DVB-NGH: the Next Generation of Digital Broadcast Services to Handheld Devices

This paper reviews the main technical solutions adopted by the next-generation mobile broadcasting standard DVB-NGH, the handheld evolution of the second-generation digital terrestrial TV standard DVB-T2. The main new technical elements introduced with respect to DVB-T2 are: layered video coding with multiple physical layer pipes, time-frequency slicing, full support of an IP transport layer with a dedicated protocol stack, header compression mechanisms for both IP and MPEG-2 TS packets, new low-density parity check coding rates for the data path (down to 1/5), nonuniform constellations for 64 Quadrature Amplitude Modulation (QAM) and 256QAM, 4-D rotated constellations for Quadrature Phase Shift Keying (QPSK), improved time interleaving in terms of zapping time, end-to-end latency and memory consumption, improved physical layer signaling in terms of robustness, capacity and overhead, a novel distributed multiple input single output transmit diversity scheme for single-frequency networks (SFNs), and efficient provisioning of local content in SFNs. All these technological solutions, together with the high performance of DVB-T2, make DVB-NGH a real next-generation mobile multimedia broadcasting technology. In fact, DVB-NGH can be regarded the first third-generation broadcasting system because it allows for the possibility of using multiple input multiple output antenna schemes to overcome the Shannon limit of single antenna wireless communications. Furthermore, DVB-NGH also allows the deployment of an optional satellite component forming a hybrid terrestrial-satellite network topology to improve the coverage in rural areas where the installation of terrestrial networks could be uneconomical.Gómez Barquero, D.; Douillard, C.; Moss, P.; Mignone, V. (2014). DVB-NGH: the Next Generation of Digital Broadcast Services to Handheld Devices. IEEE Transactions on Broadcasting. 60(2):246-257. doi:10.1109/TBC.2014.2313073S24625760

Network Convergence in Multicarrier Hybrid Cellular Network

In a multicarrier communication system with known channel state information at transmitter (CSIT), it is well-known that the water-filling power allocation scheme is optimal in achieving the Shannon capacity. However, in a multicarrier broadcast network (e.g. over-the-air TV network) without CSIT, the optimal power allocation among subcarriers is still unknown, largely due to the heterogeneity of the channel conditions associated with different receivers. In the first part of the thesis, the performance of a generic multicarrier broadcast network is thoroughly studied by exploiting the frequency diversity over subcarriers. In particular, the performance metric is first defined based on the relationship among broadcast transmission rate, coverage area and outage probability. In order to maximize the network performance, closed form expressions of the instantaneous mutual information (IMI) and the optimal power allocation schemes are derived for both low SNR and high SNR cases; upper and lower bounds are also provided to estimate broadcast coverage area in general SNR regime. Also we extend our discussion to the broadcast network with multiple collaborative transmitters. Extensive simulation results are provided to validate our analysis. In the second part of the thesis, we discuss the optimal performance of a generic broadcast cellular hybrid network. It is well known that the Dirty Paper Coding (DPC) achieves the channel capacity for multiuser degraded channels. However, the optimality of DPC remains unknown for non-degraded channel. Specifically, we derive the optimal interference pre-cancellation order for a DPC based broadcast and unicast hybrid network. Different DPC cancellation schemes are studied to maximize the hybrid capacity region. The conditions for each scheme being optimal are analytically derived. Both ergodic and outage capacity are considered as our performance metric. Our results show that the optimal interference pre-cancellation order varies with SNR and broadcast and unicast channel conditions. Moreover, in low SNR condition, the optimal power allocation scheme is derived to reach the maximal sum rate

Revealing the (predictive) code of top-down signals in the brain

Reciprocal connections are common in the brain, yet little is known about their functional role. Top-down connections, in particular, remain functionally obscure in both neuroscience and in the nascent field of deep learning. On the theoretical side, predictive coding has been put forward as a framework that assigns specific roles to top-down and bottom-up connections in sensory information processing. It remains unclear, however, if and how the brain implements this predictive code. This work examined top-down signals in the auditory cortex and in the corticostriatal system in macaques in order to validate the claims put forward by predictive coding. This theory suggests there are imbalances in message passing up and down the cortical hierarchy; these imbalances imply cross-frequency couplings should predominate top-down. It is unknown whether these asymmetries are expressed in cross-frequency interactions in the brain. This work examined cross-frequency interactions across four sectors of the macaque auditory cortex. Predictive coding also applies in decision making, where it allows for action selection based on predicted reward (or value). This is more commonly known as reinforcement learning (RL) and is supported by the fronto-striatal systems in the brain. The computational mechanisms that drive learning in this system are unknown, however. This work drew on a recurrent neural network (RNN) model of the dlPFC-dSTR circuit in the brain together with recordings from macaques from the same regions to answer this question. Altogether, the findings are largely consistent with the predictive coding framework.Open Acces

