In this paper, we propose and analyze the opportunistic amplify-and-forward (AF) relaying scheme using antenna selection in conjunction with different adaptive transmission techniques over Rayleigh fading channels. In this scheme, the best antenna of a source and the best relay are selected for communication between the source and destination. Closed-form expressions for the outage probability and average symbol error rate (SER) are derived to confirm that increasing the number of antennas is the best option as compared with increasing the number of relays. We also obtain closed-form expressions for the average channel capacity under three different adaptive transmission techniques: 1) optimal power and rate adaptation; 2) constant power with optimal rate adaptation; and 3) channel inversion with a fixed rate. The channel capacity performance of the considered adaptive transmission techniques is evaluated and compared with a different number of relays and various antennas configurations for each adaptive technique. Our derived analytical results are verified through extensive Monte Carlo simulations.

Due to rapid increases in the number of users and diversity of devices, temporal fluctuation of traffic on information communication network is becoming large and rapid recently. Especially, sudden traffic changes such as flash crowds often cause serious congestion on the network and result in nearly fatal slow down of date-communication speed. In order to keep communication quality high on the network, routing protocols that are scalable and able to quickly respond to rapid, and often unexpected, traffic fluctuation are highly desired. One of the promising approaches is the distributed routing protocol, which works without referring global information of the whole network but requires only limited informatin of it to realize route selection. These approaches include biologically inspired routing protocols based on the Adaptive Response by Attractor Selection model (ARAS), in which routing tables are updated along with only a scalar value reflecting communication quality measured on each router without evaluating communication quality over the whole network. However, the lack of global knowledge of the current status of the network often makes it difficult to respond promptly to traffic changes on the network that occurs at outside of the local scope of the protocol and causes inefficient use of network resources. In order to solve the essential problem of the local scope, we extend ARAS and propose a routing protocol with active and stochastic route exploration. The proposed protocol can obtain current communication quality of the network beyond its local scope and promptly responds to traffic changes occur on the network by utilizing the route exploration. In order to compensate destabilization of routing itself due to the active and stochastic exploration, we also introduce a short-term memory to the dynamics of the proposed attractor selection model. We conform by numerical simulations that the proposed protocol successfully balances rapid exploration with reliable routing owning to the memory term.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

As an important extension of high-efficiency video coding (HEVC), screen content coding (SCC) includes various new coding modes, such as Intra Block Copy (IBC), Palette-based coding (Palette), and Adaptive Color Transform (ACT). These new tools have improved screen content encoding performance. This paper proposed a novel and fast algorithm by classifying Code Units (CUs) as text CUs or non-text CUs. For text CUs, the Intra mode was skipped in the compression process, whereas for non-text CUs, the IBC mode was skipped. The current CU depth range was then predicted according to its adjacent left CU depth level. Compared with the reference software HM16.7+SCM5.4, the proposed algorithm reduced encoding time by 23% on average and achieved an approximate 0.44% increase in Bjøntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.

Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.

In this paper, we consider a two-hop relay network with a decode-and-forward (DF) relaying protocol where a multi-input/multi-output (MIMO) relay station (RS) is deployed in a cell edge to extend cell coverage of a base station (BS). We propose two MIMO relaying schemes to improve the quality of the BS-RS link, which is a key to improve data rates in the DF relaying: 1) spatial multiplexed MIMO antenna relaying (SM-MAR) with a uniform channel decomposition (UCD) precoder, and 2) MIMO relaying with section diversity (SD-MAR). In the SM-MAR, we greatly simplify user allocation by the UCD precoder and propose a sophisticated rate maximization technique to resolve the non-convexity of rate maximization problems. Through simulations, we show that the proposed UCD based power allocation exhibits up to two times higher achievable throughput than other techniques. In addition, the proposed SD-MAR supports the BS with a single transmit antenna and increases the signal quality of the BS-RS link with the selection diversity at the RS, which is much simpler to be implemented. For the SD-MAR, we derive a closed form expression for the achievable throughput and show that the selection diversity plays more important role on the achievable throughput than the multiuser diversity.

With the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99 Cup competition.

In this paper, the average symbol error probability (SEP) performance of decode-and-forward (DF) relaying mobile-to-mobile (M2M) systems with transmit antenna selection (TAS) over N-Nakagami fading channels is investigated. The moment generating function (MGF) method is used to derive exact SEP expressions, and the analysis is verified via simulation. The optimal power allocation problem is investigated. Performance results are presented which show that the fading coefficient, number of cascaded components, relative geometrical gain, number of antennas, and power allocation parameter have a significant effect on the SEP.

