With the rising popularity of delay-sensitive real-time multimedia applications (video, voice, and data) in IEEE 802.11 wireless local area networks (WLANs), it is becoming important to study the medium access control (MAC) layer delay performance of WLANs. The MAC layer delay can be classified into two categories: 1) medium access delay, and 2) delay at interface queue (IFQ). In this paper, based on a two-dimensional chain model, we analyze the medium access delay and give a method to calculate the IFQ delay. The proposed analysis is applicable to both the basic access and the RTS/CTS access mechanisms. Through extensive simulations, we evaluate our model. The simulation results show that our analysis is extremely accurate for both basic access and RTS/CTS access mechanism of the 802.11 DCF protocol.

Reconfigurable intelligent surface (RIS) has the capability of boosting system performance by manipulating the wireless propagation environment. This paper investigates a downlink RIS-aided non-orthogonal multiple access (NOMA) system, where a RIS is deployed to enhance physical-layer security (PLS) in the presence of an eavesdropper. In order to improve the main link's security, the RIS is deployed between the source and the users, in which a reflecting element separation scheme is developed to aid data transmission of both the cell-center and the cell-edge users. Additionally, the closed-form expressions of secrecy outage probability (SOP) are derived for the proposed RIS-aided NOMA scheme. To obtain more deep insights on the derived results, the asymptotic performance of the derived SOP is analyzed. Moreover, the secrecy diversity order is derived according to the asymptotic approximation in the high signal-to-noise ratio (SNR) and main-to-eavesdropper ratio (MER) regime. Furthermore, based on the derived results, the power allocation coefficient and number of elements are optimized to minimize the system SOP. Simulations demonstrate that the theoretical results match well with the simulation results and the SOP of the proposed scheme is clearly less than that of the conventional orthogonal multiple access (OMA) scheme obviously.

Modern large scale distributed storage systems play a central role in data center and cloud storage, while node failure in data center is common. The lost data in failure node must be recovered efficiently. Locally repairable codes (LRCs) are designed to solve this problem. The locality of an LRC is the number of nodes that participate in recovering the lost data from node failure, which characterizes the repair efficiency. An LRC is called optimal if its minimum distance attains Singleton-type upper bound [1]. In this paper, using basic techniques of linear algebra over finite field, infinite optimal LRCs over extension fields are derived from a given optimal LRC over base field(or small field). Next, this paper investigates the relation between near-MDS codes with some constraints and LRCs, further, proposes an algorithm to determine locality of dual of a given linear code. Finally, based on near-MDS codes and the proposed algorithm, those obtained optimal LRCs are shown.

Data mining across different companies, organizations, online shops, or the likes is necessary so as to discover valuable shared patterns, associations, trends, or dependencies in their shared data. Privacy, however, is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement, in this paper we propose an effective distributed privacy-preserving data mining approach called SDDM. SDDM is characterized by its ability to resist collusion. Unless the number of colluding sites in a distributed system is larger than or equal to 4, privacy cannot be violated. Results of performance study demonstrated the effectiveness of SDDM.

A novel standard-compliant multiple description (MD) video codec is proposed in this paper, which aims to achieve effective redundancy allocation using inter- and intra-description correlation. The inter-description correlation at macro block (MB) level is applied to produce side information of different modes which is helpful for better side decoding quality. Furthermore, the intra-description correlation at MB level is exploited to design the adaptive skip mode for higher compression efficiency. The experimental results exhibit a better rate of side and central distortion performance compared with other relevant MDC schemes.

As an ongoing video compression standard, High Efficiency Video Coding (HEVC) has achieved better rate distortion performance than H.264, but it also leads to enormous encoding complexity. In this paper, we propose a novel fast coding unit partition algorithm in the intra prediction of HEVC. Firstly, instead of the time-consuming rate distortion optimization for coding mode decision, just-noticeable-difference (JND) values can be exploited to partition the coding unit according to human visual system characteristics. Furthermore, coding bits in HEVC can also be considered as assisted information to refine the partition results. Compared with HEVC test model HM10.1, the experimental results show that the fast intra mode decision algorithm provides over 28% encoding time saving on average with comparable rate distortion performance.

Testing is a critical stage in integrated circuits production in order to guarantee reliability. The complexity and high integration level of mixed-signal ICs has put forward new challenges to circuit testing. This paper describes an oscillation-based combined self-test strategy for the analog portion and analog-to-digital converters (ADCs) in integrated mixed-signal circuits. In test mode, the analog portion under test is reconfigured into an oscillator, generating periodic signals as the test stimulus of ADC. By analyzing the A/D conversion results, a histogram test of ADC can be performed, and the oscillation frequency as well as amplitude can be checked, and in this way the oscillation test of the analog portion is realized simultaneously. For an analog benchmark circuit combined with an ADC, triangle oscillation and sinusoid oscillation schemes are both given to test their faults. Experimental results show that fault coverage of the analog portion is 92.2% and 94.3% in the two schemes respectively, and faults in the ADC can also be tested.

