Fast simulation techniques of large scale RLC networks with nonlinear devices are presented. Generally, when scale of nonlinear part in a circuit is much less than the linear part, matrix or circuit partitioning approach is known to be efficient. In this paper, these partitioning techniques are used for the conventional transient analysis using an implicit numerical integration and the circuit-based finite-difference time-domain (FDTD) method, whose efficiency and accuracy are evaluated developing a prototype simulator. It is confirmed that the matrix and circuit partitioning approaches do not degrade accuracy of the transient simulations that is compatible to SPICE, and that the circuit partitioning approach is superior to the matrix one in efficiency. Moreover, it is demonstrated that the circuit-based FDTD method can be efficiently combined with the matrix or circuit partitioning approach, compared with the transient analysis using an implicit numerical integration.

The capacity maximization of line-of-sight (LoS) two-input and multiple-output (TIMO) channels in indoor environments is investigated in this paper. The 3×2 TIMO channel is mainly studied. First, the capacity fluctuation number (CFN) which reflects the variation of channel capacity is proposed. Then, the expression of the average capacity against the CFN is derived. The CFN is used as a criterion for optimization of the capacity by changing inter-element spacings of transmit and receive antenna arrays. Next, the capacity sensitivity of the 3×2 TIMO channel to the orientation and the frequency variation is studied and compared with those of 2×2 and 4×2 TIMO channels. A small capacity sensitivity of the 3×2 TIMO channel is achieved and verified by both simulation and measurement results. Furthermore, the CFN can also be used as a criterion for optimization of average capacity and the proposed optimization method is validated through numerical results.

Millimeter-wave (mm-wave) radio is attracting attention as one of the key enabling physical layer technologies for the fifth-generation (5G) mobile access and backhaul. This paper aims at clarifying possible roles of mm-wave radio in the 5G development and performing a comprehensive literature survey on mm-wave radio channel modeling essential for the feasibility study. Emphasis in the literature survey is laid on grasping the typical behavior of mm-wave channels, identifying missing features in the presently available channel models for the design and evaluation of the mm-wave radio links within the 5G context, and exemplifying different channel modeling activities through analyses performed in the authors' group. As a key technological element of the mm-wave radios, reduced complexity beamforming is also addressed. Design criteria of the beamforming are developed based on the spatial multipath characteristics of measured indoor mm-wave channels.

Controlling interference from the secondary system (SS) to the receiver of the primary system (PS) is an important issue when the SS uses the same frequency band as the television broadcast system. The reason includes that the SS is unaware of the interference imposed on the primary receiver (PS-Rx), which does not have a transmitter. In this paper, we propose an interference control method between PS-Rx and SS, where a load modulation scheme is introduced to the PS-Rx. In this method, the signal from the PS transmitting station is scattered by switching its load impedance. The SS observes the scattered channel and calculates the interference suppression weights for transmitting, and controls interference by transmit beamforming. A simulation shows that the Signal-to-Interference Ratio (SIR) with interference control is improved by up to 41.5dB compared to that without interference control at short distances; the results confirm that the proposed method is effective in controlling interference between PS-Rx and SS. Furthermore, we evaluate the Signal-to-Noise Ratio (SNR) and channel capacity at SS.

In this paper, we report our analysis of path loss variation characteristics in the low millimeter wave band based on measurement results obtained by using 26.365GHz. We analyze the fading scale of the measurement results by applying a wavelet analysis method. It is clarified that a fading of unique scale between the scale of fast fading and shadowing can be seen in the low millimeter band. Finally, it is shown that the variation characteristics can be evaluated by taking reflection paths at building walls into account, in addition to the ground waves and LOS paths.

This paper describes characteristics of direct and scattered waves that are extracted from measurement channel data obtained using a 3.35GHz vector channel sounder in an indoor environment. For the scattered waves, a ray number, n, is assigned to each ray in order of the received levels and the relationship between n and the characteristics of each ray such as the received level, delay and azimuth angle of arrival (AOA) are investigated. The distribution of the received level for each n, which is normalized to the received level that is calculated based on free space at each measurement point and includes the received level of all measurement points, is a log normal distribution. Moreover, the median received level of each n of the scattered waves is approximated with two different gradient linear lines as a function of n. Furthermore, the azimuth AOA for the ray of scattered waves whose received level is relatively high is biased in the base station antenna direction and the distribution of the azimuth AOA becomes uniform with a decrease in the received ray level. Finally, a spatio-temporal channel model is proposed based on the above mentioned analysis.

Satellite clusters have been satisfactorily implemented in a number of applications, such as positioning and sensor networks, with the purpose of improving communication system capabilities. However, because the use of clusters requires good management of the resources, those solutions imply new challenges for communication systems. This paper focuses on improving the data management between network elements by considering a network formed by satellite clusters. Satellite clusters work in cooperation to provide real-time and non-real-time services in different footprint areas. This study proposes the adjustable energy consumption access scheme (AECS) as one possible solution response to particular necessities of communication and at the same time, as a way of decreasing the system energy consumption. Energy consumption is a key issue that concerns green network operations and it is directly linked to the cooperation and coordination between network elements. On the other hand, we support the implementation of Optical Inter-Satellite Links (OISL) for communication between cluster elements. The analysis involves the study of energy consumption, transmission delay, specific link margins, bit error rate (BER) and QoS.

