate false alarms, which will extend the mean acquisition time (MAT). Based on the periodicity of spread spectrum code in GNSS, this paper presents an improved double-dwell scheme that uses no threshold in detecting weak GNSS signals. By adopting this method, the acquisition performance of weak signal is significantly improved. Theoretical analysis and numerical simulation are presented detailed. Compared with the conventional MAX/TC criteria, the proposed method achieves improved performance in terms of detection probability and false alarm rate. Furthermore, the MAT decreases 15s when C/N0 is above 20dB-Hz. This can enhance the receiver sensitivity and shorten the time to first fix (TTFF).

This paper proposes an analytical, closed-form AC-DC voltage multiplier model and investigates the dependency of output current and input power on circuit and device parameters. The model uses no fitting parameters and a frequency term applicable to both multipliers using diodes and metal-oxide semiconductor field effect transistors (MOSFETs). Analysis enables circuit designers to estimate circuit parameters, such as the number of stages and capacitance per stages, and device parameters such as saturation current (in the case of diodes) or transconductance (in the case of MOSFETs). Comparisons of the proposed model with SPICE simulation results as well as other models are also provided for validation. In addition, design optimizations and the impact of AC power source impedance on output power are also investigated.

Currently there is not any prospect of realizing quantum computers which can compute prime factorization, which RSA relies on, or discrete logarithms, which ElGamal relies on, of practical size. Additionally the rapid growth of Internet of Things (IoT) is requiring practical public key cryptosystems which do not use exponential operation. Therefore we constituted a cryptosystem relying on the difficulty of factoring the product of two large prime numbers, based on the Chinese Remainder Theorem, fully exploiting another strength of MPKC that exponential operation is not necessary. We evaluated its security by performing the Gröbner base attacks with workstations and consequently concluded that it requires computation complexity no less than entirely random quadratic polynomials. Additionally we showed that it is secure against rank attacks since the polynomials of central map are all full rank, assuming the environment of conventional computers.

In this paper we propose the RF domain beamforming (BF) scheme with a single analog-to-digital/digital-to-analog converters (ADC/DAC) to reduce the power consumption of the chipset for the application to mm-wave WPAN systems and THz communication systems. We also propose the codebook search algorithm for the estimation of the channel state information (CSI) which is a bottleneck to implement the RF BF. Our simulation results show that the deterioration of bit error rate (BER) performance of our proposed design compared to the optimal baseband BF techniques [1], [2] is not significant, while the power consumption and the process time is much reduced.

Nonnegative matrix factorization (NMF) is one of the most popular tools for speech enhancement. In this letter, we present an improved semi-supervised NMF (ISNMF)-based speech enhancement algorithm combining techniques of noise estimation and Incremental NMF (INMF). In this approach, fixed speech bases are obtained from training samples offline in advance while noise bases are trained on-the-fly whenever new noisy frame arrives. The INMF algorithm is adopted for noise bases learning because it can overcome the difficulties that conventional NMF confronts in online processing. The proposed algorithm is real-time capable in the sense that it processes the time frames of the noisy speech one by one and the computational complexity is feasible. Four different objective evaluation measures at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed method over traditional semi-supervised NMF (SNMF) and well-known robust principal component analysis (RPCA) algorithm.

In recent years, various Portable Navigation Devices (PND) such as smart-phones are becoming popular as a vehicle navigation device. To compare with a conventional built-in navigation system, PND has advantages that it is low cost and easily mounted to the vehicle. On the other hand, PND has also disadvantage that in the most case it cannot obtain the reliable vehicle speed information such as wheel pulse information and that induces degradation of vehicle trajectory estimation (dead-reckoning). The vehicle trajectory estimation is the positioning method using inertial sensors, and generally used when GPS is not available. So in urban area where GPS signals are blocked or reflected by tall buildings, the degradation of vehicle trajectory estimation may cause the severe increase of position error. Accordingly, in this study two approaches are proposed to improve vehicle trajectory estimation with PND. The first one is the accurate speed estimation using time-series tightly coupled integration of accelerometer, gyro, and Doppler shift of GPS. And the second one is the correction of trajectory error using backward refinement that can work even in real-time processing. The experimental result in Shinjuku which is dense urban city shows that the error of vehicle trajectory estimation was reduced to 1/4 compared with the previous method.