A modified error correction/detection scheme based on the scheme by Yi and Lee is proposed. Algebraic decoding is used to perform error correction. Error detection is performed by an absolute value test. It is shown that the proposed scheme bridges the performance gap between Yi and Lee's scheme and Forney's optimal scheme.

Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.

The performance of cooperative spectrum sensing (CSS) is limited not only by the imperfect sensing channels but also by the imperfect reporting channels. In order to improve the transmission reliability of the reporting channels, an object based cooperative spectrum sensing scheme with best relay (Pe-BRCS) is proposed, in which the best relay is selected by minimizing the total reporting error probability to improve the sensing performance. Numerical results show that, the reduced total reporting error probability and the improved sensing performance can be achieved by the Pe-BRCS scheme.

Process variation causes significant fluctuations in the timing performance of analog circuits, which causes a fraction of circuits to fail specifications. By testing the delay-performance, we can recognize the failed circuits during production testing. In this paper, we have proposed a low overhead and process tolerant delay evaluation circuit for built-in self test (BIST) function for analog differential circuits. This circuit contains a delay generation cell, an input differential signal generation cell, a delay matching cell, a sample-hold circuit, and a comparator. This circuit was implemented with 0.18 µm CMOS process. Simulation results over process variation, devices mismatch and layout parasitics, but without silicon measurement, show that the accuracy in delay detection is within 5 ps. A case study was done over a feed-forward equalizer (FFE). A typical use of this circuit is testing the delay of various FIR (Finite Impulse Response) filters.

Numerous examples in image processing have demonstrated that human visual perception can be exploited to improve processing performance. This paper presents another showcase in which some visual information is employed to guide adaptive block-wise compressive sensing (ABCS) for image data, i.e., a varying CS-sampling rate is applied on different blocks according to the visual contents in each block. To this end, we propose a visual analysis based on the discrete cosine transform (DCT) coefficients of each block reconstructed at the decoder side. The analysis result is sent back to the CS encoder, stage-by-stage via a feedback channel, so that we can decide which blocks should be further CS-sampled and what is the extra sampling rate. In this way, we can perform multiple passes of reconstruction to improve the quality progressively. Simulation results show that our scheme leads to a significant improvement over the existing ones with a fixed sampling rate.

The full band inverse fiter of an acoustic field usually can not be approximated by causal filter because of its non-minimum phase property. However, by dividing the full band signal into many sub-bands, most of the sub-bands, that do not have zeros extremely close to or outside of the unit circle, can be inverted by causal filter. In this paper, a method of recovering the reverberated speech signals by sub-band envelope estimation is proposed. A reference signal and its reverberated version are supposed to be known at the parameter estimation phase. Both signals are divided into many sub-bands. The dereverberation filter for each sub-band is calculated by the least mean square extimation of the reference sub-band signal from the reverberated one. Reverberated signals under the same condition can be recovered using the estimated dereverberation filters. Comparison of the dereverberation error and distribution of the room transfer function zeros showed that the sub-bands that have large dereverberation errors are those with zeros extremely close to or outside of the unit circle. The performance of the dereverberation is improved with the increase of the number of sub-bands. The reverberated speech is recovered in good quality when the reverberation time is less than 0.43s, which is approximately the reverberation condition of the general small office rooms.

A unified view of RAKE reception is proposed which models a RAKE receiver as an antenna array. This unified view provides valuable insight to the signal environment under RAKE reception. Based on this view, an optimum combining scheme for RAKE receivers is proposed for the downlink of multi-code W-CDMA systems. In multi-code scenarios, the presence of inter-code interference causes severe performance degradation. The antenna array model suggests that enhancement can be achieved by increasing the receiver's degrees of freedom which is defined as the number of RAKE fingers and employs an appropriate combining scheme. The conventional maximum-ratio combining scheme is excluded since it is not capable of exploiting the increased degrees of freedom. In contrast, the proposed combining scheme provides better interference suppression when the degrees of freedom are increased. Numerical results obtained show that the proposed scheme provides very promising performance.

When wireless multi-media information such as voice, video, data and so on are transmitted, the difference required quality of Service (QoS) including required Bit Error Rate (BER), required information bit rate, message's delay constraints as well as traffic performance should be taken into account. A wireless multi-media system should achieve a flexible balance of these differences. In this letter, an Adaptive Chip/Bit Control Method is proposed for Wireless Multi-media CDMA System. The proposed method controls both chip and bit rate of each medium according to the offered traffic condition and the quality measurement of each medium. In the proposed method, measurement are carried out in the base station. Simulation results show that the proposed method not only maintain the required BER of each medium, but achieve a higher total throughput even in high traffic condition. Thus we see that the proposed method possesses higher flexible ability than conventional methods.

