An allocation of sensing and reporting times is proposed to improve the sensing performance by scheduling them in an efficient way for cognitive radio networks with cluster-based cooperative spectrum sensing. In the conventional cooperative sensing scheme, all secondary users (SUs) detect the primary user (PU) signal to check the availability of the spectrum during a fixed sensing time slot. The sensing results from the SUs are reported to cluster heads (CHs) during the reporting time slots of the SUs and the CHs forward them to a fusion center (FC) during the reporting time slots of the CHs through the common control channels for the global decision, respectively. However, the delivery of the local decision from SUs and CHs to a CH and FC requires a time which does not contribute to the performance of spectrum sensing and system throughput. In this paper, a super-allocation technique, which merges reporting time slots of SUs and CHs to sensing time slots of SUs by re-scheduling the reporting time slots, has been proposed to sense the spectrum more accurately. In this regard, SUs in each cluster can obtain a longer sensing duration depending on their reporting order and their clusters except for the first SU belonged to the first cluster. The proposed scheme, therefore, can achieve better sensing performance under -28 dB to -10 dB environments and will thus reduce reporting overhead.

This paper investigates the outage performance and optimization for the four-phase two-way transmission network with an energy harvesting (EH) relay. To enable the simultaneous information processing and energy harvesting at the relay, we firstly propose a power splitting-based two-way relaying protocol (PSTWR). Then, we discuss its outage performance theoretically and derive an explicit expression for the system outage probability. In order to find the optimal system configuration parameters such as the optimal power splitting ratio and the optimal transmit power redistribution factor, we formulate an outage-minimized optimization problem. As the problem is difficult to solve, we design a genetic algorithm (GA) based algorithm for it. Besides, we also investigate the effects of the power splitting ratio, the power redistribution factor at the relay, and the source to relay distance on the system outage performance. Finally, extensive simulation results are provided to demonstrate the accuracy of the analytical results and the effectiveness of the GA-based algorithm. Moreover, it is also shown that, the relay position greatly affects the system performance, where relatively worse outage performance is achieved when the EH relay is placed in the middle of the two sources.

In this paper, the capacity and BER performance of downlink distributed antenna systems (DAS) with transmit antenna selection and multiple receive antennas are investigated in MIMO composite channel, where path loss, Rayleigh fading and lognormal shadowing are all considered. Based on the performance analysis, using the probability density function (PDF) of the effective SNR and numerical integrations, tightly-approximate closed-form expressions of ergodic capacity and average BER of DAS are derived, respectively. These expressions have more accuracy than the existing expressions, and can match the simulation well. Besides, the outage capacity of DAS is also analyzed, and a tightly-approximate closed-form expression of outage capacity probability is derived. Moreover, a practical iterative algorithm based on Newton's method for finding the outage capacity is proposed. To avoid iterative calculation, another approximate closed-form outage capacity is also derived by utilizing the Gaussian distribution approximation. With these theoretical expressions, the downlink capacity and BER performance of DAS can be effectively evaluated. Simulation results show that the theoretical analysis is valid, and consistent with the corresponding simulation.

Synchronous computing models provided by real-time synchrony protocols, such as TTA [1] and PALS [2], greatly simplify the design, implementation, and verification of real-time distributed systems. However, their application to real systems has been limited since their assumptions on underlying systems are hard to satisfy. In particular, most previous real-time synchrony protocols hypothesize the existence of underlying fault tolerant real-time networks. This, however, might not be true in most soft real-time applications. In this paper, we propose a practical approach to a synchrony protocol, called Quality-Aware PALS (qPALS), which provides the benefits of a synchronous computing model in environments where no fault-tolerant real-time network is available. qPALS supports two flexible global synchronization protocols: one tailored for the performance and the other for the correctness of synchronization. Hence, applications can make a negotiation flexibly between performance and correctness. In qPALS, the Quality-of-Service (QoS) on synchronization and consistency is specified in a probabilistic manner, and the specified QoS is supported under dynamic and unpredictable network environments via a control-theoretic approach. Our simulation results show that qPALS supports highly reliable synchronization for critical events while still supporting the efficiency and performance even when the underlying network is not stable.

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

Conventional mobile state (MS) and base station (BS) association based on average signal strength often results in imbalance of cell load which may require more powerful processor at BSs and degrades the perceived transmission rate of MSs. To deal with this problem, a Markov decision process (MDP) for load balancing in a multi-cell system with multi-carriers is formulated. To solve the problem, exploiting Sarsa algorithm of on-line learning type [12], ${\alpha}$-controllable load balancing algorithm is proposed. It is designed to control tradeoff between the cell load deviation of BSs and the perceived transmission rates of MSs. We also propose an ${\varepsilon}$-differential soft greedy policy for on-line learning which is proven to be asymptotically convergent to the optimal greedy policy under some condition. Simulation results verify that the ${\alpha}$-controllable load balancing algorithm controls the behavior of the algorithm depending on the choice of ${\alpha}$. It is shown to be very efficient in balancing cell loads of BSs with low ${\alpha}$.

