Approximate decoding approaches for network coded correlated data

This paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth variations. We show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples showing how the proposed algorithm can be deployed in sensor networks and distributed imaging applications.

Decoding Delay Minimization in Inter-Session Network Coding

Intra-session network coding has been shown to offer significant gains in terms of achievable throughput and delay in settings where one source multicasts data to several clients. In this paper, we consider a more general scenario where multiple sources transmit data to sets of clients over a wireline overlay network. We propose a novel framework for efficient rate allocation in networks where intermediate network nodes have the opportunity to combine packets from different sources using randomized network coding. We formulate the problem as the minimization of the average decoding delay in the client population and solve it with a gradient-based stochastic algorithm. Our optimized inter-session network coding solution is evaluated in different network topologies and is compared with basic intra-session network coding solutions. Our results show the benefits of proper coding decisions and effective rate allocation for lowering the decoding delay when the network is used by concurrent multicast sessions.

Nonverbal social communication and gesture control in schizophrenia

Schizophrenia patients are severely impaired in nonverbal communication, including social perception and gesture production. However, the impact of nonverbal social perception on gestural behavior remains unknown, as is the contribution of negative symptoms, working memory, and abnormal motor behavior. Thus, the study tested whether poor nonverbal social perception was related to impaired gesture performance, gestural knowledge, or motor abnormalities. Forty-six patients with schizophrenia (80%), schizophreniform (15%), or schizoaffective disorder (5%) and 44 healthy controls matched for age, gender, and education were included. Participants completed 4 tasks on nonverbal communication including nonverbal social perception, gesture performance, gesture recognition, and tool use. In addition, they underwent comprehensive clinical and motor assessments. Patients presented impaired nonverbal communication in all tasks compared with controls. Furthermore, in contrast to controls, performance in patients was highly correlated between tasks, not explained by supramodal cognitive deficits such as working memory. Schizophrenia patients with impaired gesture performance also demonstrated poor nonverbal social perception, gestural knowledge, and tool use. Importantly, motor/frontal abnormalities negatively mediated the strong association between nonverbal social perception and gesture performance. The factors negative symptoms and antipsychotic dosage were unrelated to the nonverbal tasks. The study confirmed a generalized nonverbal communication deficit in schizophrenia. Specifically, the findings suggested that nonverbal social perception in schizophrenia has a relevant impact on gestural impairment beyond the negative influence of motor/frontal abnormalities.