Reducing the energy consumption of the emerging genre of smart handheld devices while simultaneously maintaining mobile applications and services is a major challenge. This work is inspired by an observation on the resource usage patterns of mobile applications. In contrast to existing DVFS scheduling algorithms and history-based prediction techniques, we propose a resource-driven DVFS scheme in which resource state machines are designed to model the resource usage patterns in an online fashion to guide DVFS. We have implemented the proposed scheme on Android smartphones and conducted experiments based on real-world applications. The results are very encouraging and demonstrate the efficacy of the proposed scheme.
In this paper, we study a coalitional game approach to resource allocation in a multi-channel cooperative cognitive radio network with multiple primary users (PUs) and secondary users (SUs). We propose to form the grand coalition by grouping all PUs and SUs in a set, where each PU can lease its spectrum to all SUs in a time-division manner while the SUs in return assist PUs' data transmission as relays. We use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoffs among the users. Due to the convexity of the proposed game, the Shapley value is shown to be in the core. We derive the optimal strategy for the SU, i.e., transmitting its own data or serving as a relay, that maximizes the sum rate of all PUs and SUs. The payoff allocations according to the core and the Shapley value are illustrated by an example, which demonstrates the benefits of forming the grand coalition as compared with non-coalition and other coalition schemes.
In this paper, we consider a two-way relay network in which two users exchange messages through a single relay using a physical-layer network coding (PNC) based protocol. The protocol comprises two phases of communication. In the multiple access (MA) phase, two users transmit their modulated signals concurrently to the relay, and in the broadcast (BC) phase, the relay broadcasts a network-coded (denoised) signal to both users. Nonbinary and binary network codes are considered for uniform and nonuniform pulse amplitude modulation (PAM) adopted in the MA phase, respectively. We examine the effect of different choices of symbol mapping (i.e., mapping from the denoised signal to the modulation symbols at the relay) and bit mapping (i.e., mapping from the modulation symbols to the source bits at the user) on the system error-rate performance. A general optimization framework is proposed to determine the optimal symbol/bit mappings with joint consideration of noisy transmissions in both communication phases. Complexity-reduction techniques are developed for solving the optimization problems. It is shown that the optimal symbol/bit mappings depend on the signal-to-noise ratio (SNR) of the channel and the modulation scheme. A general strategy for choosing good symbol/bit mappings is also presented based on a high-SNR analysis, which suggests using a symbol mapping that aligns the error patterns in both communication phases and Gray and binary bit mappings for uniform and nonuniform PAM, respectively.
In this paper, we present an automatic foreground object detection method for videos captured by freely moving cameras. While we focus on extracting a single foreground object of interest throughout a video sequence, our approach does not require any training data nor the interaction by the users. Based on the SIFT correspondence across video frames, we construct robust SIFT trajectories in terms of the calculated foreground feature point probability. Our foreground feature point probability is able to determine candidate foreground feature points in each frame, without the need of user interaction such as parameter or threshold tuning. Furthermore, we propose a probabilistic consensus foreground object template (CFOT), which is directly applied to the input video for moving object detection via template matching. Our CFOT can be used to detect the foreground object in videos captured by a fast moving camera, even if the contrast between the foreground and background regions is low. Moreover, our proposed method can be generalized to foreground object detection in dynamic backgrounds, and is robust to viewpoint changes across video frames. The contribution of this paper is trifold: (1) we provide a robust decision process to detect the foreground object of interest in videos with contrast and viewpoint variations; (2) our proposed method builds longer SIFT trajectories, and this is shown to be robust and effective for object detection tasks; and (3) the construction of our CFOT is not sensitive to the initial estimation of the foreground region of interest, while its use can achieve excellent foreground object detection results on real-world video data.
Multimedia Broadcast/Multicast Service (MBMS) is a bandwidth efficient broadcast scheme for multimedia communications. To support prioritized transmissions, the unequal error protection (UEP) for multi-resolution multimedia sources can be realized through MBMS. Nevertheless, the enhancement on the transmission fidelity in base layer typically sacrifices the fidelity of enhancement layers. Herein, a novel dual diversity space-time coding (DDSTC) is proposed to exploit the intrinsic UEP capability of space-time codes by utilizing a constellation mapping duo for two consecutive transmission periods in multiple-input multiple-output (MIMO) systems. As compared with Alamouti coding, the DDSTC achieves coding gains on the transmission error rates of base layer without significant degradations on the enhancement layers. At the transmission rates of base and enhancement layers equal to 2 bits per transmission, the DDSTC obtains 1.3 dB and 3.0 dB coding gains for base layer in $2 \times 2$ and $2 \times 3$ MIMO systems respectively. Besides, analytical analysis on symbol error probabilities verifies that 6 dB asymptotic coding gain is reachable in rich transmit diversity scenarios. While attaining the considerable improvements on error rates , the DDSTC avoids the high decoding complexity by adopting our proposed decoding schemes. Simulation results show that DDSTC outperforms conventional UEP schemes based on hierarchical modulations or power allocations.