The frequency spectrum available for the wireless communication is extremely crowded. In order to improve the spectral efficiency, the two-way relay networks have aroused great attention. A simple $N^{th}$ best-relay selection criterion for the opportunistic two-way relay networks is proposed, which can be implemented easily by extending the distributed timer technique in practice, since the proposed criterion is mainly based on the channel gains. The outage performance of the proposed relay selection scheme is analyzed under the outdated channel state information (CSI), and a tight closed-form lower bound and asymptotic value of the outage probability over Rayleigh fading channels are obtained. Simulation results demonstrate that the tight closed-form lower bound of the outage probability very closely matches with simulated ones in the whole SNR region, and the asymptotic results provide good tight approximations to the simulation ones, especially in the high SNR region.

In recent years, wearable sensor devices are reshaping the way people live, work, and play. A wearable sensor device is a computer that is subsumed into the personal space of the user, and is always on, and always accessible. Therefore, among the most salient aspects of a wearable sensor device should be a small form factor, long battery lifetime, and real-time characteristics. Thereby, sophisticated applications of a wearable sensor device use real-time operating systems to guarantee real-time deadlines. The deterministic multi-dimensional task scheduling algorithms are implemented on ARC (Actual Remote Control) with relatively limited hardware resources. ARC is a wearable wristwatch-type remote controller; it can also serve as a universal remote control, for various wearable sensor devices. In the proposed algorithms, there is no limit on the maximum number of task priorities, and the memory requirement can be dramatically reduced. Furthermore, regardless of the number of tasks, the complexity of the time and space of the proposed algorithms is O(1). A valuable contribution of this work is to guarantee real-time deadlines for wearable sensor devices.

Clustering is an effective method for improving the performance of large scale mobile ad hoc networks. However, when the moving speed is very fast, the topology changes quickly, which leads to frequent cluster topology updates. The drastically increasing control overheads severely threaten the throughput of the network. SCBCS (Signal Characteristic Based Cluster Scheme) is proposed as a method to potentially reduce the control overheads caused by cluster formation and maintenance in aeronautical ad hoc networks. Each node periodically broadcasts Hello packets. The Hello packets can be replaced by data packets, which preserve bandwidth. The characteristics of the received packets, such as the Doppler shift and the power of two successive Hello packets, help to calculate the relative speed and direction of motion. Then, the link connection lifetime is estimated by the relative speed and direction of motion. In the clustering formation procedure, the node with the longest estimated link connection time to its one-hop neighbors is chosen as the cluster head. In the cluster maintenance procedure, re-affiliation and re-clustering schemes are designed to keep the clusters more stable. The re-clustering phenomenon is reduced by limiting the ripple effect. Simulations have shown that SCBCS prolongs the link connection lifetime and the cluster lifetime, which can reduce the topology update overheads in highly dynamic aeronautical ad hoc networks.

The performance of IEEE 802.11e enhanced distributed channel access (EDCA) is influenced by several interactive parameters that make quality of service (QoS) control complex and difficult. In EDCA, the most critical performance influencing parameters are the arbitration interframe space (AIFS) and contention window size (CW) of each access category (AC). The objective of this paper is to provide a scheme for parameter control such that the throughput per station as well as the overall system throughput of the network is maximized and controllable. For this purpose, a simple and accurate analytical model describing the throughput behavior of EDCA networks is presented in this paper. Based on this model, the paper further provides a scheme in which a Pareto optimal system configuration is obtained via an appropriate CW control for a given AIFS value, which is a different approach compared to relevant papers in the literature that deal with CW control only. The simulation results confirm the effectiveness of the proposed method which shows significant performance improvements compared to other existing algorithms.

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

The use of a smartphone with one hand has been getting difficult as the size of the smartphone display gets larger. In addition, people handicapped or using one hand for handling other pieces may encounter difficulties to touch exact numbers for calling with their smartphones. Thus, motion-tracked speed dialing can be a good solution for these cases and also an advanced feature applicable in the future network not requiring dedicated Internet connections. The previous study on this feature showed a result with a limitation of using servers connected via Internet connections for solving real-time control issues. Therefore, this paper proposes a scheme for the smartphone dialing on motion-tracked numbers without any servers or Internet connections, and presents the details of its implementation and results.