The bag-of-words model (BOW) has been extensively adopted by recent human action recognition methods. The pooling operation, which aggregates local descriptor encodings into a single representation, is a key determiner of the performance of the BOW-based methods. However, the spatio-temporal relationship among interest points has rarely been considered in the pooling step, which results in the imprecise representation of human actions. In this paper, we propose a novel pooling strategy named contextual max pooling (CMP) to overcome this limitation. We add a constraint term into the objective function under the framework of max pooling, which forces the weights of interest points to be consistent with their probabilities. In this way, CMP explicitly considers the spatio-temporal contextual relationships among interest points and inherits the positive properties of max pooling. Our method is verified on three challenging datasets (KTH, UCF Sports and UCF Films datasets), and the results demonstrate that our method achieves better results than the state-of-the-art methods in human action recognition.

In this letter, we propose an energy-efficient in-network processing method for continuous grouped aggregation queries in wireless sensor networks. As in previous work, in our method sensor nodes partially compute aggregates as data flow through them to reduce data transferred. Different from other methods, our method considers group information of partial aggregates when sensor nodes forward them to next-hop nodes in order to maximize data reduction by same-group partial aggregation. Through experimental evaluation, we show that our method outperforms the existing methods in terms of energy efficiency.

In this paper, we propose an analog cancellation scheme for multipath self-interference channels in full-duplex wireless orthogonal frequency-division multiplexing (OFDM) systems. The conventional approaches emulate the radio-frequency (RF) self-interference signals by passing the RF transmit signals through delay lines and programmable attenuators. By contrast, our proposed scheme computes the phase-rotated and weighted versions of the baseband transmit signals in the baseband domain, which are simply upconverted to obtain the emulated RF self-interference signals. Numerical results are presented to verify the suppression performance of the proposed scheme.

Accessing a geo-location database is one of the approaches for a secondary user (SU) to obtain the list of available channels for its operation. Channel availability is calculated based on information stored in the geo-location database and information submitted by the SU so that primary users (PU) are protected from harmful interference. The available channel checking process is modeled as a number of intersection tests between the protected contours of PUs and the operation area of the SU regarding to all potential channels. Existing studies indicated that these intersection tests consume time and introduce overhead to the database, especially when the contours or the operation areas are represented by n-polygons and the number of vertices n is a large number. This paper presents a novel method of determining available channels which reduces the number of intersection tests. By submitting SU's preferred channels or the number of channels to be checked to the database, the calculation time and database's load will be reduced significantly. This paper also presents analysis and simulation results of the database workload and the average number of channels obtained per query on different query methods. Suitable query method can be selected based on the number of similar channels in neighbor areas and the maximum number of intersection tests.

This paper describes a method of identifying nonlocal dependencies in incremental parsing. Our incremental parser inserts empty elements at arbitrary positions to generate partial parse trees including empty elements. To identify the correspondence between empty elements and their fillers, our method adapts a hybrid approach: slash feature annotation and heuristic rules. This decreases local ambiguity in incremental parsing and improves the accuracy of our parser.

A novel ROI-based reversible data hiding scheme is proposed for medical images, which is able to hide electronic patient record (EPR) and protect the region of interest (ROI) with tamper localization and recovery. The proposed scheme combines prediction error expansion with the sorting technique for embedding EPR into ROI, and the recovery information is embedded into the region of non-interest (RONI) using histogram shifting (HS) method which hardly leads to the overflow and underflow problems. The experimental results show that the proposed scheme not only can embed a large amount of information with low distortion, but also can localize and recover the tampered area inside ROI.

Square-root Nyquist transmit filtering is typically used in single-carrier (SC) transmission. By changing the filter roll-off factor, the bit-error rate (BER), peak-to-average power ratio (PAPR), and spectrum efficiency (SE) changes, resulting in a tradeoff among these performance indicators. In this paper, assuming SC with frequency-domain equalization (SC-FDE), we design a new transmit filtering based on the minimum variance of instantaneous transmit power (VIP) criterion in order to reduce the PAPR of the transmit signal of SC-FDE. Performance evaluation of SC-FDE using the proposed transmit filtering is done by computer simulation, and shows that the proposed transmit filtering contributes lower transmit PAPR, while there exists only a small degradation in BER performance compared to SC-FDE using square-root Nyquist filtering.

In a patch-based super-resolution algorithm, a low-resolution patch is influenced by surrounding patches due to blurring. We propose to remove this boundary effect by subtracting the blur from the surrounding high-resolution patches, which enables more accurate sparse representation. We demonstrate improved performance through experimentation. The proposed algorithm can be applied to most of patch-based super-resolution algorithms to achieve additional improvement.

