Fast proliferation of mobile Internet and high-demand mobile applications necessitates the introduction of different priority classes in next-generation cellular networks. This is especially crucial for efficient use of radio resources in the heterogeneous and virtualized network environments. Despite the fact that many analytical tools have been proposed for capacity and radio resource modelling in cellular networks, only a few of them explicitly incorporate priorities among services. We propose a novel analytical model to analyse the performance of a priority-based cellular CDMA system with finite source population. When the cell load is above a certain level, low-priority calls may be blocked to preserve the quality of service of high-priority calls. The proposed model leads to an efficient closed-form solution that enables fast and very accurate calculation of resource occupancy of the CDMA system and call blocking probabilities, for different services and many priority classes. To achieve them, the system is modelled as a continuous-time Markov chain. We evaluate the accuracy of the proposed analytical model by means of computer simulations and find that the introduced approximation errors are negligible.

In the future Internet of Things/M2M network, enormous amounts of data generated from sensors must be processed and utilized by cloud applications. In recent years, sensor data stream delivery, which collects and sends sensor data periodically, has been attracting great attention. As for sensor data stream delivery, the receivers have different delivery cycle requirements depending on the applications or situations. In this paper, we propose a sensor data stream delivery method to accommodate heterogeneous cycles on the cloud. The proposed method uses distributed hashing to determine relay nodes on the cloud and construct delivery paths autonomously. We evaluate the effectiveness of the proposed method in simulations. The simulation results show that the proposed method halves the maximum load of nodes compared to the baseline methods and achieves high load balancing.

This paper analyzes three passive noise shaping techniques in a SAR ADC. These passive noise shaping techniques can realize 1st and 2nd order noise shaping. These proposed opamp-less noise shaping techniques are realized by charge-redistribution. This means that the proposals maintain the basic architecture and operation principle of a charge-redistribution SAR ADC. Since the proposed techniques work in a passive mode, the proposals have high power efficiency. Meanwhile, the proposed noise shaping SAR ADCs are robust to feature size scaling and power supply reduction. Flicker noise is not introduced into the ADC by passive noise shaping techniques. Therefore, no additional calibration techniques for flicker noise are required. The noise shaping effects of the 1st and 2nd order noise shaping are verified by behavioral simulation results. The relationship between resolution improvement and oversampling rate is also explored in this paper.

A negative capacitor is fabricated using poly(vinylidene fluoride-trifluoroethylene) copolymer and connected in series to an a-IZO TFT. It is experimentally demonstrated that the negative capacitance of the negative capacitor can create steep switching in the a-IZO TFT (e.g., a subthreshold slope change from 342mV/decade to 102mV/decade at room-temperature).

As three dimensional (3D) discrete wavelet transform (DWT) is widely used for high resolution volumetric data compression, and to further improve the performance of lossless coding, the adaptive directional lifting (ADL) structure based on non-separable 3D DWT with a (5,3) filter is proposed in this paper. The proposed 3D DWT has less lifting steps and better prediction performance compared to the existing separable 3D DWT with fixed filter coefficients. It also has compatibility with the conventional DWT defined by the JPEG2000 international standard. The proposed method shows comparable and better results with the non-separable 3D DWT and separable 3D DWT and it is effective for lossless coding of high resolution volumetric data.

Because accurate position information plays an important role in wireless sensor networks (WSNs), target localization has attracted considerable attention in recent years. In this paper, based on target spatial domain discretion, the target localization problem is formulated as a sparsity-seeking problem that can be solved by the compressed sensing (CS) technique. To satisfy the robust recovery condition called restricted isometry property (RIP) for CS theory requirement, an orthogonalization preprocessing method named LU (lower triangular matrix, unitary matrix) decomposition is utilized to ensure the observation matrix obeys the RIP. In addition, from the viewpoint of the positioning systems, taking advantage of the joint posterior distribution of model parameters that approximate the sparse prior knowledge of target, the sparse Bayesian learning (SBL) approach is utilized to improve the positioning performance. Simulation results illustrate that the proposed algorithm has higher positioning accuracy in multi-target scenarios than existing algorithms.

The random deployment of small cell base stations (BSs) causes the coverage areas of neighboring cells to overlap, which increases intercell interference and degrades the system capacity. This paper proposes a new intercell interference management (IIM) scheme to improve the system capacity in multiple-input multiple-output (MIMO) small cell networks. The proposed IIM scheme consists of both an interference cancellation (IC) technique on the receiver side, and a neural network (NN) based power control algorithm for intercell interference coordination (ICIC) on the transmitter side. In order to improve the system capacity, the NN power control optimizes downlink transmit power while IC eliminates interfering signals from received signals. Computer simulations compare the system capacity of the MIMO network with several ICIC algorithms: the NN, the greedy search, the belief propagation (BP), the distributed pricing (DP), and the maximum power, all of which can be combined with IC reception. Furthermore, this paper investigates the application of a multi-layered NN structure called deep learning and its pre-training scheme, into the mobile communication field. It is shown that the performance of NN is better than that of BP and very close to that of greedy search. The low complexity of the NN algorithm makes it suitable for IIM. It is also demonstrated that combining IC and sectorization of BSs acquires high capacity gain owing to reduced interference.

