In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. One is within each frame and the other is induced from the similarity between adjacent frames. The latter further means not only in the image domain, but also in a sparsifying transformed domain. Since the cost function we define is convex, we can find the optimal solution using a convex optimization technique with small computational cost. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.

This contribution presents and analyzes the statistical regularity related to the noise power spectrum series and the speech spectrum series. It also undertakes a thorough inquiry of the quasi-Gaussian distributed power spectrum series obtained using the radical root transformation. Consequently, a noise-estimation algorithm is proposed for speech enhancement. This method is effective for separating the noise power spectrum from the noisy speech power spectrum. In contrast to standard noise-estimation algorithms, the proposed method requires no speech activity detector. It was confirmed to be conceptually simple and well suited to real-time implementations. Practical experiment tests indicated that our method is preferred over previous methods.

This paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and correcting preposition errors. Their most distinct advantage is that they can correct errors with feedback messages explaining why the preposition is erroneous. This paper proposes a method for automatically generating them by comparing learner and native corpora. Experiments show (i) automatically generated error case frames achieve a performance comparable to previous methods; (ii) error case frames are intuitively interpretable and manually modifiable to improve them; (iii) feedback messages provided by error case frames are effective in language learning assistance. Considering these advantages and the fact that it has been difficult to provide feedback messages using automatically generated rules, error case frames will likely be one of the major approaches for preposition error correction.

In order to realize an Internet-of-Things (IoT) with tiny sensors integrated in our buildings, our clothing, and the public spaces, battery lifetime and battery size remain major challenges. Power reduction in IoT sensor nodes is determined by both sleep mode as well as active mode contributions. A power state machine, at the system level, is the key to achieve ultra-low average power consumption by alternating the system between active and sleep modes efficiently. While, power consumption in the active mode remains dominant, other power contributions like for timekeeping in standby and sleep conditions are becoming important as well. For example, non-conventional critical blocks, such as crystal oscillator (XO) and resistor-capacitor oscillator (RCO) become more crucial during the design phase. Apart from power reduction, low-voltage operation will further extend the battery life. A 2.4GHz multi-standard radio is presented, as a test case, with an average power consumption in the µW range, and state-of-the-art performance across a voltage supply range from 1.2V to 0.9V.

This paper presents a thin, compact beacon transmitter operating without needing battery replacement by using a photovoltaic (PV) film harvester. The beacon is formed of a power-control circuit (PCC) that can monitor small amounts of power from the harvester and properly control mode switching at low-power consumption. This leads to the realization of a maintenance-free beacon requiring no battery replacement. The beacon prototype is 55×20×2 mm in size and has a PV cell of 3 cm2. It allows a start-up operation from just 44-lux illuminance. The PV area required for the operation can be 1.7 times smaller than that of conventional beacons, thanks to the current saving with appropriate sequential control of the PCC. Since the beacon makes operation possible in emergency stairs, underground passages and other dark places, the application field for Internet of things (IoT) services can be expanded. Furthermore, a beacon equipped with a secondary battery (BSB: Beacon with Secondary Battery) can be configured by adding a charge-discharge power monitoring circuit. The BSB transmits an advertising packet during the daytime while charging surplus power, and works using the stored power during the night; this results in a continuous operation for one week with one transmission every 3 seconds even at 0-lux illuminance. Without developing a new radiofrequency chip or module, commercial low-power devices can be easily adjusted depending on the application by adding appropriate power-control circuits. We are convinced that this design scheme will be effective as a rapid design proposal for IoT services.

In this paper, an accurate experimental noise model to improve the EEHEMT nonlinear model using the Verilog-A language in Agilent ADS is presented for the first time. The present EEHEMT model adopts channel noise to model the noise behavior of pseudomorphic high electron mobility transistor (pHEMT). To enhance the accuracy of the EEHEMT noise model, we add two extra noise sources: gate shot noise and induced gate noise current. Here we demonstrate the power spectral density of the channel noise Sid and gate noise Sig versus gate-source voltage for 0.25 µm pHEMT devices. Additionally, the related noise source parameters, i.e., P, R, and C are presented. Finally, we compare four noise parameters between the simulation and model, and the agreement between the measurement and simulation results shows that this proposed approach is dependable and accurate.

To ensure secure mobile communication, the communicating entities must know their mutual identities. The entities which need to be identified in a mobile communication system are mobile devices and the network. Third Generation Partnership Project (3GPP) has specified Evolved Packet System Authentication and Key Agreement (EPS AKA) procedure for the mutual authentication of user and the Long Term Evolution (LTE) network. EPS AKA certainly overcomes most of the vulnerabilities in the Global System for Mobile Communications (GSM) and Universal Mobile Telecommunication System (UMTS) access procedures. However, the LTE access procedure still has security weaknesses against some of the sophisticated security threats, such as, Denial-of-Service (DoS) attacks, Man-in-the-Middle (MitM) attacks, rogue base station attacks and fails to ensure privacy protection for some of the important parameters. This paper proposes an improved security framework for the LTE access procedure by ensuring the confidentiality protection of International Mobile Subscriber Identity (IMSI) and random-challenge RAND. Also, our proposed system is designed to reduce the impact of DoS attacks which try to overwhelm the network with useless computations. We use a one-time shared key with a short lifetime between the UE and MME to protect IMSI and RAND privacy. Finally, we explore the parameters design for the proposed system which leads to satisfy the requirements imposed on computational load and latency as well as security strength.