Many malware programs emerging from the Internet are compressed and/or encrypted by a wide variety of packers to deter code analysis, thus making it necessary to perform unpacking first. To do this task efficiently, Guo et al. proposed a generic unpacking system named Justin that provides original entry point (OEP) candidates. Justin executes a packed program, and then it extracts written-and-executed points caused by the decryption of the original binary until it determines the OEP has appeared, taking those points as candidates. However, for several types of packers, the system can provide comparatively large sets of candidates or fail to capture the OEP. For more effective generic unpacking, this paper presents a novel OEP detection method featuring two mechanisms. One identifies the decrypting routine by tracking relations between writing instructions and written areas. This is based on the fact that the decrypting routine is the generator for the original binary. In case our method fails to detect the OEP, the other mechanism sorts candidates based on the most likely candidate so that analysts can reach the correct one quickly. With experiments using a dataset of 753 samples packed by 25 packers, we confirm that our method can be more effective than Justin's heuristics, in terms of detecting OEPs and reducing candidates. After that, we also propose a method combining our method with one of Justin's heuristics.

A survey was conducted on the use of ICT by visually impaired people. Among 304 respondents, 81 used smartphones and 44, tablets. Blind people used feature phones at a higher rate and smartphones and tablets at lower rates than people with low vision. The most popular smartphone model was iPhone and the most popular tablet model was iPad. While almost all blind users used the speech output accessibility feature and only a few of them used visual features, low vision users used both visual features such as Zoom, Large text, and Invert colors and speech output at high rates both on smartphones and tablets. The most popular text entry methods were different between smartphones and tablets. For smartphones flick and numeric keypad input were popular among low vision users while voice input was the most popular among blind users. For tablets a software QWERTY keyboard was the most popular among both blind and low vision users. The advantages of smartphones were access to geographical information, quick Web browsing, voice input, and extensibility for both blind and low vision users, object recognition for blind users, and readability for low vision users. Tablets also work as a vision aid for people with low vision. The drawbacks of smartphones and tablets were text entry and touch operation difficulties and inaccessible apps for both blind and low vision users, problems in speech output for blind users, and problems in readability for low vision users. Researchers and makers of operating systems (OS) and apps should assume responsibility for solving these problems.

For robust visual tracking, the main challenges of a subspace representation model can be attributed to the difficulty in handling various appearances of the target object. Traditional subspace learning tracking algorithms neglected the discriminative correlation between different multi-view target samples and the effectiveness of sparse subspace learning. For learning a better subspace representation model, we designed a discriminative graph to model both the labeled target samples with various appearances and the updated foreground and background samples, which are selected using an incremental updating scheme. The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a balance between within-class local manifold and global discriminative information from foreground and background samples. Based on the discriminative graph, we achieved a sparse embedding by using L2,1-norm, which is incorporated to select relevant features and learn transformation in a unified framework. In a tracking procedure, the subspace learning is embedded into a Bayesian inference framework using compound motion estimation and a discriminative observation model, which significantly makes localization effective and accurate. Experiments on several videos have demonstrated that the proposed algorithm is robust for dealing with various appearances, especially in dynamically changing and clutter situations, and has better performance than alternatives reported in the recent literature.

Fault localization is essential for conducting effective program repair. However, preliminary studies have shown that existing fault localization approaches do not take the requirements of automatic repair into account, and therefore restrict the repair performance. To address this issue, this paper presents the first study on designing fault localization approaches for automatic program repair, that is, we propose a fault localization approach using failure-related contexts in order to improve automatic program repair. The proposed approach first utilizes program slicing technique to construct a failure-related context, then evaluates the suspiciousness of each element in this context, and finally transfers the result of evaluation to automatic program repair techniques for performing repair on faulty programs. The experimental results demonstrate that the proposed approach is effective to improve automatic repair performance.

Provable Data Possession (PDP) schemes enable users to efficiently check the integrity of their data in the cloud. Support for massive and dynamic sets of data and adaptability to third-party auditing are two key factors that affect the practicality of existing PDP schemes. We propose a secure and efficient PDP system called IDPA-MF-PDP, by exploiting the characteristics of real-world cloud storage environments. The cost of auditing massive and dynamic sets of data is dramatically reduced by utilizing a multiple-file PDP scheme (MF-PDP), based on the data update patterns of cloud storage. Deployment and operational costs of third-party auditing and information leakage risks are reduced by an auditing framework based on integrated data possession auditors (DPAs), instantiated by trusted hardware and tamper-evident audit logs. The interaction protocols between the user, the cloud server, and the DPA integrate MF-PDP with the auditing framework. Analytical and experimental results demonstrate that IDPA-MF-PDP provides the same level of security as the original PDP scheme while reducing computation and communication overhead on the DPA, from linear the size of data to near constant. The performance of the system is bounded by disk I/O capacity.