Linear discriminant analysis (LDA) is a popular supervised dimension reduction algorithm, which projects the data into an effective low-dimensional linear subspace while the separation between the projected data from different classes is improved. While this subspace is typically determined by solving a generalized eigenvalue decomposition problem, its high computation costs prohibit the use of LDA especially when the scale and the dimensionality of the data are large. Based on the recent success of least squares LDA (LSLDA), we propose a novel rank-one update method with a simplified class indicator matrix. Using the proposed algorithm, we are able to derive the LSLDA model efficiently. Moreover, our LSLDA model can be extended to address the learning task of concept drift, in which the recently received data exhibit with gradual or abrupt changes in distribution. In other words, our LSLDA is able to observe and model the data distribution changes, while the dependency on outdated data will be suppressed. This proposed LSLDA will benefit applications of streaming data classification or mining, and it can recognize data with newly added class labels during the learning process. Experimental results on both synthetic and real datasets (with and without concept drift) confirm the effectiveness of our propose LSLDA.
One of the most exciting but challenging endeavors in music research is to develop a computational model that comprehends the affective content of music signals and organizes a music collection according to emotion. In this paper, we propose a novel acoustic emotion Gaussians (AEG) model that defines a proper generative process of emotion perception in music. As a generative model, AEG permits easy and straightforward interpretations of the model learning processes. To bridge the acoustic feature space and music emotion space, a set of latent feature classes, which are learned from data, is introduced to perform the end-to-end semantic mappings between the two spaces. Based on the space of latent feature classes, the AEG model is applicable to both automatic music emotion annotation and emotion-based music retrieval. To gain insights into the AEG model, we also provide illustrations of the model learning process. A comprehensive performance study is conducted to demonstrate the superior accuracy of AEG over its predecessors, using two emotion annotated music corpora MER60 and MTurk. Our results show that the AEG model outperforms the state-of-the-art methods in automatic music emotion annotation. Moreover, for the first time a quantitative evaluation of emotion-based music retrieval is reported.
A major challenge in the design of multicore embedded systems is how to tackle the communications among tasks with performance requirements and precedence constraints. In this paper, we consider the problem of scheduling real-time tasks over multilayer bus systems with the objective of minimizing the communication cost. We show that the problem is NP-hard and determine the best possible approximation ratio of approximation algorithms. First, we propose a polynomial-time optimal algorithm for a restricted case where one multilayer bus, and the unit execution time and communication time are considered. The result is then extended as a pseudopolynomial-time optimal algorithm to consider multiple multilayer buses with arbitrary execution and communication times, as well as different timing constraints and objective functions. We compare the performance of the proposed algorithm with that of some popular heuristics, and provide further insights into the multilayer bus system design.
We present a framework to count the number of people in an environment where multiple cameras with different angles of view are available. We consider the visual cues captured by each camera as a knowledge source, and carry out cross-camera knowledge transfer to alleviate the difficulties of people counting, such as partial occlusions, low-quality images, clutter backgrounds, and so on. Specifically, this work can distinguish itself with the following contributions. First, we overcome the variations of multiple heterogeneous cameras with different perspective settings by matching the same groups of pedestrians taken by these cameras, and present an algorithm for accomplishing cross-camera correspondence. Second, the proposed counting model is composed of a pair of collaborative regressors. While one regressor measures the people count by features extracted from the intra-camera visual evidences, the other recovers the yielded residual by taking the conflicts among inter-camera predictions into account. The two regressors are elegantly coupled, and jointly lead to an accurate counting system. Besides, we provide a set of manually annotated pedestrian labels on PETS 2010 videos for performance evaluation. Our approach is comprehensively tested in various settings and compared with competitive baselines. The significant improvement in performance manifests the effectiveness of the proposed approach.
Most existing studies on music mood classification have been focusing on Western music while little research has investigated whether mood categories, audio features, and classification models developed from Western music are applicable to non-Western music. This paper attempts to answer this question through a comparative study on English and Chinese songs. Specifically, a set of Chinese pop songs were annotated using an existing mood taxonomy developed for English songs. Six sets of audio features commonly used on Western music (e.g., timbre, rhythm) were extracted from both Chinese and English songs, and mood classification performances based on these feature sets were compared. In addition, experiments were conducted to test the generalizability of classification models across English and Chinese songs. Results of this study shed light on cross-cultural applicability of research results on music mood classification.