There is a strong demand to enjoy broadband and stable Internet connectivity not only in office and the home but also in high-speed train. Several systems are providing high-speed train with Internet connectivity using various technologies such as leaky coaxial cable (LCX), Wi-Fi, and WiMAX. However, their actual throughputs are less than 2Mbps. We developed a free-space optical (FSO) communication transceiver called LaserTrainComm2014 that achieves the throughput of 1 Gbps between the ground and a train. LaserTrainComm2014 employs a high-speed image sensor for coarse tracking and a quadrant photo-diode (QPD) for accurate tracking. Since the image captured by the high-speed image sensor has several types of noise, image processing is necessary to detect the beacon light of the other LaserTrainComm2014. As a result of field experiments in a vehicle test course, LaserTrainComm2014 achieves handover time of 21 milliseconds (ms) in the link layer at the speed of 60km/h. Even if the network layer signaling takes time of 10 milliseconds, the total communication disruption time due to handover is short enough to provide passengers with Internet connectivity for live streaming Internet applications such as YouTube, Internet Radio, and Skype.

The likelihood-ratio based score level fusion (LR-based fusion) scheme has attracted much attention, since it maximizes accuracy if a log-likelihood ratio (LLR) is accurately estimated. In reality, it can happen that a user cannot input some query samples due to temporary physical conditions such as injuries and illness. It can also happen that some modalities tend to cause false rejection (i.e. the user is a “goat” for these modalities). The LR-based fusion scheme can handle these situations by setting LLRs corresponding to missing query samples to 0. In this paper, we refer to such a mode as a “modality selection mode”, and address an issue of accuracy in this mode. Specifically, we provide the following contributions: (1) We firstly propose a “modality selection attack”, in which an impostor inputs only query samples whose LLRs are more than 0 (i.e. takes an optimal strategy) to impersonate others. We also show that the impostor can perform this attack against the SPRT (Sequential Probability Ratio Test)-based fusion scheme, which is an extension of the LR-based fusion scheme to a sequential fusion scenario. (2) We secondly consider the case when both genuine users and impostors take this optimal strategy, and show that the overall accuracy in this case is “worse” than the case when they input all query samples. More specifically, we prove that the KL (Kullback-Leibler) divergence between a genuine distribution of integrated scores and an impostor's one, which can be compared with password entropy, is smaller in the former case. We also show to what extent the KL divergence losses for each modality. (3) We finally evaluate to what extent the overall accuracy becomes worse using the NIST BSSR1 Set 2 and Set 3 datasets, and discuss directions of multibiometric applications based on the experimental results.

Coordinate interleaved orthogonal design (CIOD) using four transmit antennas provides full diversity, full rate (FDFR) properties with low decoding complexity. However, the constellation expansion due to the coordinate interleaving of the rotated constellation results in peak to average power ratio (PAPR) increase. In this paper, we propose two signal constellation design methods which have low PAPR. In the first method we propose a signal constellation by properly selecting the signal points among the expanded square QAM constellation points, based on the co-prime interleaving of the first coordinate signal. We design a regular interleaving pattern so that the coordinate distance product (CPD) after the interleaving becomes large to get the additional coding gain. In the other method we propose a novel constellation with low PAPR based on the clipping of the rotated square QAM constellation. Our proposed signal constellations show much lower PAPR than the ordinary rotated QAM constellations for CIOD.

Single-carrier (SC) transmission with space-time block coded (STBC) transmit diversity can achieve good bit error rate (BER) performance. However, in a high mobility environment, the STBC codeword orthogonality is distorted and as consequence, the BER performance is degraded by the interference caused by the orthogonality distortion of STBC codeword. In this paper, we proposed a novel frequency-domain equalization (FDE) for SC-STBC transmit diversity in doubly selective fading channel. Multiple FDE weight matrices, each associated with a different code block, are jointly optimized based on the minimum mean square error (MMSE) criterion taking into account not only channel frequency variation but also channel time variation over the STBC codeword. Computer simulations confirm that the proposed robust FDE achieves BER performance superior to conventional FDE, which was designed based on the assumption of a quasi-static fading.

We propose a low-noise and low-power dynamic comparator with an offset calibration circuit for Low-Power ADCs. The proposed comparator equips the control circuit in order to switching the comparison accuracy and the current consumption. When high accuracy is not required, current consumption is reduced by allowing the noise increase. Compared with a traditional dynamic comparator, the proposed architecture reduced the current consumption to 78% at 100MHz operating and 1.8V supply voltage. Furthermore, the offset voltage is corrected with minimal current consumption by controlling the on/off operation of the offset calibration circuit.

