In this paper, an adaptive updating probabilistic model is proposed to track an object in real-world environment that includes motion blur, illumination changes, pose variations, and occlusions. This model adaptively updates tracker with the searching and updating process. The searching process focuses on how to learn appropriate tracker and updating process aims to correct it as a robust and efficient tracker in unconstrained real-world environments. Specifically, according to various changes in an object's appearance and recent probability matrix (TPM), tracker probability is achieved in Expectation-Maximization (EM) manner. When the tracking in each frame is completed, the estimated object's state is obtained and then fed into update current TPM and tracker probability via running EM in a similar manner. The highest tracker probability denotes the object location in every frame. The experimental result demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments.

A dual-hop amplify-and-forward (AF) relaying system with beamforming is analyzed over η-µ fading channels that includes Nakagami-m, Nakagami-q (Hoyt), and Rayleigh fading channels as special cases. New and exact expressions for the outage probability (OP) and average capacity are derived. Moreover, a new asymptotic analysis is also conducted for the OP and average capacity in terms of basic elementary functions which make it easy to understand the system behavior and the impact of channel parameters. The viability of the analysis is verified by Monte Carlo simulations.

This paper addresses the attribute recognition problem, a field of research that is dominated by studies in the visible spectrum. Only a few works are available in the thermal spectrum, which is fundamentally different from the visible one. This research performs recognition specifically on wearable attributes, such as glasses and masks. Usually these attributes are relatively small in size when compared with the human body, on top of a large intra-class variation of the human body itself, therefore recognizing them is not an easy task. Our method utilizes a decomposition framework based on Robust Principal Component Analysis (RPCA) to extract the attribute information for recognition. However, because it is difficult to separate the body and the attributes without any prior knowledge, noise is also extracted along with attributes, hampering the recognition capability. We made use of prior knowledge; namely the location where the attribute is likely to be present. The knowledge is referred to as the Probability Map, incorporated as a weight in the decomposition by RPCA. Using the Probability Map, we achieve an attribute-wise decomposition. The results show a significant improvement with this approach compared to the baseline, and the proposed method achieved the highest performance in average with a 0.83 F-score.

In this paper, we present several cryptanalyses of Hierocrypt-L1 block cipher, which was selected as one of the CRYPTREC recommended ciphers in Japan in 2003. We present a differential attack and an impossible differential attack on 8 S-function layers in a related-key setting. We first show that there exist the key scheduling differential characteristics which always hold, then we search for differential paths for the data randomizing part with the minimum active S-boxes using the above key differentials. We also show that our impossible differential attack is a new type.

We investigate the coding scheme and error probability in information transmission over continuous-time additive Gaussian noise channels with feedback. As is known, the error probability can be substantially reduced by using feedback, namely, under the average power constraint, the error probability may decrease more rapidly than the exponential of any order. Recently Gallager and Nakibolu proposed, for discrete-time additive white Gaussian noise channels, a feedback coding scheme such that the resulting error probability Pe(N) at time N decreases with an exponential order αN which is linearly increasing with N. The multiple-exponential decay of the error probability has been studied mostly for white Gaussian channels, so far. In this paper, we treat continuous-time Gaussian channels, where the Gaussian noise processes are not necessarily white nor stationary. The aim is to prove a stronger result on the multiple-exponential decay of the error probability. More precisely, for any positive constant α, there exists a feedback coding scheme such that the resulting error probability Pe(T) at time T decreases more rapidly than the exponential of order αT as T→∞.

In this paper, we identify the tradeoff between security and reliability in the amplify-and-forward (AF) distributed beamforming (DBF) cooperative network with K untrusted relays. In particular, we derive the closed-form expressions for the connection outage probability (COP), the secrecy outage probability (SOP), the tradeoff relationship, and the secrecy throughput. Analytical and simulation results demonstrate that increasing K leads to the enhancement of the reliability performance, but the degradation of the security performance. This tradeoff also means that there exists an optimal K maximizing the secrecy throughput.

We treat lossless fixed-to-variable length source coding under general sources for finite block length setting. We evaluate the threshold of the overflow probability for prefix and non-prefix codes in terms of the smooth max-entropy. We clarify the difference of the thresholds between prefix and non-prefix codes for finite block length. Further, we discuss our results under the asymptotic block length setting.

Spatial stochastic models have been much used for performance analysis of wireless communication networks. This is due to the fact that the performance of wireless networks depends on the spatial configuration of wireless nodes and the irregularity of node locations in a real wireless network can be captured by a spatial point process. Most works on such spatial stochastic models of wireless networks have adopted homogeneous Poisson point processes as the models of wireless node locations. While this adoption makes the models analytically tractable, it assumes that the wireless nodes are located independently of each other and their spatial correlation is ignored. Recently, the authors have proposed to adopt the Ginibre point process — one of the determinantal point processes — as the deployment models of base stations (BSs) in cellular networks. The determinantal point processes constitute a class of repulsive point processes and have been attracting attention due to their mathematically interesting properties and efficient simulation methods. In this tutorial, we provide a brief guide to the Ginibre point process and its variant, α-Ginibre point process, as the models of BS deployments in cellular networks and show some existing results on the performance analysis of cellular network models with α-Ginibre deployed BSs. The authors hope the readers to use such point processes as a tool for analyzing various problems arising in future cellular networks.

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.

In this work, a high efficiency coding unit (CU) size decision algorithm is proposed for high efficiency video coding (HEVC) inter coding. The CU splitting or non-splitting is modeled as a binary classification problem based on probability graphical model (PGM). This method incorporates two sub-methods: CU size termination decision and CU size skip decision. This method focuses on the trade-off between encoding efficiency and encoding complexity, and it has a good performance. Particularly in the high resolution application, simulation results demonstrate that the proposed algorithm can reduce encoding time by 53.62%-57.54%, while the increased BD-rate are only 1.27%-1.65%, compared to the HEVC software model.

