Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called “conditional information leakage given the eavesdropper's received signals,” which expresses the amount of information that an eavesdropper gains from his/her received signal. A benefit of introducing this quantity is that we can develop a fast algorithm for computing the conditional information leakage, which has linear complexity in the code length n, while the complexity for computing the usual information leakage is exponential in n. Validity of such a conditional information leakage as a security criterion is confirmed by studying the cases of binary symmetric channels and binary erasure channels.

In recent years, with the improvement of health awareness, people have paid more and more attention to proper meal. Existing research has shown that a proper meal can help people prevent lifestyle diseases such as diabetes. In this research, by attaching sensors to the tableware, the information during the meal can be captured, and after processing and analyzing it, the meal information, such as time and sequence of meal, can be obtained. This paper introduces how to use supervised learning and multi-instance learning to deal with meal information and a detailed comparison is made. Three supervised learning algorithms and two multi-instance learning algorithms are used in the experiment. The experimental results showed that although the supervised learning algorithms have achieved good results in F-score, the multi-instance learning algorithms have achieved better results not only in accuracy but also in F-score.

Green cognitive radio (CR) plays an important role in offering secondary users (SUs) with more spectrum with smaller energy expenditure. However, the energy efficiency (EE) issues associated with green CR for fading channels have not been fully studied. In this paper, we investigate the average EE maximization problem for spectrum-sharing CR in fading channels. Unlike previous studies that considered either the peak or the average transmission power constraints, herein, we considered both of these constraints. Our aim is to maximize the average EE of SU by optimizing the transmission power under the joint peak and average transmit power constraints, the rate constraint of SU and the quality of service (QoS) constraint of primary user (PU). Specifically, the QoS for PU is guaranteed based on either the average interference power constraint or the PU outage constraint. To address the non-convex optimization problem, an iterative optimal power allocation algorithm that can tackle the problem efficiently is proposed. The optimal transmission powers are identified under both of perfect and imperfect channel side information (CSI). Simulations show that our proposed scheme can achieve higher EE over the existing scheme and the EE achieved under perfect CSI is better than that under imperfect CSI.

In this paper, the artificial noise (AN)-aided multiple-input single-output (MISO) cognitive radio network with simultaneous wireless information and power transfer (SWIPT) is considered, in which the cognitive user adopts the power-splitting (PS) receiver architecture to simultaneously decode information and harvest energy. To support secure communication and facilitate energy harvesting, AN is transmitted with information signal at cognitive base station (CBS). The secrecy energy efficiency (SEE) maximization problem is formulated with the constraints of secrecy rate and harvested energy requirements as well as primary user's interference requirements. However, this challenging problem is non-convex due to the fractional objective function and the coupling between the optimization variables. For tackling the challenging problem, a double-layer iterative optimization algorithm is developed. Specifically, the outer layer invokes a one-dimension search algorithm for the newly introduced tight relaxation variable, while the inner one leverages the Dinkelbach method to make the fractional optimization problem more tractable. Furthermore, closed-form expressions for the power of information signal and AN are obtained. Numerical simulations are conducted to demonstrate the efficiency of our proposed algorithm and the advantages of AN in enhancing the SEE performance.

The eyes are continuously fluctuating during fixation. These fluctuations are called fixational eye movements. Fixational eye movements consist of tremors, microsaccades, and ocular drifts. Fixational eye movements aid our vision by shaping spatial-temporal characteristics. Here, it is known that photoreceptors, the first input layer of the retinal network, have a spatially non-uniform cell alignment called the cone mosaic. The roles of fixational eye movements are being gradually uncovered; however, the effects of the cone mosaic are not considered. Here we constructed a large-scale visual system model to explore the effect of the cone mosaic on the visual signal processing associated with fixational eye movements. The visual system model consisted of a brainstem, eye optics, and photoreceptors. In the simulation, we focused on the roles of fixational eye movements on signal processing with sparse sampling by photoreceptors given their spatially non-uniform mosaic. To analyze quantitatively the effect of fixational eye movements, the capacity of information processing in the simulated photoreceptor responses was evaluated by information rate. We confirmed that the information rate by sparse sampling due to the cone mosaic was increased with fixational eye movements. We also confirmed that the increase of the information rate was derived from the increase of the responses for the edges of objects. These results suggest that visual information is already enhanced at the level of the photoreceptors by fixational eye movements.

Outlier detection in a data set is very important in performing proper data mining. In this paper, we propose a method for efficiently detecting outliers by performing cluster analysis using the DS algorithm improved from the k-means algorithm. This method is simpler to detect outliers than traditional methods, and these detected outliers can quantitatively indicate “the degree of outlier”. Using this method, we detect abnormal trading days from OHLCs for S&P500 and FTSA, which are typical and world-wide stock indexes, from the beginning of 2005 to the end of 2015. They are defined as non-steady trading days, and the conditions for becoming the non-steady markets are mined as new knowledge. Applying the mined knowledge to OHLCs from the beginning of 2016 to the end of 2018, we can predict the non-steady trading days during that period. By verifying the predicted content, we show the fact that the appropriate knowledge has been successfully mined and show the effectiveness of the outlier detection method proposed in this paper. Furthermore, we mutually reference and comparatively analyze the results of applying this method to multiple stock indexes. This analyzes possible to visualize when and where social and economic impacts occur and how they propagate through the earth. This is one of the new applications using this method.