Recently, multi-user multiple-input multiple-output (MU-MIMO) systems are being widely studied. For interference cancellation, MU-MIMO commonly uses spatial precoding techniques. These techniques, however, require the transmitters to have perfect knowledge of the downlink channel state information (CSI), which is hard to achieve in high mobility environments. Instead of spatial precoding, a collaborative interference cancellation (CIC) technique can be implemented for these environments. In CIC, mobile stations (MSs) collaborate and share their received signals to increase the demultiplexing capabilities. To obtain efficient signal-exchange between collaborating users, signal selection can be implemented. In this paper, a signal selection scheme suitable for a QRM-MLD algorithm is proposed. The proposed scheme uses the minimum Euclidean distance criterion to obtain an optimum bit error rate (BER) performance. Numerical results obtained through computer simulations show that the proposed scheme is able to provide BER performance near to that of MLD even when the number of candidates in QRM-MLD is relatively small. In addition, the proposed scheme is feasible to implement owing to its low computational complexity.

Performance evaluation of an improved multiband impulse radio ultra-wideband (MIR UWB) system based on sub-band selection is proposed in this paper. In the improved scheme, a data mapping algorithm is introduced to a conventional MIR UWB system, and out of all the sub-bands, only partial ones are selected to transmit information data, which can improve the flexibility of sub-bands/spectrum allocation, avoid interference and provide a variety of data rates. Given diagrams of a transmitter and receiver, the exact bit error rate (BER) of the improved system is derived. A comparison of system performance between the improved MIR UWB system and the conventional MIR UWB system is presented in different channels. Simulation results show that the improved system can achieve the same data rate and better BER performance than the conventional MIR UWB system under additive white Gaussian noise (AWGN), multipath fading and interference coexistence channels. In addition, different data transmission rates and BER performances can be easily achieved by an appropriate choice of system parameters.

In the future Internet of Things/M2M network, enormous amounts of data generated from sensors must be processed and utilized by cloud applications. In recent years, sensor data stream delivery, which collects and sends sensor data periodically, has been attracting great attention. As for sensor data stream delivery, the receivers have different delivery cycle requirements depending on the applications or situations. In this paper, we propose a sensor data stream delivery method to accommodate heterogeneous cycles on the cloud. The proposed method uses distributed hashing to determine relay nodes on the cloud and construct delivery paths autonomously. We evaluate the effectiveness of the proposed method in simulations. The simulation results show that the proposed method halves the maximum load of nodes compared to the baseline methods and achieves high load balancing.

In this paper, we study the impact of imperfect channel information on an amplify-and-forward (AF)-based two-way relaying network (TWRN) with adaptive modulation which consists of two end-terminals and multiple relays. Specifically, we consider a single-relay selection scheme of the TWRN in the presence of outdated channel state information (CSI) and channel estimation errors. First, we choose the best relay based on outdated CSI, and perform adaptive modulation on both relaying paths with channel estimation errors. Then, we discuss the impact of the outdated CSI on the statistics of the signal-to-noise ratio (SNR) per hop. In addition, we formulate the end-to-end SNRs with channel estimation errors and offer statistic analyses in the presence of both the outdated CSI and channel estimation errors. Finally, we provide the performance analyses of the proposed TWRN with adaptive modulation in terms of average spectral efficiency, average bit error rate, and outage probability. Numerical examples are given to verify our obtained analytical results for various system conditions.

We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the prohibitive computations by using a low-rank approximation method. The empirical results acquired by applying the proposed method to several multi-label datasets demonstrate that its performance is comparable to those of recent multi-label feature selection methods and that it reduces the computation time.

The likelihood-ratio based score level fusion (LR-based fusion) scheme has attracted much attention, since it maximizes accuracy if a log-likelihood ratio (LLR) is accurately estimated. In reality, it can happen that a user cannot input some query samples due to temporary physical conditions such as injuries and illness. It can also happen that some modalities tend to cause false rejection (i.e. the user is a “goat” for these modalities). The LR-based fusion scheme can handle these situations by setting LLRs corresponding to missing query samples to 0. In this paper, we refer to such a mode as a “modality selection mode”, and address an issue of accuracy in this mode. Specifically, we provide the following contributions: (1) We firstly propose a “modality selection attack”, in which an impostor inputs only query samples whose LLRs are more than 0 (i.e. takes an optimal strategy) to impersonate others. We also show that the impostor can perform this attack against the SPRT (Sequential Probability Ratio Test)-based fusion scheme, which is an extension of the LR-based fusion scheme to a sequential fusion scenario. (2) We secondly consider the case when both genuine users and impostors take this optimal strategy, and show that the overall accuracy in this case is “worse” than the case when they input all query samples. More specifically, we prove that the KL (Kullback-Leibler) divergence between a genuine distribution of integrated scores and an impostor's one, which can be compared with password entropy, is smaller in the former case. We also show to what extent the KL divergence losses for each modality. (3) We finally evaluate to what extent the overall accuracy becomes worse using the NIST BSSR1 Set 2 and Set 3 datasets, and discuss directions of multibiometric applications based on the experimental results.

