This study investigates a real-time joint channel and hyperparameter estimation method for orthogonal frequency division multiplexing mobile communications. The channel frequency response of the pilot subcarrier and its fixed hyperparameters (such as channel statistics) are estimated using a Liu and West filter (LWF), which is based on the state-space model and sequential Monte Carlo method. For the first time, to our knowledge, we demonstrate that the conventional LWF biases the hyperparameter due to a poor estimate of the likelihood caused by overfitting in noisy environments. Moreover, this problem cannot be solved by conventional smoothing techniques. For this, we modify the conventional LWF and regularize the likelihood using a Kalman smoother. The effectiveness of the proposed method is confirmed via numerical analysis. When both of the Doppler frequency and delay spread hyperparameters are unknown, the conventional LWF significantly degrades the performance, sometimes below that of least squares estimation. By avoiding the hyperparameter estimation failure, our method outperforms the conventional approach and achieves good performance near the lower bound. The coding gain in our proposed method is at most 10 dB higher than that in the conventional LWF. Thus, the proposed method improves the channel and hyperparameter estimation accuracy. Derived from mathematical principles, our proposal is applicable not only to wireless technology but also to a broad range of related areas such as machine learning and econometrics.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.

This paper proposes an opamp-free solution to implement single-phase-clock controlled noise shaping in a SAR ADC. Unlike a conventional noise shaping SAR ADC, the proposal realizes noise shaping by charge redistribution, which is a passive technique. The passive implementation has high power efficiency. Meanwhile, since the proposal maintains the basic architecture and operation method of a traditional SAR ADC, it retains all the advantages of a SAR ADC. Furthermore, noise shaping helps to improve the performance of SAR ADC and relaxes its non-ideal effects. Designed in a 65-nm CMOS technology, the prototype realizes 58-dB SNDR based on an 8-bit C-DAC at 50-MS/s sampling frequency. It consumes 120.7-µW power from a 0.8-V supply and achieves a FoM of 14.8-fJ per conversion step.

An online version of convolutive non-negative sparse coding (CNSC) with the generalized Kullback-Leibler (K-L) divergence is proposed to adaptively learn spectral-temporal bases from speech streams. The proposed scheme processes training data piece-by-piece and incrementally updates learned bases with accumulated statistics to overcome the inefficiency of its offline counterpart in processing large scale or streaming data. Compared to conventional non-negative sparse coding, we utilize the convolutive model within bases, so that each basis is capable of describing a relatively long temporal span of signals, which helps to improve the representation power of the model. Moreover, by incorporating a voice activity detector (VAD), we propose an unsupervised enhancement algorithm that updates the noise dictionary adaptively from non-speech intervals. Meanwhile, for the speech intervals, one can adaptively learn the speech bases by keeping the noise ones fixed. Experimental results show that the proposed algorithm outperforms the competing algorithms substantially, especially when the background noise is highly non-stationary.

Analyzing the schedulability of hierarchical real-time systems is difficult because of the systems' complex behavior. It gets more complicated when shared resources or dependencies among tasks are included. This paper introduces a framework based on UPPAAL that can analyze the schedulability of hierarchical real-time systems.

Oblivious RAM is a technique for hiding the access patterns between a client and an untrusted server. However, current ORAM algorithms incur large communication or storage overhead. We propose a novel ORAM construction using a matrix logical structure for server storage where a client downloads blocks from each row, choosing the column randomly to hide the access pattern. Both a normal construction and recursive construction, where a position map normally stored on the client is also stored on the server, are presented. We show our matrix ORAM achieves constant bandwidth cost for the normal construction, uses similar storage to the existing Path ORAM, and improves open the bandwidth cost compared to Path ORAM under certain conditions in the recursive construction.

Many research efforts are being focused upon the design of dynamic Inter-Cell Interference Coordination (ICIC) schemes for macrocell/picocell heterogeneous networks employing Cell Range Expansion (CRE). In order to protect the expanded Pico User Equipments (ePUEs) located in the CRE region from severe Macro Base Station (MBS) interference in downlink, the conventional methods reduce the transmit power of the MBS in the Almost Blank Subframes (ABSs), where ePUEs can be scheduled. However, this severely limits the amount of usable resources/power for the MBS as compared to Resource Block (RB)-based dynamic allocation. Instead, we propose a self-organized RB-based dynamic resource allocation method. Based on the proposed partial Channel State Information (CSI) sharing, the MBS obtains ePUEs' CSI and predicts their RB allocation. Then, the MBS reduces its transmit power in RBs where the ePUEs' allocation probability is estimated to be high. The simulation results show that the proposed scheme achieves excellent macrocell/picocell performance trade-offs, even when taking into account the overhead increase due to the partial CSI sharing.

