The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

The mobile cloud computing (MCC) paradigm is aimed at integrating mobile devices with cloud computing. In the client-server architecture of MCC, mobile devices offload tasks to the cloud to utilize the computation and storage resources of data centers. However, due to the rapid increase in the traffic demand and complexity of mobile applications, service providers have to continuously upgrade their infrastructures at great expense. At the same time, modern mobile devices have greater resources (communication, computation, and sensing), and these resources are not always fully utilized by device users. Therefore, mobile devices, from time to time, encounter other devices that could provide resources to them. Because the amount of such resources has increased with the number of mobile devices, researchers have begun to consider making use of these resources, located at the “edge” of mobile networks, to increase the scalability of future information networks. This has led to a cooperation based architecture of MCC. This paper reports the concept and design of an resource sharing mechanism that utilize resources in mobile devices through opportunistic contacts between them. Theoretical models and formal definitions of problems are presented. The efficiency of the proposed mechanism is validated through formal proofs and extensive simulation.

Head action recognition, as a specific problem in action recognition, has been studied in this paper. Different from most existing researches, our head action recognition problem is specifically defined for the requirement of some practical applications. Based on our definition, we build a corresponding head action dataset which contains many challenging cases. For action recognition, we proposed a real-time head action recognition framework based on HOF and ELM. The framework consists of face detection based ROI determination, HOF feature extraction in ROI, and ELM based action prediction. Experiments show that our method achieves good accuracy and is efficient enough for practical applications.

Many studies have reported that the single-event transient (SET) width increases with temperature. However, the mechanism for this temperature dependency is not clear, especially for an N-hit SET. In this study, TCAD simulations are carried out to study the temperature dependence of N-hit SETs in detail. Several possible factors are examined, and the results show that the temperature dependence in bulk devices is due to the decrease in the carrier mobility with temperature in both the struck NMOS and the pull-up PMOS. In contrast, the temperature dependence in SOI devices is due to the decrease in the diffusion constant and carrier lifetime with temperature, which enhances the parasitic bipolar effect.

Traditional speech enhancement (SE) algorithms usually have fluctuant performance when they deal with different types of noisy speech signals. In this paper, we propose multi-task Bayesian compressive sensing based speech enhancement (MT-BCS-SE) algorithm to achieve not only comparable performance to but also more stable performance than traditional SE algorithms. MT-BCS-SE algorithm utilizes the dependence information among compressive sensing (CS) measurements and the sparsity of speech signals to perform SE. To obtain sufficient sparsity of speech signals, we adopt overcomplete dictionary to transform speech signals into sparse representations. K-SVD algorithm is employed to learn various overcomplete dictionaries. The influence of the overcomplete dictionary on MT-BCS-SE algorithm is evaluated through large numbers of experiments, so that the most suitable dictionary could be adopted by MT-BCS-SE algorithm for obtaining the best performance. Experiments were conducted on well-known NOIZEUS corpus to evaluate the performance of the proposed algorithm. In these cases of NOIZEUS corpus, MT-BCS-SE is shown that to be competitive or even superior to traditional SE algorithms, such as optimally-modified log-spectral amplitude (OMLSA), multi-band spectral subtraction (SSMul), and minimum mean square error (MMSE), in terms of signal-noise ratio (SNR), speech enhancement gain (SEG) and perceptual evaluation of speech quality (PESQ) and to have better stability than traditional SE algorithms.

This paper presents an analysis of electrically large antennas using the adaptive integral method (AIM). The arbitrarily shaped perfectly conducting surfaces are modeled using triangular patches and the associated electric field integral equation (EFIE) is solved for computing the radiation patterns of these antennas. The method of moments (MoM) is used to discretize the integral equations and the resultant matrix system will be solved by an iterative solver. The AIM is employed in the iterative solver to speed up the matrix-vector multiplication and to reduce the memory requirement. As specific applications, radiation patterns of parabolic reflectors and X-band horns are computed using the proposed method.

A robust method is presented for 3D face landmarking with facial pose and expression variations. This method is based on Multi-level Partition of Unity (MPU) Implicits without relying on texture, pose, orientation and expression information. The MPU Implicits reconstruct 3D face surface in a hierarchical way. From lower to higher reconstruction levels, the local shapes can be reconstructed gradually according to their significance. For 3D faces, three landmarks, nose, left eyehole and right eyehole, can be detected uniquely with the analysis of curvature features at lower levels. Experimental results on GavabDB database show that this method is invariant to pose, holes, noise and expression. The overall performance of 98.59% is achieved under pose and expression variations.

This paper proposes a novel method to segment the object of a specific class based on a rough detection window (such as Deformable Part Model (DPM) in this paper), which is robust to the positions of the bounding boxes. In our method, the DPM is first used to generate the root and part windows of the object. Then a set of object part candidates are generated by randomly sampling windows around the root window. Furthermore, an undirected graph (the minimum spanning tree) is constructed to describe the spatial relationships between the part windows. Finally, the object is segmented by grouping the part proposals on the undirected graph, which is formulated as an energy function minimization problem. A novel energy function consisting of the data term and the smoothness term is designed to characterize the combination of the part proposals, which is globally minimized by the dynamic programming on a tree. Our experimental results on challenging dataset demonstrate the effectiveness of the proposed method.