This paper proposes statistical analysis of phase-only correlation functions with phase-spectrum differences following wrapped distributions. We first assume phase-spectrum differences between two signals to be random variables following a linear distribution. Next, based on directional statistics, we convert the linear distribution into a wrapped distribution by wrapping the linear distribution around the circumference of the unit circle. Finally, we derive general expressions of the expectation and variance of the POC functions with phase-spectrum differences following wrapped distributions. We obtain exactly the same expressions between a linear distribution and its corresponding wrapped distribution.

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.

An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.

This paper proposes and theoretically analyzes the performance of amplify-and-forward (AF) relaying free-space optical (FSO) systems using avalanche photodiode (APD) over atmospheric turbulence channels. APD is used at each relay node and at the destination for optical signal conversion and amplification. Both serial and parallel relaying configurations are considered and the subcarrier binary phase-shift keying (SC-BPSK) signaling is employed. Closed-form expressions for the outage probability and the bit-error rate (BER) of the proposed system are analytically derived, taking into account the accumulating amplification noise as well as the receiver noise at the relay nodes and at the destination. Monte-Carlo simulations are used to validate the theoretical analysis, and an excellent agreement between the analytical and simulation results is confirmed.

In the paradigm of network coding, when the network topology information cannot be utilized completely, random linear network coding (RLNC) is proposed as a feasible coding scheme. But since RLNC neither considers the global network topology nor coordinates codings between different nodes, it may not achieve the best possible performance of network coding. Hence, the performance analysis of RLNC is very important for both theoretical research and practical applications. Motivated by a fact that different network topology information can be available for different network communication problems, we study and obtain several upper and lower bounds on the failure probability at sink nodes depending on different network topology information in this paper, which is also the kernel to discuss some other types of network failure probabilities. In addition, we show that the obtained upper bounds are tight, the obtained lower bound is asymptotically tight, and we give the worst cases for different scenarios.

Human body segmentation has many applications in a wide variety of image processing tasks, from intelligent vehicles to entertainment. A substantial amount of research has been done in the field of segmentation and it is still one of the active research areas, resulting in introduction of many innovative methods in literature. Still, until today, a method that can overcome the human segmentation problems and adapt itself to different kinds of situations, has not been introduced. Many of methods today try to use the graph-cut framework to solve the segmentation problem. Although powerful, these methods rely on a distance penalty term (intensity difference or RGB color distance). This term does not always lead to a good separation between two regions. For example, if two regions are close in color, even if they belong to two different objects, they will be grouped together, which is not acceptable. Also, if one object has multiple parts with different colors, e.g. humans wear various clothes with different colors and patterns, each part will be segmented separately. Although this can be overcome by multiple inputs from user, the inherent problem would not be solved. In this paper, we have considered solving the problem by making use of a human probability map, super-pixels and Grab-cut framework. Using this map relives us from the need for matching the model to the actual body, thus helps to improve the segmentation accuracy. As a result, not only the accuracy has improved, but also it also became comparable to the state-of-the-art interactive methods.

This paper claims to use a new question expansion method for question classification in cQA services. The input questions consist of only a question whereas training data do a pair of question and answer. Thus they cannot provide enough information for good classification in many cases. Since the answer is strongly associated with the input questions, we try to create a pseudo answer to expand each input question. Translation probabilities between questions and answers and a pseudo relevant feedback technique are used to generate the pseudo answer. As a result, we obtain the significant improved performances when two approaches are effectively combined.

Research into closed-form Gaussian sum smoother has provided an attractive approach for tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density (PHD). However, Gaussian sum smoother with nonlinear target model has particular nonlinear expressions in the backward smoothed density that are different from the other filters and smoothers. In order to extend the closed-form solution of linear Gaussian sum smoother to nonlinear model, two closed-form approximations for nonlinear Gaussian sum smoother are proposed, which use Gaussian particle approximation and unscented transformation approximation, separately. Since the estimated target number of PHD smoother is not stable, a heuristic approximation method is added. At last, the Bernoulli smoother and PHD smoother are simulated using Gaussian particle approximation and unscented transformation approximation, and simulation results show that the two proposed algorithms can obtain smoothed tracks with nonlinear models, and have better performance than filter.

In this paper, we investigate the effect of outdated channel state information (CSI) on decode-and-forward opportunistic mobile relaying networks with direct link (DL) between source node and destination node. Relay selection schemes with different levels of CSI are considered: 1) only outdated CSI is available during the relay selection procedure; 2) not only outdated CSI but also second-order statistics information are available in relay selection process. Three relay selection schemes are proposed based on the two levels of outdated CSI. Closed-form expressions of the outage probability are derived for the proposed relay selection schemes. Meanwhile, the asymptotic behavior and the achievable diversity of three relay selection schemes are analyzed. Finally, simulation results are presented to verify our analytical results.

Zero-shot learning refers to the object classification problem where no training samples are available for testing classes. For zero-shot learning, attribute transfer plays an important role in recognizing testing classes. One popular method is the indirect attribute prediction (IAP) model, which assumes that all attributes are independent and equally important for learning the zero-shot image classifier. However, a more practical assumption is that different attributes contribute unequally to the classifier learning. We therefore propose assigning different weights for the attributes based on the relevance probabilities between the attributes and the classes. We incorporate such weighed attributes to IAP and propose a relevance probability-based indirect attribute weighted prediction (RP-IAWP) model. Experiments on four popular attributed-based learning datasets show that, when compared with IAP and RFUA, the proposed RP-IAWP yields more accurate attribute prediction and zero-shot image classification.