Long-distance wireless local area networks (WLANs) are the key enablers of wide-area and low-cost access networks in rural areas. In a WLAN, the long propagation delay between an access point (AP) and stations (STAs) significantly degrades the throughput and creates a throughput imbalance because the delay causes unexpected frame collisions. This paper summarizes the problems caused in the medium access control (MAC) mechanism of the WLAN by a long propagation delay. We propose a MAC protocol for solving the delay-induced throughput degradation and the throughput imbalance between the uplink and the downlink in WLANs to address these problems. In the protocol, the AP extends NAV duration of CTS frame to protect an ACK frame and transmits its data frame to avoid delay induced frame collisions by piggybacking on the ACK frame transmission. We also provide a throughput model for the proposed protocol based on the Bianchi model. A numerical analysis using the proposed throughput model and simulation evaluation demonstrate that the proposed protocol increases the system throughput by 150% compared with that obtained using the conventional method, and the uplink throughput can be increased to the same level as the downlink throughput.

The Seebeck coefficient of Si wire co-doped with P and Ga atoms is investigated for applying thermoelectric devices. The observed Seebeck coefficient is closed to the theoretical values of electronic part of Seebeck coefficient due to the electronic transport. From the estimation of phonon scattering processes, it is found that the phonon-drag contribution to the Seebeck coefficient in co-doped Si wire is mainly governed by the phonon-boundary scattering.

High Efficiency Video Coding is a new video coding standard after H.264/AVC. By introducing a flexible coding unit, which can be recursively divided from 64×64 to 8×8 blocks in a Quadtree-Structure, HEVC achieves significantly higher coding efficiency than the previous standards. With the flexible CU structure, HEVC can effectively adapt to highly varying contents with a smaller CU or to flat contents with a larger CU, making it suitable for applications from mobile video to super high definition television. On the other hand, CU division does incur high computational cost for HEVC. In this paper, we propose a simple and fast CU division algorithm by using only a subset of pixels to determine when CU division happens. Experiment results show that our algorithm can achieve prediction quality close to HEVC Test Model with much lower computational cost.

High Efficiency Video Coding (HEVC) is the latest coding standard. Compared with Advanced Video coding (H.264/AVC), HEVC offers about a 50% bitrate reduction at the same reconstructed video quality. However, this new coding standard leads to enormous computational complexity, which makes it difficult to encode video in real time. Therefore, in this paper, aiming at the high complexity of intra coding in HEVC, a new fast coding unit (CU) splitting algorithm is proposed based on the decision tree. Decision tree, as a method of machine learning, can be designed to determine the size of CUs adaptively. Here, two significant features, Just Noticeable Difference (JND) values and coding bits of each CU can be extracted to train the decision tree, according to their relationships with the CUs' partitions. The experimental results have revealed that the proposed algorithm can save about 34% of time, on average, with only a small increase of BD-rate under the “All_Intra” setting, compared with the HEVC reference software.

The phonon-drag contribution to the Seebeck coefficient (Sph) for p-type Si, Ge and Si1-xGex is investigated for thermoelectric applications. The Sph in Si and Ge is found to mainly determined by the phonon velocity, phonon mean free path and carrier mobility associated with acoustic deformation potential scattering. Moreover, the Sph in Si1-xGex is predictable by the above-mentioned material parameters interpolated with those in Si and Ge.

Effective communication strategies with a properly designed source precoding matrix (PM) and a properly designed relay beamforming matrix (BM) can significantly improve the spectral efficiency of multiple-input multiple-output (MIMO) relaying broadcast channels (RBCs). In the present paper, we first propose a general communication scheme with non-regenerative relay that can overcome the half-duplex relay constraint of the general MIMO-RBC. Based on the proposed scheme, the robust source PM and relay BM are designed for imperfect channel state information at the transmitter (CSIT). In contrast to the conventional non-regenerative relaying communication scheme for the MIMO-RBC, in the proposed scheme, the source can send information continuously to the relay and users during two phases. Furthermore, in conjunction with the advanced precoding strategy, the proposed scheme can achieve a full-degree-of-freedom (DoF) MIMO-RBC with that each entry in the related channel matrix is considered to an i.i.d. complex Gaussian variable. The robust source PM and relay BM designs were investigated based on both throughput and fairness criteria with imperfect CSIT. However, solving the problems associated with throughput and fairness criteria for the robust source PM and relay BM designs is computationally intractable because these criteria are non-linear and non-convex. In order to address these difficulties, we first set up equivalent optimization problems based on a tight lower bound of the achievable rate. We then decompose the equivalent throughput problem into several decoupled subproblems with tractable solutions. Finally, we obtain the suboptimal solution for the throughput problem by an alternating optimization approach. We solve the fairness problem by introducing an adjusted algorithm according to the throughput problem. Finally, we demonstrate that, in both cases of throughput and fairness criteria, the proposed relaying communication scheme with precoding algorithms outperforms existing methods.