In this paper, we study the problem of a Boolean function can be represented as the sum of two bent functions. This problem was recently presented by N. Tokareva when studying the number of bent functions [27]. Firstly, several classes of functions, such as quadratic Boolean functions, Maiorana-MacFarland bent functions, many partial spread functions etc, are proved to be able to be represented as the sum of two bent functions. Secondly, methods to construct such functions from low dimension ones are also introduced. N. Tokareva's main hypothesis is proved for n≤6. Moreover, two hypotheses which are equivalent to N. Tokareva's main hypothesis are presented. These hypotheses may lead to new ideas or methods to solve this problem. Finally, necessary and sufficient conditions on the problem when the sum of several bent functions is again a bent function are given.

We investigate through simulation simultaneous linear and nonlinear impairments using a realistic reconfigurable optical add drop multiplexer (ROADM) model while considering optical filtering and in-band coherent crosstalk at each ROADM and the nonlinear interfering effects from neighbor superchannels with the QPSK or 16QAM modulation format.

This paper describes a numerical assessment methodology of pacemaker EMI triggered by HF-band wireless power transfer system. By using three dimensional full-wave numerical simulation based on finite element method, interference voltage induced at the connector of the pacemaker inside the phantom that is used for in-vitro EMI assessment is obtained. Simulated example includes different exposure scenarios in order to estimate the maximum interference voltage.

An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.

In interactive audio services, users can render audio objects rather freely to match their desires and the spatial audio object coding (SAOC) scheme is fairly good both in the sense of bitrate and audio quality. But rather perceptible audio quality degradation can occur when an object is suppressed or played alone. To complement this, the SAOC scheme with Two-Step Coding (SAOC-TSC) was proposed. But the bitrate of the side information increases two times compared to that of the original SAOC due to the bitrate needed for the residual coding used to enhance the audio quality. In this paper, an efficient residual coding method of the SAOC-TSC is proposed to reduce the side information bitrate without audio quality degradation or complexity increase.

As semiconductor technologies have advanced, the reliability problem caused by soft-errors is becoming one of the serious issues in LSIs. Moreover, multiple component errors due to single soft-errors also have become a serious problem. In this paper, we propose a method to synthesize multiple component soft-error tolerant application-specific datapaths via high-level synthesis. The novel feature of our method is speculative resource sharing between the retry parts and the secondary parts for time overhead mitigation. A scheduling algorithm using a special priority function to maximize speculative resource sharing is also an important feature of this study. Our approach can reduce the latency (schedule length) in many applications without deterioration of reliability and chip area compared with conventional datapaths without speculative resource sharing. We also found that our method is more effective when a computation algorithm possesses higher parallelism and a smaller number of resources is available.

The 60 GHz band compact-range communication is very promising for short-time, short distance communication. Unfortunately, due to the short wavelengths in this frequency band the shadowing effects caused by human bodies, furniture, etc are severe and need to be modeled properly. The numerical methods like the finite-difference time-domain method (FDTD), the finite-element method (FEM), the method of moments (MoM) are unable to compute the field scattered by large objects due to their excessive time and memory requirements. Ray-based approaches like the geometrical theory of diffraction (GTD), uniform geometrical theory of diffraction (UTD), uniform asymptotic theory of diffraction (UAT) are effective and popular solutions but suffer from computation of corner-diffracted field, field at the caustics. Fresnel zone number (FZN) adopted modified edge representation (MER) equivalent edge current (EEC) is an accurate and fast high frequency diffraction technique which expresses the fields in terms of line integration. It adopts distances, rather than the angles used in GTD, UTD or UAT but still provides uniform and highly accurate fields everywhere including geometrical boundaries. Previous work verified this method for planar scatterers. In this work, FZN MER EEC is used to compute field distribution in the millimeter-wave compact range communication in the presence of three dimensional scatterers, where shadowing effects rather than multi-path dominate the radio environments. First, circular cylinder is disintegrated into rectangular plate and circular disks and then FZN MER is applied along with geodesic path loss. The dipole wave scattering from perfectly conducting circular cylinder is discussed as numerical examples.