In recent years, the need to build solid state drive (SSD)-based cloud storage systems has been increasing in order to process the big data generated by lots of Internet of Things devices and Internet users. Because these kinds of cloud systems require high performance and reliable storage, the use of flash-based Redundant Array of Independent Disks (RAID) will increase. But in flash-based RAID storage, parity data must be updated with every data write operation, which can more quickly overwhelm SSD's lifespan. To solve this problem, this letter proposes parity data deduplication for OpenStack cloud storage systems using an all flash array. Unlike the traditional data deduplication method, it only removes parity data, which will be stored in the parity disks of the all flash array. Experiments show that the proposed parity data deduplication method can efficiently reduce the number of parity data write operations, compared to the traditional data deduplication method.

The information maximization (Infomax) based on information entropy theory is a class of methods that can be used to blindly separate the sources. Torkkola applied the Infomax criterion to blindly separate the mixtures where the sources have been delayed with respect to each other. Compared to the frequency domain methods, this time domain method has simple adaptation rules and can be easily implemented. However, Torkkola's method works only in the real valued field. In this letter, the Infomax for blind separation of the delayed sources is extended to the complex case for processing of complex valued signals. Firstly, based on the gradient ascent the adaptation rules for the parameters of the unmixing network are derived and the steps of algorithm are given. Then, a measurement matrix is constructed to evaluate the separation performance. The results of computer experiment support the extended algorithm.

This paper proposes a new class of Hilbert pairs of almost symmetric orthogonal wavelet bases. For two wavelet bases to form a Hilbert pair, the corresponding scaling lowpass filters are required to satisfy the half-sample delay condition. In this paper, we design simultaneously two scaling lowpass filters with the arbitrarily specified flat group delay responses at ω=0, which satisfy the half-sample delay condition. In addition to specifying the number of vanishing moments, we apply the Remez exchange algorithm to minimize the difference of frequency responses between two scaling lowpass filters, in order to improve the analyticity of complex wavelets. The equiripple behavior of the error function can be obtained through a few iterations. Therefore, the resulting complex wavelets are orthogonal and almost symmetric, and have the improved analyticity. Finally, some examples are presented to demonstrate the effectiveness of the proposed design method.

Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all points covered by r is maximized. In this paper, we address the problem of processing MaxRS queries over road network databases and propose two new external memory methods. Through a set of simulations, we evaluate the performance of the proposed methods.

We propose a kernel-based quadratic classification method based on kernel principal component analysis (KPCA). Subspace methods have been widely used for multiclass classification problems, and they have been extended by the kernel trick. However, there are large computational complexities for the subspace methods that use the kernel trick because the problems are defined in the space spanned by all of the training samples. To reduce the computational complexity of the subspace methods for multiclass classification problems, we extend Oja's averaged learning subspace method and apply a subset approximation of KPCA. We also propose an efficient method for selecting the basis vectors for this. Due to these extensions, for many problems, our classification method exhibits a higher classification accuracy with fewer basis vectors than does the support vector machine (SVM) or conventional subspace methods.

In this letter, we propose a novel texture descriptor that takes advantage of an anisotropic neighborhood. A brand new encoding scheme called Reflection and Rotation Invariant Uniform Patterns (rriu2) is proposed to explore local structures of textures. The proposed descriptor is called Oriented Local Binary Patterns (OLBP). OLBP may be incorporated into other varieties of Local Binary Patterns (LBP) to obtain more powerful texture descriptors. Experimental results on CUReT and Outex databases show that OLBP not only significantly outperforms LBP, but also demonstrates great robustness to rotation and illuminant changes.

Enforcing access control policies in Information-Centric Networking (ICN) is difficult due to there being multiple copies of contents in various network locations. Traditional Access Control List (ACL)-based schemes are ill-suited for ICN, because all potential content distribution servers should have an identical access control policy or they should contact a centralized ACL server whenever their contents are accessed by consumers. To address these problems, we propose a distributed capability access control scheme for ICN. The proposed scheme is composed of an internal capability and an external capability. The former is included in the content and the latter is added to a request message sent from the consumer. The content distribution servers can validate the access right of the consumer through the internal and external capabilities without contacting access control policies. The proposed model also enhances the privacy of consumers by keeping the content name and consumer identification anonymous. The performance analysis and implementation show that the proposed scheme is feasible and more efficient than other access control schemes.

Mixed-signal integrated circuit design and simulation highly rely on behavioral models of circuit blocks. Such models are used for the validation of design specification, optimization of system topology, and behavioral synthesis using a description language, etc. However, automatic behavioral model generation is still in its early stages; in most scenarios designers are responsible for creating behavioral models manually, which is time-consuming and error prone. In this paper an automatic behavioral model generation method for switched-capacitor (SC) integrator is proposed. This technique is based on symbolic circuit modeling with approximation, by which parametric behavioral integrator model can be generated. Such parametric models can be used in circuit design subject to severe process variational. It is demonstrated that the automatically generated integrator models can accurately capture process variation effects on arbitrarily selected circuit elements; furthermore, they can be applied to behavioral simulation of SC Sigma-Delta modulators (SDMs) with acceptable accuracy and speedup. The generated models are compared to a recently proposed manually generated behavioral integrator model in several simulation settings.