Phishing attacks target financial returns by luring Internet users to exposure their sensitive information. Phishing originates from e-mail fraud, and recently it is also spread by social networks and short message service (SMS), which makes phishing become more widespread. Phishing attacks have drawn great attention due to their high volume and causing heavy losses, and many methods have been developed to fight against them. However, most of researches suffered low detection accuracy or high false positive (FP) rate, and phishing attacks are facing the Internet users continuously. In this paper, we are concerned about feature engineering for improving the classification performance on phishing web pages detection. We propose a novel anti-phishing framework that employs feature engineering including feature selection and feature extraction. First, we perform feature selection based on genetic algorithm (GA) to divide features into critical features and non-critical features. Then, the non-critical features are projected to a new feature by implementing feature extraction based on a two-stage projection pursuit (PP) algorithm. Finally, we take the critical features and the new feature as input data to construct the detection model. Our anti-phishing framework does not simply eliminate the non-critical features, but considers utilizing their projection in the process of classification, which is different from literatures. Experimental results show that the proposed framework is effective in detecting phishing web pages.

Unlimited requirements for system-on-chip (SoC) facilitate three-dimensional (3D) technology as a promising alternative for extending Moore's Law. In spite of many advantages 3D technology provides, 3D technology faces testing issues because of the complexity of 3D design. Therefore, resolving the problem of test optimization and reducing test cost are crucial challenges. In this paper, we propose a novel optimization mechanism of 3D SoCs to minimize test time for mid-bond testing. To make our proposed mechanism more practical, we discuss test cost in mid-bond testing with consideration of manufacturing influence factors. Experimental results on ITC'02 SoC benchmark circuits show that our proposed mechanism reduces mid-bond test time by around 73% on average compared with one baseline solution, furthermore, the mechanism also proves its capacity in test cost reduction.

In this paper, we analyze a cooperative communication network with multi energy-harvesting and decode-and-forward relays in which the best relay is selected based on criteria such as Maximizing First-Hop Signal to Noise Ratios (SNRs) (MFHS protocol), Maximizing Second-Hop SNRs (MSHS protocol), and Maximizing End-to-End SNRs (MEES protocol). In these protocols, the relays apply power-splitting receivers to harvest energy from radio frequency signals emitted from a source. Thus, each received SNR in the second hop is a function of a direct relay-destination gain and an indirect source-relay gain. The system performance of the proposed protocols is evaluated via exact outage probability analyses and Monte Carlo simulations. For further comparisons, an energy-harvesting decode-and-forward scheme with randomly relay selection (RRS protocol) and an energy-harvesting amplify-and-forward scheme (BAF protocol) are investigated and discussed. The simulation results show that 1) the MEES protocol outperforms the MFHS and MSHS protocols, and the MFHS protocol is more efficient than the MSHS protocol in the low SNR regions; 2) the proposed protocols achieve the best performance at the specific optimal power splitting ratios for which the MEES protocol has a balanced ratio for energy harvesting and decoding capacity; and 3) the theoretical analyses agree well with the simulation results.

In recent times, wireless Local Area Networks (wireless LANs) based on the IEEE 802.11 standard have been spreading rapidly, and connecting to the Internet using wireless LANs has become more common. In addition, public wireless LAN service areas, such as train stations, hotels, and airports, are increasing and tethering technology has enabled smartphones to act as access points (APs). Consequently, there can be multiple APs in the same area. In this situation, users must select one of many APs. Various studies have proposed and evaluated many AP selection methods; however, existing methods do not consider AP mobility. In this paper, we propose an AP selection method based on cooperation among APs and user movement. Moreover, we demonstrate that the proposed method dramatically improves throughput compared to an existing method.

Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.

This paper presents the design of an ad hoc two-way two-hop relay network using physical-layer network coding (PNC) in which multiple antennas are used at all nodes. In the considered network, the Alamouti's space-time block code (STBC) is used for transmission while linear detection is used for signal recovery. In order to facilitate linear estimation, we develop an equivalent multiuser STBC model for the proposed network and design the sum-and-difference matrix which allows convenient combination of the transmitted symbols from the end nodes. In addition, a simple relay selection method based on minimum mean square error (MSE) is proposed for performance improvement. Simulation results show that the proposed network achieves diversity order 2 while requiring only polynomial complexity. Moreover, it is possible to achieve significant bit error rate (BER) performance improvement when the proposed relay selection algorithm is used.