Video copy detection methods have emerged in recent years for a variety of applications. However, the lack of efficiency in the usual retrieval systems restricts their use. In this paper, we propose a parallel implementation strategy for content based video copy detection (CBCD) by using a multi-core processor. This strategy can support video copy detection effectively, and the processing time tends to decrease linearly as the number of processors increases. Experiments have shown that our approach is successful in speeding up computation and as well as in keeping the performance.

This paper proposes a novel video stitching method that improves real-time performance and visual quality of a multi-camera video surveillance system. A two-stage seam searching algorithm based on enhanced dynamic programming is proposed. It can obtain satisfactory result and achieve better real-time performance than traditional seam-searching methods. The experiments show that the computing time is reduced by 66.4% using the proposed algorithm compared with enhanced dynamic programming, while the seam-searching accuracy is maintained. A real-time local update scheme reduces the deformation effect caused by moving objects passing through the seam, and a seam-based local color transfer model is constructed and applied to achieve smooth transition in the overlapped area, and overcome the traditional pixel blending methods. The effectiveness of the proposed method is proved in the experiements.

We develop a secret key generation scheme using phase estimation in ultra-wideband (UWB) wireless fading channels. Based on the reciprocity theorem, two terminals extract the phase of the channel as a common random source to generate secret bits. Moreover, we study the secret key rate by a pair of nodes observing correlated sources and communicating to achieve secret key agreement over public communication channels. As our main results, we establish a more practical upper bound from Cramer-Rao bound (CRB) and compare it with a universally theoretical upper bound on the shared maximum key rate from mutual information of correlated random sources. Derivation and numerical examples are presented to demonstrate the bound. Simulation studies are also provided to validate feasibility and efficiency of the proposed scheme.

Cellular automata (CA) based cryptosystem has been studied for almost three decades, yet most of previously reported researches focus on the symmetric key encryption schemes. Up to now, few CA based public key encryption scheme has been proposed. To fill the gap, in this paper, we propose a new public key encryption scheme based on layered cellular automata (LCA). Specifically, in the proposed scheme, based on the T-shaped neighborhood structure, we combine four one-dimensional reversible CAs (set as the private key) to form the transition rules of a two-dimension CA, where the two-dimension CA is set as the corresponding public key. Based on the hardness assumption of the Decisional Dependent CA problem in LCA, we formally prove the proposed scheme is indistinguishably secure against the chosen-plaintext attack (IND-CPA). In addition, we also use a numeric example to demonstrate its feasibility. Finally, analysis of key space and time efficiency are also carried out along with RSA-1024, and the simulation results demonstrate that our proposed scheme is more efficient.

In this paper, a robust watermarking scheme is proposed that uses the scale-invariant feature transform (SIFT) algorithm in the discrete wavelet transform (DWT) domain. First, the SIFT feature areas are extracted from the original image. Then, one level DWT is applied on the selected SIFT feature areas. The watermark is embedded by modifying the fractional portion of the horizontal or vertical, high-frequency DWT coefficients. In the watermark extracting phase, the embedded watermark can be directly extracted from the watermarked image without requiring the original cover image. The experimental results showed that the proposed scheme obtains the robustness to both signal processing and geometric attacks. Also, the proposed scheme is superior to some previous schemes in terms of watermark robustness and the visual quality of the watermarked image.

The increasing demand in promoting cloud computing in either business or other areas requires more security of a cloud storage system. Traditional cloud storage systems fail to protect data integrity information (DII), when the interactive messages between the client and the data storage server are sniffed. To protect DII and support public verifiability, we propose a data integrity verification scheme by deploying a designated confirmer signature DCS as a building block. The DCS scheme strikes the balance between public verifiable signatures and zero-knowledge proofs which can address disputes between the cloud storage server and any user, whoever acting as a malicious player during the two-round verification. In addition, our verification scheme remains blockless and stateless, which is important in conducting a secure and efficient cryptosystem. We perform security analysis and performance evaluation on our scheme, and compared with the existing schemes, the results show that our scheme is more secure and efficient.

At SAC 2004, Junod and Vaudenay designed the FOX family based on the Lai-Massey scheme. They noted that it was impossible to find any useful differential characteristic or linear trail after 8 rounds of FOX64 or FOX128. In this paper, we provide the lower bound of differentially active S-boxes in consecutive rounds of the Lai-Massey scheme that has SPS as its F-function, and we propose the necessary conditions for the reachability of the lower bound. We demonstrate that similar results can be obtained with respect to the lower bound of linearly active S-boxes by proving the duality in the Lai-Massey scheme. Finally, we apply these results to FOX64 and FOX128 and prove that it is impossible to find any useful differential characteristics or linear trail after 6 rounds of FOX64. We provide a more precise security bound for FOX128.