Bugs are becoming unavoidable in complex integrated circuit design. It is imperative to identify the bugs as soon as possible through post-silicon debug. For post-silicon debug, observability is one of the biggest challenges. Scan-based debug mechanism provides high observability by reusing scan chains. However, it is not feasible to scan dump cycle-by-cycle during program execution due to the excessive time required. In fact, it is not necessary to scan out the error-free states. In this paper, we introduce Suspect Window to cover the clock cycle in which the bug is triggered. Then, we present an efficient approach to determine the suspect window. Based on Suspect Window, we propose a novel debug mechanism to locate the bug both temporally and spatially. Since scan dumps are only taken in the suspect window with the proposed mechanism, the time required for locating the bug is greatly reduced. The approaches are evaluated using ISCAS'89 and ITC'99 benchmark circuits. The experimental results show that the proposed mechanism can significantly reduce the overall debug time compared to scan-based debug mechanism while keeping high observability.

In the neck part of a two-stage object detection network, feature fusion is generally carried out in either a top-down or bottom-up manner. However, two types of imbalance may exist: feature imbalance in the neck of the model and gradient imbalance in the region of interest extraction layer due to the scale changes of objects. The deeper the network is, the more abstract the learned features are, that is to say, more semantic information can be extracted. However, the extracted image background, spatial location, and other resolution information are less. In contrast, the shallow part can learn little semantic information, but a lot of spatial location information. We propose the Both Ends to Centre to Multiple Layers (BEtM) feature fusion method to solve the feature imbalance problem in the neck and a Multi-level Region of Interest Feature Extraction (MRoIE) layer to solve the gradient imbalance problem. In combination with the Region-based Convolutional Neural Network (R-CNN) framework, our Balanced Feature Fusion (BFF) method offers significantly improved network performance compared with the Faster R-CNN architecture. On the MS COCO 2017 dataset, it achieves an average precision (AP) that is 1.9 points and 3.2 points higher than those of the Feature Pyramid Network (FPN) Faster R-CNN framework and the Generic Region of Interest Extractor (GRoIE) framework, respectively.

Despite the increasing use of mobile computing, exploiting its full potential is difficult due to its inherent characteristics such as error-prone transmission channels, diverse node capabilities, frequent disconnections and mobility. Mobile Cloud Computing (MCC) is a paradigm that is aimed at overcoming previous problems through integrating mobile devices with cloud computing. Mobile devices, in the traditional client-server architecture of MCC, offload their tasks to the cloud to utilize the computation and storage resources of data centers. However, along with the development of hardware and software technologies in mobile devices, researchers have begun to take into consideration local resource sharing among mobile devices themselves. This is defined as the cooperation based architecture of MCC. Analogous to the conventional terminology, the resource platforms that are comprised of surrounding surrogate mobile devices are called local resource clouds. Some researchers have recently verified the feasibility and benefits of this strategy. However, existing work has neglected an important issue with this approach, i.e., how to construct local resource clouds in dynamic mobile wireless networks. This paper presents the concept and design of a local resource cloud that is both energy and time efficient. Along with theoretical models and formal definitions of problems, an efficient heuristic algorithm with low computational complexity is also presented. The results from simulations demonstrate the effectiveness of the proposed models and method.

Surface light field advances conventional light field rendering techniques by utilizing geometry information. Using surface light field, real-world objects with complex appearance could be faithfully represented. This capability could play an important role in many VR/AR applications. However, an accurate geometric model is needed for surface light field sampling and processing, which limits its wide usage since many objects of interests are difficult to reconstruct with their usually very complex appearances. We propose a novel two-step optimization framework to reduce the dependency of accurate geometry. The key insight is to treat surface light field sampling as a multi-view multi-texture optimization problem. Our approach can deal with both model inaccuracy and image to model misalignment, making it possible to create high-fidelity surface light field models without using high-precision special hardware.

Sharing data might bring the risk of disclosing the sensitive knowledge in it. Usually, the data owner may choose to sanitize data by modifying some items in it to hide sensitive knowledge prior to sharing. This paper focuses on protecting sensitive knowledge in the form of frequent itemsets by data sanitization. The sanitization process may result in side effects, i.e., the data distortion and the damage to the non-sensitive frequent itemsets. How to minimize these side effects is a challenging problem faced by the research community. Actually, there is a trade-off when trying to minimize both side effects simultaneously. In view of this, we propose a data sanitization method based on evolutionary multi-objective optimization (EMO). This method can hide specified sensitive itemsets completely while minimizing the accompanying side effects. Experiments on real datasets show that the proposed approach is very effective in performing the hiding task with fewer damage to the original data and non-sensitive knowledge.

