Search

Packet loss-resilient and security are two major challenges faced by real-time audio transmission over IP networks. Due to the capability of recovering the signal from a small set of samplings and the randomness in the acquisition process, compressive sensing (CS) has a vast prospect in dealing with these problems. In this paper, we propose a secure and packet loss-resistant real-time audio transmission framework (CS-SPT) based on the principle of CS. Inspired by the interleaving technique, an ultralow complexity scrambling matrix was adopted in the proposed CS-SPT to improve its packet loss-resilient capability by increasing the information redundancy. Moreover, the energy of ciphertext is homogenized using a diffusion operation. Experimental results show that compared with existing methods, the proposed CS-SPT not only improves the packet loss-resilient ability significantly but also can resist several major attacks, such as COAs and KPAs.

In recent years, neuroimaging studies have remarkably demonstrated the presence of cognitive motor dissociation in patients with disorders of consciousness (DoC). These findings accelerated the development of brain–computer interfaces (BCIs) as clinical tools for behaviorally unresponsive patients. This article reviews the recent progress of BCIs in patients with DoC and discusses the open challenges. In view of the practical application of BCIs in patients with DoC, four aspects of the relevant literature are introduced: consciousness detection, auxiliary diagnosis, prognosis, and rehabilitation. For each aspect, the paradigm design, brain signal processing methods, and experimental results of representative BCI systems are analyzed. Furthermore, this article provides guidance for BCI design for patients with DoC and discusses practical challenges for future research.

Purpose Based on a theoretical framework of ambidexterity in technology sourcing beyond organizational and technological boundaries, this study aims to explore how start-ups balance technology sourcing in organizational ambidexterity (TSOA) to produce high venture performance. Design/methodology/approach A questionnaire survey is distributed among start-ups in the science and technology park of a university in eastern China, producing a total of 45 valid responses. The fuzzy-set approach to qualitative comparative analysis is used in this study. Findings The findings show that start-ups achieve high venture performance through external technology sourcing (external exploration and exploitation) in the initial stage. In the growth stage, start-ups adopt external and internal technology sourcing (internal exploration and exploitation) to produce high venture performance. The technology sourcing strategy in ambidextrous activity for start-ups is punctuated equilibrium and evolving from the external ambidexterity to internally and externally coordinate ambidexterity at entrepreneurial stages. Originality/value This study creatively adopts configuration-based thinking to investigate how to balance TSOA for high venture performance, extending the literature on technology sourcing and contributing to the balance theory of exploration and exploitation.

Multimodal emotion recognition in conversational videos (ERC) develops rapidly in recent years. To fully extract the relative context from video clips, most studies build their models on the entire dialogues which make them lack of real-time ERC ability. Different from related researches, a novel multimodal emotion recognition model for conversational videos based on reinforcement learning and domain knowledge (ERLDK) is proposed in this paper. In ERLDK, the reinforcement learning algorithm is introduced to conduct real-time ERC with the occurrence of conversations. The collection of history utterances is composed as an emotion-pair which represents the multimodal context of the following utterance to be recognized. Dueling deep-Q-network (DDQN) based on gated recurrent unit (GRU) layers is designed to learn the correct action from the alternative emotion categories. Domain knowledge is extracted from public dataset based on the former information of emotion-pairs. The extracted domain knowledge is used to revise the results from the RL module and is transformed into other dataset to examine the rationality. The experimental results on datasets show that ERLDK achieves the state-of-the-art results on weighted average and most of the specific emotion categories.

Grant-free non-orthogonal multiple access (NOMA) has recently received wide attention for reducing signaling overhead and transmission latency in massive machine-type communications (mMTC). In grant-free NOMA systems, user activity and data (UAD) has to be detected, which is challenging in practice. As an emerging technique, compressive sensing (CS) shows great promise in solving this problem by exploiting the inherent sparsity nature of user activity. This paper proposes to use the weighted l2,1 minimization (WL21M) to jointly detect UAD in realistic dynamic scenarios. At first, the average recoverability of the WL21M is analyzed. This analysis reveals the fact that the WL21M can improve the detection performance by means of an appropriate weighting and the incorporation of intrinsic temporal correlation. Motivated by the analysis, a collaborative hierarchical match pursuit (C-HiMP) algorithm is proposed for dynamic UAD detection. In the C-HiMP, a sequence of WL21M problems are solved in the subspaces spanned by all of the components in the hierarchical estimated support sets, where the weights are collaboratively updated by the solutions in previous time slots so that an attractive self-correction capacity is obtained. Simulation results demonstrate that the proposed C-HiMP can obtain significant performance improvements, in terms of detection accuracy and computational complexity, compared with several state-of-the-art CS-based detection algorithms.