In this paper, the posterior matching scheme proposed by Shayevits and Feder is extended to the Gaussian broadcast channel with feedback, and the error probabilities and achievable rate region are derived for this coding strategy by using the iterated random function theory. A variant of the Ozarow-Leung code for the general two-user broadcast channel with feedback can be realized as a special case of our coding scheme. Furthermore, for the symmetric Gaussian broadcast channel with feedback, our coding scheme achieves the linear-feedback sum-capacity like the LQG code and outperforms the Kramer code.

This paper provides a new method to correct annotation errors in a treebank. The previous error correction method constructs a pseudo parallel corpus where incorrect partial parse trees are paired with correct ones, and extracts error correction rules from the parallel corpus. By applying these rules to a treebank, the method corrects errors. However, this method does not achieve wide coverage of error correction. To achieve wide coverage, our method adopts a different approach. In our method, we consider that if an infrequent pattern can be transformed to a frequent one, then it is an annotation error pattern. Based on a tree mining technique, our method seeks such infrequent tree patterns, and constructs error correction rules each of which consists of an infrequent pattern and a corresponding frequent pattern. We conducted an experiment using the Penn Treebank. We obtained 1,987 rules which are not constructed by the previous method, and the rules achieved good precision.

As a hot topic of speech signal processing, speech emotion recognition methods have been developed rapidly in recent years. Some satisfactory results have been achieved. However, it should be noted that most of these methods are trained and evaluated on the same corpus. In reality, the training data and testing data are often collected from different corpora, and the feature distributions of different datasets often follow different distributions. These discrepancies will greatly affect the recognition performance. To tackle this problem, a novel corpus-invariant discriminant feature representation algorithm, called transfer discriminant analysis (TDA), is presented for speech emotion recognition. The basic idea of TDA is to integrate the kernel LDA algorithm and the similarity measurement of distributions into one objective function. Experimental results under the cross-corpus conditions show that our proposed method can significantly improve the recognition rates.

Existing studies on transportation mode detection from global positioning system (GPS) trajectories mainly adopt handcrafted features. These features require researchers with a professional background and do not always work well because of the complexity of traffic behavior. To address these issues, we propose a model using a sparse autoencoder to extract point-level deep features from point-level handcrafted features. A convolution neural network then aggregates the point-level deep features and generates a trajectory-level deep feature. A deep neural network incorporates the trajectory-level handcrafted features and the trajectory-level deep feature for detecting the users' transportation modes. Experiments conducted on Microsoft's GeoLife data show that our model can automatically extract the effective features and improve the accuracy of transportation mode detection. Compared with the model using only handcrafted features and shallow classifiers, the proposed model increases the maximum accuracy by 6%.

Content extraction from deep Web pages has received great attention in recent years. However, the increasingly complicated HTML structure of Web documents makes it more difficult to recognize the data records by only analyzing the HTML source code. In this paper, we propose a method named LTDE to extract data records from a deep Web page. Instead of analyzing the HTML source code, LTDE utilizes the visual features of data records in deep Web pages. A Web page is considered as a finite set of visual blocks. The data records are the visual blocks that have similar layout. We also propose a pattern recognizing method named layout tree to cluster the similar layout visual blocks. The weight of all clusters is calculated, and the visual blocks in the cluster that has the highest weight are chosen as the data records to be extracted. The experiment results show that LTDE has higher effectiveness and better robustness for Web data extraction compared to previous works.

We regard a De Bruijn sequence of order n as a bijection on $mathbb{F}_2^n$ and consider the transition mappings between them. It is shown that there are only two conjugate transformations that always transfer De Bruijn sequences to De Bruijn sequences.

This study introduces large-scale field experiments of VoiceTra, which is the world's first speech-to-speech multilingual translation application for smart phones. In the study, approximately 10 million input utterances were collected since the experiments commenced. The usage of collected data was analyzed and discussed. The study has several important contributions. First, it explains system configuration, communication protocol between clients and servers, and details of multilingual automatic speech recognition, multilingual machine translation, and multilingual speech synthesis subsystems. Second, it demonstrates the effects of mid-term system updates using collected data to improve an acoustic model, a language model, and a dictionary. Third, it analyzes system usage.