With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.

This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a simultaneous wireless information and power transfer (SWIPT) system, wherein a legitimate user (LU) exploits the power splitting (PS) scheme for simultaneous information decoding (ID) and energy harvesting (EH). To prevent interference from eavesdroppers on the LU, artificial noise (AN) is incorporated into the confidential signal at the transmitter. We maximize the secrecy energy efficiency (SEE) by joining the power of the confidential signal, the AN power, and the PS ratio, while taking into account the minimum secrecy rate requirement of the LU, the required minimum harvested energy, the allowed maximum radio frequency transmission power, and the PS ratio. The formulated SEEM problem involves nonconvex fractional programming and is generally intractable. Our solution is Lagrangian relaxation method than can transform the original problem into a two-layer optimization problem. The outer layer problem is a single variable optimization problem with a Lagrange multiplier, which can be solved easily. Meanwhile, the inner layer one is fractional programming, which can be transformed into a subtractive form solved using the Dinkelbach method. A closed-form solution is derived for the power of the confidential signal. Simulation results verify the efficiency of the proposed SEEM algorithm and prove that AN-aided design is an effective method for improving system SEE.

The outcome of document clustering depends on the scheme used to assign a weight to each term in a document. While recent works have tried to use distributions related to class to enhance the discrimination ability. It is worth exploring whether a deviation approach or an entropy approach is more effective. This paper presents a comparison between deviation-based distribution and entropy-based distribution as constraints in term weighting. In addition, their potential combinations are investigated to find optimal solutions in guiding the clustering process. In the experiments, the seeded k-means method is used for clustering, and the performances of deviation-based, entropy-based, and hybrid approaches, are analyzed using two English and one Thai text datasets. The result showed that the deviation-based distribution outperformed the entropy-based distribution, and a suitable combination of these distributions increases the clustering accuracy by 10%.

Information floating (IF) permits mobile nodes to transmit information to other nodes by direct wireless communication only in transmittable areas (TAs), thus avoiding unneeded and inefficient information distribution to irrelevant areas, which is a problem with the so-called epidemic communication used in delay tolerant networks. In this paper, we propose applying IF to sensor networking to find and share available routes in disaster situations. In this proposal, IF gathers and shares information without any assistance from gateways, which is normally required for conventional wireless sensor networks. A performance evaluation based on computer simulation results is presented. Furthermore, we demonstrate that the proposed method is effective by highlighting its advantageous properties and directly comparing it with a method based on epidemic communication. Our findings suggest that the proposed method is a promising step toward more effective countermeasures against restricted access in disaster zones.

In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.

Secret key generation based on channel characteristics is an effective physical-layer security method for 5G wireless networks. The issues of how to ensure the high key generation rate and correlation of the secret key under active attack are needed to be addressed. In this paper, a new practical secret key generation scheme with high rate and correlation is proposed. In our proposed scheme, Alice and Bob transmit independent random sequences instead of known training sequences or probing signals; neither Alice nor Bob can decode these random sequences or estimate the channel. User's random sequences together with the channel effects are used as common random source to generate the secret key. With this solution, legitimate users are able to share secret keys with sufficient length and high security under active attack. We evaluate the proposed scheme through both analytic and simulation studies. The results show that our proposed scheme achieves high key generation rate and key security, and is suitable for 5G wireless networks with resource-constrained devices.

For high transmission efficiency, good modulation schemes are expected. This paper focuses on the enhancement of the modulation scheme of free space optical turbo coded system. A free space optical turbo coded system using a new signaling scheme called hybrid PPM-OOK signaling (HPOS) is proposed and investigated. The theoretical formula of the bit error rate of the uncoded HPOS system is derived. The effective information rate performances (i.e. channel capacity) of the proposed HPOS turbo coded system are evaluated through computer simulation in free space optical channel, with weak, moderate, strong scintillation. The performance of the proposed HPOS turbo coded system is compared with those of the conventional OOK (On-Off Keying) turbo coded system and BPPM (Binary Pulse Position Modulation) turbo coded system. As results, the proposed HPOS turbo coded system shows the same tolerance capability to background noise and atmospheric turbulence as the conventional BPPM turbo coded system, and it has 1.5 times larger capacity.

The natural gradient descent is an optimization method for real-valued neural networks that was proposed from the viewpoint of information geometry. Here, we present an extension of the natural gradient descent to complex-valued neural networks. Our idea is to use the Hermitian extension of the Fisher information matrix. Moreover, we generalize the projected natural gradient (PRONG), which is a fast natural gradient descent algorithm, to complex-valued neural networks. We also consider the advantage of complex-valued neural networks over real-valued neural networks. A useful property of complex numbers in the complex plane is that the rotation is simply expressed by the multiplication. By focusing on this property, we construct the output function of complex-valued neural networks, which is invariant even if the input is changed to its rotated value. Then, our complex-valued neural network can learn rotated data without data augmentation. Finally, through simulation of online character recognition, we demonstrate the effectiveness of the proposed approach.

