Native Mandarin normal-hearing (NH) listeners can easily perceive lexical tones even under conditions of great voice pitch variations across speakers by using the pitch contrast between context and target stimuli. It is however unclear whether cochlear implant (CI) users with limited access to pitch cues can make similar use of context pitch cues for tone normalization. In this study, native Mandarin NH listeners and pre-lingually deafened unilaterally implanted CI users were asked to recognize a series of Mandarin tones varying from Tone 1 (high-flat) to Tone 2 (mid-rising) with or without a preceding sentence context. Most of the CI subjects used a hearing aid (HA) in the non-implanted ear (i.e., bimodal users) and were tested both with CI alone and CI + HA. In the test without context, typical S-shaped tone recognition functions were observed for most CI subjects and the function slopes and perceptual boundaries were similar with either CI alone or CI + HA. Compared to NH subjects, CI subjects were less sensitive to the pitch changes in target tones. In the test with context, NH subjects had more (resp. fewer) Tone-2 responses in a context with high (resp. low) fundamental frequencies, known as the contrastive context effect. For CI subjects, a similar contrastive context effect was found statistically significant for tone recognition with CI + HA but not with CI alone. The results suggest that the pitch cues from CIs may not be sufficient to consistently support the pitch contrast processing for tone normalization. The additional pitch cues from aided residual acoustic hearing can however provide CI users with a similar tone normalization capability as NH listeners.
Organic light-emitting diode (OLED) technology is considered as a promising alternative to mobile displays. This paper ex- plores how to reduce the OLED power consumption by exploiting visual attention. First, we model the problem of OLED im- age scaling optimization, with the objective of minimizing the power required to display an image without adversely impacting the user’s visual experience. Then, we propose an algorithm to solve the fundamental problem, and prove its optimality even without the accurate power model. Finally, based on the algorithm, we consider implementation issues and realize two application scenarios on a commercial OLED mobile tablet. The results of experiments conducted on the tablet with real images demonstrate that the proposed methodology can achieve significant power savings while retaining the visual quality.
Mobile devices will provide improved computing resources to sustain progressively more complicated applications. However, the concept of fair scheduling and governing borrowed from legacy operating systems cannot be applied seamlessly in mobile systems, thereby degrading user experience or reducing energy efficiency. In this paper, we posit that mobile applications should be treated unfairly. To this end, we propose the concept of application sensitivity and devise a user-centric scheduler and governor that allocate computing resources to applications according to their sensitivity. Furthermore, we integrate our design into the Android operating system. The results of extensive experiments on a commercial smartphone with real-world mobile apps demonstrate that the proposed design can achieve significant energy efficiency gains while maintaining the quality of user experience.
With the increasing variety of mobile applications, reducing the energy consumption of mobile devices is a major challenge in sustaining multimedia streaming applications. This paper explores how to minimize the energy consumption of the backlight when displaying a video stream without adversely impacting the user's visual experience. First, we model the problem as a dynamic backlight scaling optimization problem. Then, we propose algorithms to solve the fundamental problem and prove the optimality in terms of energy savings. Finally, based on the algorithms, we present a cloud-based energy-saving service. We have also developed a prototype implementation integrated with existing video streaming applications to validate the practicability of the approach. The results of experiments conducted to evaluate the efficacy of the proposed approach are very encouraging and show energy savings of 15-49 percent on commercial mobile devices.
Coexistence of multiple radio access technologies (RATs) is a promising paradigm to improve spectral efficiency. This letter presents a game-theoretic network selection scheme in a cognitive heterogeneous networking environment with timevarying channel availability. We formulate the network selection problem as a noncooperative game with secondary users (SUs) as the players, and show that the game is an ordinal potential game (OPG). A decentralized, stochastic learning-based algorithm is proposed where each SU's strategy progressively evolves toward the Nash equilibrium (NE) based on its own action-reward history, without the need to know actions in other SUs. The convergence properties of the proposed algorithm toward an NE point are theoretically and numerically verified. The proposed algorithm demonstrates good throughput and fairness performances in various network scenarios.
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.
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.
Layer-based video coding, together with adaptive modulation and coding, is a promising technique for providing real-time video multicast services on heterogeneous mobile devices. With the rapid growth of data communications for emerging applications, reducing the energy consumption of mobile devices is a major challenge. This paper addresses the problem of resource allocation for video multicast in fourth-generation wireless systems, with the objective of minimizing the total energy consumption for data reception. First, we consider the problem when scalable video coding is applied. We prove that the problem is NP-hard and propose a 2-approximation algorithm to solve it. Then, we investigate the problem under multiple description coding, and show that it is also NP-hard and cannot be approximated in polynomial time with a ratio better than 2, unless P=NP. To solve this case, we develop a pseudopolynomial time 2-approximation algorithm. The results of simulations conducted to compare the proposed algorithms with a brute-force optimal algorithm and a conventional approach are very encouraging.