There is a strong push towards the ultra-realistic presentation of multimedia contents made possible by the latest advances in computational and signal processing technologies. Three-dimensional sound presentation is necessary to convey a natural and rich multimedia experience. Promising ways to achieve this include the sound field reproduction technique known as high-order Ambisonics (HOA). While these advanced methods are now within the capabilities of consumer-level processing systems, their adoption is hindered by the lack of contents. Production and coding of the audio components in multimedia focus on traditional formats such as stereophonic sound. Mainstream audio codecs and media such as CDs or DVDs do not support advanced, rich contents such as HOA encodings. To ameliorate this problem and speed up the adoption of spatial sound technologies, this paper proposes a novel way to downmix HOA contents into a stereo signal. The resulting data can be distributed using conventional methods such as audio CDs or as the audio component of an internet video stream. The results can be listened to using legacy stereo reproduction systems. However, they include spatial information encoded as the inter-channel level and phase differences. The proposed method consists of a downmixing filterbank which independently modulate inter-channel differences at each frequency bin. The proposal is evaluated using simple test signals and found to outperform conventional methods such as matrix-encoded surround and the Ambisonics UHJ format in terms of spatial resolution. The proposal can be coupled with a previously presented method to recover HOA signals from stereo recordings. The resulting system allows for the preservation of full-surround spatial information in ultra-realistic contents when they are transferred using a stereo stream. Simulation results show that a compatible decoder can accurately recover up to five HOA channels from a stereo signal (2nd order HOA data in the horizontal plane).

Many recent studies have focused on leveraging rich information types to increase useful information for improving fault localization effectiveness. However, they rarely investigate the impact of information richness on fault localization to give guidance on how to enrich information for improving localization effectiveness. This paper presents the first systematic study to fill this void. Our study chooses four representative information types and investigates the relationship between their richness and the localization effectiveness. The results show that information richness related to frequency execution count involves a high risk of degrading the localization effectiveness, and backward slice is effective in improving localization effectiveness.

Boneh and Franklin considered to add the revocation functionality to identity-based encryption (IBE). Though this methodology is applicable to any IBE and hierarchical IBE (HIBE), the resulting scheme is non-scalable. Therefore, a generic transformation of scalable revocable (H)IBE (R(H)IBE) from non-scalable R(H)IBE is really desirable. Towards this final goal, in this paper we introduce prototype RHIBE which does not require to be scalable (but requires some conditions), and propose a generic transformation of scalable RHIBE from prototype RHIBE. Moreover, we construct a prototype RHIBE scheme based on the decisional bilinear Diffie-Hellman (DBDH) assumption. Since our prototype RHIBE provides history-free update, insider security, and decryption key exposure resistance, our construction yields the first RHIBE scheme based on the static assumption with these desirable properties.

We propose part-segment (PS) features for estimating an articulated pose in still images. The PS feature evaluates the image likelihood of each body part (e.g. head, torso, and arms) robustly to background clutter and nuisance textures on the body. While general gradient features (e.g. HOG) might include many nuisance responses, the PS feature represents only the region of the body part by iterative segmentation while updating the shape prior of each part. In contrast to similar segmentation features, part segmentation is improved by part-specific shape priors that are optimized by training images with fully-automatically obtained seeds. The shape priors are modeled efficiently based on clustering for fast extraction of PS features. The PS feature is fused complementarily with gradient features using discriminative training and adaptive weighting for robust and accurate evaluation of part similarity. Comparative experiments with public datasets demonstrate improvement in pose estimation by the PS features.

In PQCrypto 2013, Yasuda, Takagi and Sakurai proposed a new signature scheme as one of multivariate public key cryptosystems (MPKCs). This scheme (called YTS) is based on the fact that there are two isometry classes of non-degenerate quadratic forms on a vector space with a prescribed dimension. The advantage of YTS is its efficiency. In fact, its signature generation is eight or nine times faster than Rainbow of similar size. For the security, it is known that the direct attack, the IP attack and the min-rank attack are applicable on YTS, and the running times are exponential time for the first and the second attacks and sub-exponential time for the third attack. In the present paper, we give a new attack on YTS whose approach is to use the diagonalization of matrices. Our attack works in polynomial time and it actually recovers equivalent secret keys of YTS having 140-bits security against min-rank attack in around fifteen seconds.

In many movies, social conditions and awareness of the issues of the times are depicted in any form. Even if fantasy and science fiction are works far from reality, the character relationship does mirror the real world. Therefore, we try to understand social conditions of the real world by analyzing the movie. As a way to analyze the movies, we propose a method of estimating interpersonal relationships of the characters, using a machine learning technique called Markov Logic Network (MLN) from movie script databases on the Web. The MLN is a probabilistic logic network that can describe the relationships between characters, which are not necessarily satisfied on every line. In experiments, we confirmed that our proposed method can estimate favors between the characters in a movie with F-measure of 58.7%. Finally, by comparing the relationships with social indicators, we discussed the relevance of the movies to the real world.

This letter proposes a novel post-processing method for self-similarity based super-resolution (SR). Existing back-projection (BP) methods enhance SR images by refining the reconstructed coarse high-frequency (HF) information. However, it causes artifacts due to interpolation and excessively smoothes small HF signals, particularly in texture regions. Motivated by these observations, we propose a novel post-processing method referred to as middle-frequency (MF) based refinement. The proposed method refines the reconstructed HF information in the MF domain rather than in the spatial domain, as in BP. In addition, it does not require an internal interpolation process, so it is free from the side-effects of interpolation. Experimental results show that the proposed algorithm provides superior performance in terms of both the quantity of reproduced HF information and the visual quality.