We have proposed a generic architecture that can integrate the aspects of confidentiality and integrity into the A/D conversion framework. A conceptual account of the development of the proposed architecture is presented. Using the principle of this architecture we have presented a CMOS circuit design to facilitate a fully integrated Authenticated-Encrypted ADC (AE-ADC). We have implemented and demonstrated a partial 8-bit ADC Analog Front End of this proposed circuit in 0.18µm CMOS with an ENOB of 7.64 bits.

In this paper, we consider a source coding with side information partially used at the decoder through a codeword. We assume that there exists a relative delay (or gap) of the correlation between the source sequence and side information. We also assume that the delay is unknown but the maximum of possible delays is known to two encoders and the decoder, where we allow the maximum of delays to change by the block length. In this source coding, we give an inner bound and an outer bound on the achievable rate region, where the achievable rate region is the set of rate pairs of encoders such that the decoding error probability vanishes as the block length tends to infinity. Furthermore, we clarify that the inner bound coincides with the outer bound when the maximum of delays for the block length converges to a constant.

A program is non-interferent if it leaks no secret information to an observable output. However, non-interference is too strict in many practical cases and quantitative information flow (QIF) has been proposed and studied in depth. Originally, QIF is defined as the average of leakage amount of secret information over all executions of a program. However, a vulnerable program that has executions leaking the whole secret but has the small average leakage could be considered as secure. This counter-intuition raises a need for a new definition of information leakage of a particular run, i.e., dynamic leakage. As discussed in [5], entropy-based definitions do not work well for quantifying information leakage dynamically; Belief-based definition on the other hand is appropriate for deterministic programs, however, it is not appropriate for probabilistic ones.In this paper, we propose new simple notions of dynamic leakage based on entropy which are compatible with existing QIF definitions for deterministic programs, and yet reasonable for probabilistic programs in the sense of [5]. We also investigated the complexity of computing the proposed dynamic leakage for three classes of Boolean programs. We also implemented a tool for QIF calculation using model counting tools for Boolean formulae. Experimental results on popular benchmarks of QIF research show the flexibility of our framework. Finally, we discuss the improvement of performance and scalability of the proposed method as well as an extension to more general cases.

This paper addresses the association problem of tracking closely spaced targets in group or formation. In the Labeled Multi-Bernoulli Filter (LMB), the weight of a hypothesis is directly affected by the distance between prediction and measurement. This may generate false associations when dealing with the closely spaced multiple targets. Thus we consider utilizing structure information among the group or formation. Since, the relative position relation of the targets in group or formation varies slightly within a short time, the targets are considered as nodes of a topological structure. Then the position relation among the targets is modeled as a hypergraph. The hypergraph matching method is used to resolve the association matrix. At last, with the structure prior information introduced, the new joint cost matrix is re-derived to generate hypotheses, and the filtering recursion is implemented in a Gaussian mixture way. The simulation results show that the proposed algorithm can effectively deal with group targets and is superior to the LMB filter in tracking precision and accuracy.

In this paper, we focus on a large-scale ICN (Information-Centric Networking), and reveal the scaling property of ICN. Because of in-network content caching, ICN is a sort of cache networks and expected to be a promising architecture for replacing future Internet. To realize a global-scale (e.g., Internet-scale) ICN, it is crucial to understand the fundamental properties of such large-scale cache networks. However, the scaling property of ICN has not been well understood due to the lack of theoretical foundations and analysis methodologies. For answering research questions regarding the scaling property of ICN, we derive the cache hit probability at each router, the average content delivery delay of each entity, and the average content delivery delay of all entities over a content distribution tree comprised of a single repository (i.e., content provider), multiple routers, and multiple entities (i.e., content consumers). Through several numerical examples, we investigate the effect of the topology and the size of the content distribution tree and the cache size at routers on the average content delivery delay of all entities. Our findings include that the average content delivery delay of ICNs converges to a constant value if the cache size of routers are not small, which implies high scalability of ICNs, and that even when the network size would grow indefinitely, the average content delivery delay is upper-bounded by a constant value if routers in the network are provided with a fair amount of content caches.

Information-Centric Networking (ICN) can offer rich functionalities to the network, e.g, in-network caching, and name-based forwarding. Incremental deployment of ICN is a key challenge that enable smooth migration from current IP network to ICN. We can say that Network Function Virtualization (NFV) must be one of the key technologies to achieve this deployment because of its flexibility to support new network functions. However, when we consider the ICN deployment with NFV, there exist two performance issues, processing delay of name-based forwarding and computational overhead of virtual machine. In this paper we proposed a NFV infrastructure-assisted ICN packet forwarding by integrating the name look-up to the Open vSwitch. The contributions are twofold: 1) First, we provide the novel name look-up scheme that can forward ICN packets without costly longest prefix match searching. 2) Second, we design the ICN packet forwarding scheme that integrates the partial name look-up into the virtualization infrastructure to mitigate computation overhead.