A rich and robust representation for scene characters plays a significant role in automatically understanding the text in images. In this letter, we focus on the issue of feature representation, and propose a novel encoding method named bilateral convolutional activations encoded with Fisher vectors (BCA-FV) for scene character recognition. Concretely, we first extract convolutional activation descriptors from convolutional maps and then build a bilateral convolutional activation map (BCAM) to capture the relationship between the convolutional activation response and the spatial structure information. Finally, in order to obtain the global feature representation, the BCAM is injected into FV to encode convolutional activation descriptors. Hence, the BCA-FV can effectively integrate the prominent features and spatial structure information for character representation. We verify our method on two widely used databases (ICDAR2003 and Chars74K), and the experimental results demonstrate that our method achieves better results than the state-of-the-art methods. In addition, we further validate the proposed BCA-FV on the “Pan+ChiPhoto” database for Chinese scene character recognition, and the experimental results show the good generalization ability of the proposed BCA-FV.

Nonnegative component representation (NCR) is a mid-level representation based on nonnegative matrix factorization (NMF). Recently, it has attached much attention and achieved encouraging result for action recognition. In this paper, we propose a novel hierarchical dictionary learning strategy (HDLS) for NMF to improve the performance of NCR. Considering the variability of action classes, HDLS clusters the similar classes into groups and forms a two-layer hierarchical class model. The groups in the first layer are disjoint, while in the second layer, the classes in each group are correlated. HDLS takes account of the differences between two layers and proposes to use different dictionary learning methods for this two layers, including the discriminant class-specific NMF for the first layer and the discriminant joint dictionary NMF for the second layer. The proposed approach is extensively tested on three public datasets and the experimental results demonstrate the effectiveness and superiority of NCR with HDLS for large-scale action recognition.

The current high efficiency video coding (HEVC) standard is developed to achieve greatly improved compression performance compared with the previous coding standard H.264/AVC. It adopts a quadtree based picture partition structure to flexibility signal various texture characteristics of images. However, this results in a dramatic increase in computational complexity, which obstructs HEVC in real-time application. To alleviate this problem, we propose a fast coding unit (CU) size decision algorithm in HEVC intra coding based on consideration of the depth level of neighboring CUs, distribution of rate distortion (RD) value and distribution of residual data. Experimental results demonstrate that the proposed algorithm can achieve up to 60% time reduction with negligible RD performance loss.

A mobile ad hoc network (MANET) is a self-organizing and adaptive wireless network constructed by the dynamic gathering of mobile nodes (MNs). The communication among MNs in MANETs is carried out without base stations or access points and the transmission of data packets is completed through relays among nodes. Due to the mobility of MNs, the topology of a MANET frequently changes and thus results in the disability of on-the-fly data transmission routes. To cope with the intrinsic properties of MANETs, Dynamic Backup Routes Routing Protocol (DBR2P), a distributed backup routes mechanism for quick reconnection during link failures, is proposed in this paper. DBR2P is an on-demand routing protocol which can set up many backup routes to reach a destination node in a given period of time. The information of backup routes can be saved in a specific on-the-route node and enables backup routes to be found immediately in situation regarding disconnection. When a link fails, routes from the source node to the destination node are analyzed to obtain backup routes and to sustain quick reconnection. As a result, DBR2P could more thoroughly improve the quality of routing than previous routing protocols.

Address configuration is a key problem in data center networks. The core issue of automatic address configuration is assigning logical addresses to the physical network according to a blueprint, namely logical-to-device ID mapping, which can be formulated as a graph isomorphic problem and is hard. Recently years, some work has been proposed for this problem, such as DAC and ETAC. DAC adopts a sub-graph isomorphic algorithm. By leveraging the structure characteristic of data center network, DAC can finish the mapping process quickly when there is no malfunction. However, in the presence of any malfunctions, DAC need human effort to correct these malfunctions and thus is time-consuming. ETAC improves on DAC and can finish mapping even in the presence of malfunctions. However, ETAC also suffers from some robustness and efficiency problems. In this paper, we present GA-MAP, a data center networks address mapping algorithm based on genetic algorithm. By intelligently leveraging the structure characteristic of data center networks and the global search characteristic of genetic algorithm, GA-MAP can solve the address mapping problem quickly. Moreover, GA-MAP can even finish address mapping when physical network involved in malfunctions, making it more robust than ETAC. We evaluate GA-MAP via extensive simulation in several of aspects, including computation time, error-tolerance, convergence characteristic and the influence of population size. The simulation results demonstrate that GA-MAP is effective for data center addresses mapping.