Most unsupervised video segmentation algorithms are difficult to handle object extraction in dynamic real-world scenes with large displacements, as foreground hypothesis is often initialized with no explicit mutual constraint on top-down spatio-temporal coherency despite that it may be imposed to the segmentation objective. To handle such situations, we propose a multiscale saliency flow (MSF) model that jointly learns both foreground and background features of multiscale salient evidences, hence allowing temporally coherent top-down information in one frame to be propagated throughout the remaining frames. In particular, the top-down evidences are detected by combining saliency signature within a certain range of higher scales of approximation coefficients in wavelet domain. Saliency flow is then estimated by Gaussian kernel correlation of non-maximal suppressed multiscale evidences, which are characterized by HOG descriptors in a high-dimensional feature space. We build the proposed MSF model in accordance with the primary object hypothesis that jointly integrates temporal consistent constraints of saliency map estimated at multiple scales into the objective. We demonstrate the effectiveness of the proposed multiscale saliency flow for segmenting dynamic real-world scenes with large displacements caused by uniform sampling of video sequences.

As the semiconductor technology continues to develop, hundreds of cores will be deployed on a single die in the future Chip-Multiprocessors (CMPs) design. Three-Dimensional Network-on-Chips (3D NoCs) has become an attractive solution which can provide impressive high performance. An efficient and deadlock-free routing algorithm is a critical to achieve the high performance of network-on-chip. Traditional methods based on deterministic and turn model are deadlock-free, but they are unable to distribute the traffic loads over the network. In this paper, we propose an efficient, adaptive and deadlock-free algorithm (EAR) based on a novel routing selection strategy in 3D NoC, which can distribute the traffic loads not only in intra-layers but also in inter-layers according to congestion information and path diversity. Simulation results show that the proposed method achieves the significant performance improvement compared with others.

A novel rendering algorithm with a best-matching patch is proposed to address the noise artifacts associated with Monte Carlo renderings. First, in the sampling stage, the representative patch is selected through a modified patch shift procedure, which gathers homogeneous pixels together to stay clear of the edges. Second, each pixel is filtered over a discrete set of filters, where the range kernel is computed using the selected patches. The difference between the selected patch and the filtered value is used as the pixel error, and the single filter that returns the smallest estimated error is chosen. In the reconstruction stage, pixel colors are combined with features of depth, normal and texture to form a cross bilateral filter, which highly preserves scene details while effectively removing noise. Finally, a heuristic metric is calculated to allocate additional samples in difficult regions. Compared with state-of-the art methods, the proposed algorithm performs better both in visual image quality and numerical error.

Buffer management and delivery latency in various networks have been extensively studied. However, little work has considered the condition in which the traffic exhibits interpacket dependency, a common occurrence with many applications. Furthermore, the existing work related to such traffic mainly focuses on maximizing goodput and little attention has been paid to delivery latency. This paper concentrates on the delivery latency minimization problem for streaming data with packet dependencies. A novel optimization model is proposed to describe the aforementioned problem and the theoretical lower bound for delivery latency is deduced. Based on this model, a plain buffer management (PBM) algorithm is applied to the implementation of the buffer scheduling process. Afterwards, we improve the PBM algorithm under the guidance of a heuristic idea and put forward an optimal buffer management greedy (OBMG) algorithm. Experiments demonstrate that the OBMG algorithm outperforms the currently best known online (BKO) algorithm as it decreases the average delivery latency by 35.6%. In some cases, delivery latency obtained from the OBMG algorithm can be quite close to the theoretical lower bound. In addition, the OBMG algorithm can reduce CPU computational overhead by more than 12% in comparison to the BKO algorithm.

In this paper, a multiple-object tracking approach in large-scale scene is proposed based on visual sensor network. Firstly, the object detection is carried out by extracting the HOG features. Then, object tracking is performed based on an improved particle filter method. On the one hand, a kind of temporal and spatial dynamic model is designed to improve the tracking precision. On the other hand, the cumulative error generated from evaluating particles is eliminated through an appearance model. In addition, losses of the tracking will be incurred for several reasons, such as occlusion, scene switching and leaving. When the object is in the scene under monitoring by visual sensor network again, object tracking will continue through object re-identification. Finally, continuous multiple-object tracking in large-scale scene is implemented. A database is established by collecting data through the visual sensor network. Then the performances of object tracking and object re-identification are tested. The effectiveness of the proposed multiple-object tracking approach is verified.

SDN (Software-Defined Networking) enables software applications to program individual network devices dynamically and therefore control the behavior of the network as a whole. Incomplete programming and/or inconsistency with the network policy of SDN software applications may lead to verification issues. The objective of this paper is to describe the formal modeling that uses the process algebra called pACSR and then suggest a method to verify the firewall application running on top of the SDN controller. The firewall rules are translated into a pACSR process which acts as the specification, and packet's behaviors in SDN are also translated to a pACSR process which is a role as the implementation. Then we prove the correctness by checking whether the parallel composition of two pACSR processes is deadlock-free. Moreover, in the case of network topology changes, our verification can be directly applied to check whether any mismatches or inconsistencies will occur.