Grounding semantic cognition using computational modelling and network analysis

The overarching objective of this thesis is to further the field of grounded semantics using a range of computational and empirical studies. Over the past thirty years, there have been many algorithmic advances in the modelling of semantic cognition. A commonality across these cognitive models is a reliance on hand-engineering “toy-models”. Despite incorporating newer techniques (e.g. Long short-term memory), the model inputs remain unchanged. We argue that the inputs to these traditional semantic models have little resemblance with real human experiences. In this dissertation, we ground our neural network models by training them with real-world visual scenes using naturalistic photographs. Our approach is an alternative to both hand-coded features and embodied raw sensorimotor signals. We conceptually replicate the mutually reinforcing nature of hybrid (feature-based and grounded) representations using silhouettes of concrete concepts as model inputs. We next gradually develop a novel grounded cognitive semantic representation which we call scene2vec, starting with object co-occurrences and then adding emotions and language-based tags. Limitations of our scene-based representation are identified for more abstract concepts (e.g. freedom). We further present a large-scale human semantics study, which reveals small-world semantic network topologies are context-dependent and that scenes are the most dominant cognitive dimension. This finding leads us to conclude that there is no meaning without context. Lastly, scene2vec shows promising human-like context-sensitive stereotypes (e.g. gender role bias), and we explore how such stereotypes are reduced by targeted debiasing. In conclusion, this thesis provides support for a novel computational viewpoint on investigating meaning - scene-based grounded semantics. Future research scaling scene-based semantic models to human-levels through virtual grounding has the potential to unearth new insights into the human mind and concurrently lead to advancements in artificial general intelligence by enabling robots, embodied or otherwise, to acquire and represent meaning directly from the environment

Neural representation of complex motion in the primate cortex

This dissertation is concerned with how information about the environment is represented by neural activity in the primate brain. More specifically, it contains several studies that explore the representation of visual motion in the brains of humans and nonhuman primates through behavioral and physiological measures. The majority of this work is focused on the activity of individual neurons in the medial superior temporal area (MST) – a high-level, extrastriate area of the primate visual cortex. The first two studies provide an extensive review of the scientific literature on area MST. The area’s prominent role at the intersection of low-level, bottom-up, sensory processing and high-level, top-down mechanisms is highlighted. Furthermore, a specific article on how information about self-motion and object motion can be decoded from a population of MSTd neurons is reviewed in more detail. The third study describes a published and annotated dataset of MST neurons’ responses to a series of different motion stimuli. This dataset is analyzed using a variety of different analysis approaches in the fifth study. Classical tuning curve approaches confirm that MST neurons have large, but well-defined spatial receptive fields and are independently tuned for linear and spiral motion, as well as speed. We also confirm that the tuning for spiral motion is position invariant in a majority of MST neurons. A bias-free characterization of receptive field profiles based on a new stimulus that generates smooth, complex motion patterns turned out to be predictive of some of the tuning properties of MST neurons, but was generally less informative than similar approaches have been in earlier visual areas. The fifth study introduces a new motion stimulus that consists of hexgonal segments and presents an optimization algorithm for an adaptive online analysis of neurophysiological recordings. Preliminary physiological data and simulations show these tools to have a strong potential in characterizing the response functions of MST neurons. The final study describes a behavioral experiment with human subjects that explores how different stimulus features, such as size and contrast, affect motion perception and discusses what conclusions can be drawn from that about the representation of visual motion in the human brain. Together these studies highlight the visual motion processing pathway of the primate brain as an excellent model system for studying more complex relations of neural activity and external stimuli. Area MST in particular emerges as a gateway between perception, cognition, and action planning.2021-11-1

The role of medial entorhinal cortex activity in hippocampal CA1 spatiotemporally correlated sequence generation and object selectivity for memory function

The hippocampus is crucial for episodic memory and certain forms of spatial navigation. Firing activity of hippocampal principal neurons contains environmental information, including the presence of specific objects, as well as the animal’s spatial and temporal position relative to environmental and behavioral cues. The organization of these firing correlates may allow the formation of memory traces through the integration of object and event information onto a spatiotemporal framework of cell assemblies. Characterizing how external inputs guide internal dynamics in the hippocampus to enable this process across different experiences is crucial to understanding hippocampal function. A body of literature implicates the medial entorhinal cortex (MEC) in supplying spatial and temporal information to the hippocampus. Here we develop a protocol utilizing bilaterally implanted custom designed triple fiber optic arrays and the red-shifted inhibitory opsin JAWS to transiently inactivate large volumes of MEC in freely behaving rats. This was coupled with extracellular tetrode recording of ensembles in CA1 of the hippocampus during a novel memory task involving temporal, spatial and object related epochs, in order to assess the importance of MEC activity for hippocampal feature selectivity during a rich and familiar experience. We report that inactivation of MEC during a mnemonic temporal delay disrupts the existing temporal firing field sequence in CA1 both during and following the inactivation period. Neurons with firing fields prior to the inactivation on each trial remained relatively stable. The disruption of CA1 temporal firing field sequences was accompanied by a behavioral deficit implicating MEC activity and hippocampal temporal field sequences in effective memory across time. Inactivating MEC during the object or spatial epochs of the task did not significantly alter CA1 object selective or spatial firing fields and behavioral performance remained stable. Our findings suggest that MEC is crucial specifically for temporal field organization and expression during a familiar and rich experience. These results support a role for MEC in guiding hippocampal cell assembly sequences in the absence of salient changing stimuli, which may extend to the navigation of cognitive organization in humans and support memory formation and retrieval

The Telecommunications and Data Acquisition Report

Reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition are presented. Emphasis is placed on activities of the Deep Space Network and its associated ground facilities