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.

In this paper, we propose a channel-unaware algorithm to suppress the narrowband interference (NBI) for the time synchronization, where multiple antennas are equipped at the receiver. Based on the fact that the characteristics of synchronization signal are different from those of NBI in both the time and spatial domain, the proposed algorithm suppresses the NBI by utilizing the multiple receive antennas in the eigen domain of NBI, where the eigen domain is obtained from the time domain statistical information of NBI. Because time synchronization involves incoherent detection, the proposed algorithm does not use the desired channel information, which is different from the eigen domain interference rejection combining (E-IRC). Simulation results show, compared with the traditional frequency domain NBI suppression technique, the proposed algorithm has about a 2 dB gain under the same probability of detection.

Provisioning multiple paths can improve fault tolerance and transport capability of multi-routing in wireless networks. Disjoint paths can improve the diversity of paths and further reduce the risk of simultaneous link failure and network congestion. In this paper we first address a many-to-one disjoint-path problem (MOND) for multi-path routing in a multi-hop wireless network. The objective of this problem is to maximize the minimum number of disjoint paths of every source to the destination. We prove that it is NP-hard to obtain k disjoint paths for every source when k ≥ 3. To solve this problem efficiently, we propose a heuristic algorithm called TOMAN based on network flow theory. Experimental results demonstrate that it outperforms three related algorithms.

The Network-on-Chip (NoC) is limited by the reliability constraint, which impels us to exploit the fault-tolerant routing. Generally, there are two main design objectives: tolerating more faults and achieving high network performance. To this end, we propose a new multiple-round dimension-order routing (NMR-DOR). Unlike existing solutions, besides the intermediate nodes inter virtual channels (VCs), some turn-legally intermediate nodes inside each VC are also utilized. Hence, more faults are tolerated by those new introduced intermediate nodes without adding extra VCs. Furthermore, unlike the previous solutions where some VCs are prioritized, the NMR-DOR provides a more flexible manner to evenly distribute packets among different VCs. With extensive simulations, we prove that the NMR-DOR maximally saves more than 90% unreachable node pairs blocked by faults in previous solutions, and significantly reduces the packet latency compared with existing solutions.

Web application second-order vulnerabilities first inject malicious code into the persistent data stores of the web server and then execute it at later sensitive operations, causing severe impact. Nevertheless, the dynamic features, the complex data propagation, and the inter-state dependencies bring many challenges in discovering such vulnerabilities. To address these challenges, we propose DISOV, a web application property graph (WAPG) based method to discover second-order vulnerabilities. Specifically, DISOV first constructs WAPG to represent data propagation and inter-state dependencies of the web application, which can be further leveraged to find the potential second-order vulnerabilities paths. Then, it leverages fuzz testing to verify the potential vulnerabilities paths. To verify the effectiveness of DISOV, we tested it in 13 popular web applications in real-world and compared with Black Widow, the state-of-the-art web vulnerability scanner. DISOV discovered 43 second-order vulnerabilities, including 23 second-order XSS vulnerabilities, 3 second-order SQL injection vulnerabilities, and 17 second-order RCE vulnerabilities. While Black Widow only discovered 18 second-order XSS vulnerabilities, with none second-order SQL injection vulnerability and second-order RCE vulnerability. In addition, DISOV has found 12 0-day second-order vulnerabilities, demonstrating its effectiveness in practice.

LSI testing is critical to guarantee chips are fault-free before they are integrated in a system, so as to increase the reliability of the system. Although full-scan is a widely adopted design-for-testability technique for LSI design and testing, there is a strong need to reduce the test data Volume, scan-in Power dissipation, and test application Time (VPT) of full-scan testing. Based on the analysis of the characteristics of the variable-to-fixed run-length coding technique and the random access scan architecture, this paper presents a novel design scheme to tackle all VPT issues simultaneously. Experimental results on ISCAS'89 benchmarks have shown on average 51.2%, 99.5%, 99.3%, and 85.5% reduction effects in test data volume, average scan-in power dissipation, peak scan-in power dissipation, and test application time, respectively.

In 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) systems, when HARQ (Hybrid Automatic Repeat request) retransmission is invoked, the data at the transmitter are retransmitted randomly or sequentially regardless of their relationship to the wrongly decoded data. Such practice is inefficient since precious transmission resources will be spent to retransmit data that may be of no use in error correction at the receiver. This paper proposes an incremental redundancy HARQ scheme based on Error Position Estimating Coding (ePec) and LDPC (Low Density Parity Check Code) channel coding, which is called ePec-LDPC HARQ. The proposal is able to feedback the wrongly decoded code blocks within a specific MAC (Media Access Control) PDU (Protocol Data Unit) from the receiver. The transmitter gets the feedback information and then performs targeted retransmission. That is, only the data related to the wrongly decoded code blocks are retransmitted, which can improve the retransmission efficiency and thus reduce the retransmission overload. An enhanced incremental redundancy LDPC coding approach, called EIR-LDPC, together with a physical layer framing method, is developed to implement ePec-LDPC HARQ. Performance evaluations show that ePec-LDPC HARQ reduces the overall transmission resources by 15% compared to a conventional LDPC HARQ scheme. Moreover, the average retransmission times of each MAC PDU and the transmission delay are also reduced considerably.

In the framework of the modernization plan of COMPASS system, the existing COMPASS signals should be transmitted along with the modernized signals to maintain backward compatibility. In this paper, an efficient multiplexing scheme based on the optimal aligning method for combining COMPASS Phase II B3 and Phase III B3 signals is proposed, which offers significantly higher efficiency than Interplex and Generalized Majority Voting (GMV) multiplexing methods. The proposed scheme can provide potential opportunities for COMPASS system and other global navigation satellite systems (GNSS) modernization and construction plans.