With the knowledge of the syndromes Sa,b, 0a,b q-2, the exact error values cannot be determined by using the conventional (q-1)2-point discrete Fourier transform in the decoding of a plane algebraic-geometric code over GF(q). In this letter, the inverse q-point 1-dimensional and q2-point 2-dimensional affine Fourier transform over GF(q) are presented to be used to retrieve the actual error values, but it requires much computation efforts. For saving computation complexity, a modification of the affine Fourier transform is derived by using the property of the rational points of the plane Hermitian curve. The modified transform, which has almost the same computation complexity of the conventional discrete Fourier transform, requires the knowledge of syndromes Sa,b, 0 a,b q-2, and three more extended syndromes Sq-1,q-1, S0,q-1, Sq-1,0.

With the development of global navigation satellite systems (GNSS), the interference among global navigation satellite systems, known as the radio frequency compatibility problem, has become a matter of great concern to system providers and user communities. The acceptable compatibility threshold should be determined in the radio frequency compatibility assessment process. However, there is no common standard for the acceptable threshold in the radio frequency compatibility assessment. This paper firstly introduces the comprehensive radio frequency compatibility methodology combining the spectral separation coefficient (SSC) and code tracking spectral sensitivity coefficient (CT_SSC). Then, a method for determination of the acceptable compatibility threshold is proposed. The proposed method considers the receiver processing phase including acquisition, code and carrier tracking and data demodulation. Simulations accounting for the interference effects are carried out at each time step and every place on earth. The simulations mainly consider the signals of GPS, Galileo and BeiDou Navigation Satellite System (BDS) in the L1 band. Results show that all of the sole systems are compatible with other GNSS systems with respect to a special receiver configuration used in the simulations.

In 2002, Hwang and Yeh proposed some improved schemes to mend several security flaws in the Peyravian-Zunic password transmission scheme and password change scheme. However, this article will point out that there still exist some security flaws in the Hwang-Yeh schemes; at the same time, we shall also propose some improved versions of their schemes.

In this study, we propose a two-step approach to evaluate electromagnetic interference (EMI) with a wearable vital signal sensor. The two-step approach combines a quasi-static electromagnetic (EM) field analysis and an electric circuit analysis, and is applied to the EMI evaluation at frequencies below 1 MHz for our developed wearable electrocardiogram (ECG) to demonstrate its usefulness. The quasi-static EM field analysis gives the common mode voltage coupled from the incident EM field at the ECG sensing electrodes, and the electric circuit analysis quantifies a differential mode voltage at the differential amplifier output of the ECG detection circuit. The differential mode voltage has been shown to come from a conversion from the common mode voltage due to an imbalance between the contact impedances of the two sensing electrodes. When the contact impedance is resistive, the induced differential mode voltage increases with frequency up to 100kHz, and keeps constant after 100kHz, i.e., exhibits a high pass filter characteristic. While when the contact impedance is capacitive, the differential mode voltage exhibits a band pass filter characteristic with the maximum at frequency of around 150kHz. The differential voltage may achieve nearly 1V at the differential amplifier output for an imbalance of 30% under 10V/m plane-wave incident electric field, and completely mask the ECG signal. It is essential to reduce the imbalance as much as possible so as to prevent a significant interference voltage in the amplified ECG signal.

Systolic array implementations of modified Gaussian eliminations for the decoding of an (n, n-2t) RS code, including the Hong-Vetterli algorithm and the FIA proposed by Feng and Tzeng, are designed in this paper. These modified Gaussian eliminations are more easily understanding than the classical Berlekamp-Massey algorithm and, in addition, are efficient to decode RS codes for small e or e <

A text-dependent i-vector extraction scheme and a lexicon-based binary vector (L-vector) representation are proposed to improve the performance of text-dependent speaker verification. I-vector and L-vector are used to represent the utterances for enrollment and test. An improved cosine distance kernel is constructed by combining i-vector and L-vector together and is used to distinguish both speaker identity and lexical (or text) diversity with back-end support vector machine (SVM). Experiments are conducted on RSR 2015 Corpus part 1 and part 2, the results indicate that at most 30% improvement can be obtained compared with traditional i-vector baseline.

Test data volume and test power are two major concerns when testing modern large circuits. Recently, selective encoding of scan slices is proposed to compress test data. This encoding technique, unlike many other compression techniques encoding all the bits, only encodes the target-symbol by specifying a single bit index and copying group data. In this paper, we propose an extended selective encoding which presents two new techniques to optimize this method: a flexible grouping strategy, X bits exploitation and filling strategy. Flexible grouping strategy can decrease the number of groups which need to be encoded and improve test data compression ratio. X bits exploitation and filling strategy can exploit a large number of don't care bits to reduce testing power with no compression ratio loss. Experimental results show that the proposed technique needs less test data storage volume and reduces average weighted switching activity by 25.6% and peak weighted switching activity by 9.68% during scan shift compared to selective encoding.