Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks

The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDAR-video inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.Comment: 7 pages, double column, IEEE format, accepted at IEEE HPEC 201

Wearable EEG-based Activity Recognition in PHM-related Service Environment via Deep Learning

It is of paramount importance to track the cognitive activity or cognitve attenion of the service personnel in a Prognostics and Health Monitoring (PHM) service related training or operation environment. The electroencephalography (EEG) data is one of the good candidates for cognitive activity recognition of the user. Analyzing electroencephalography (EEG) data in an unconstrained (natural) environment for understanding cognitive state and classifying human activity is a challenging task due to multiple reasons such as low signal-to-noise ratio, transient nature, lack of baseline availability and uncontrolled mixing of various tasks. This paper proposes a framework based on an emerging tool named deep learning that monitors human activity by fusing multiple EEG sensors and also selects a smaller sensor suite for a lean data collection system. Real-time classification of human activity from spatially non collocated multi-probe EEG is executed by applying deep learning techniques without performing any significant amount of data preprocessing and manual feature engineering. Two types of deep neural networks, deep belief network (DBN) and deep convolutional neural network (DCNN) are used at the core of the proposed framework, which automatically learns necessary features from EEG for a given classification task. Validation on extensive amount of data, which was collected from several subjects while they were performing multiple tasks (listening and watching) in PHM service training session, is presented and significant parallels are drawn from existing domain knowledge on EEG data understanding. Comparison with machine learning benchmark techniques shows that deep learning based tools are better at understanding EEG data for task classification. It is observed via sensor selection that a significantly smaller EEG sensor suite can perform at a comparable accuracy as the original sensor suite

Spatial distribution of vertical carbon fluxes on the Agulhas Bank and its possible implication for the benthic nepheloid layer

Vertical particle fluxes of particulate organic carbon (POC), chlorophyll a (Chl a) and biogenic silica (bSi) were measured on the productive shelf of southern Africa, the Agulhas Bank (AB), in March 2019. Sinking particulate material in the form of aggregates is hypothesized to form the benthic nepheloid layer (BNL) which is a turbid layer found near the seabed. This layer is known to affect the spawning success of squid as it is linked to high turbidity which reduces visibility during mating. To determine the distribution of fluxes and particle composition in the AB, we collected water samples below the surface mixed layer (‘export’) and near the seabed (‘bottom’) using a Marine Snow Catcher. POC export fluxes were significantly higher inshore than offshore (mean ± SD: 944.6 ± 302.0 & 461.1 ± 162.1 mg POC m−2 d−1, respectively). There was no significant difference in the cross-shelf distribution of Chl a and bSi export fluxes, however the inshore fluxes of Chl a and bSi were higher than offshore, suggesting a link between export fluxes and sinking organic matter derived from the more productive inshore surface waters. All bottom fluxes were significantly higher inshore, suggesting the contribution of sinking organic particles and resuspended bottom sediments to inshore fluxes. POC export efficiency (ratio of exported POC flux relative to net primary production (NPP)) was higher on the AB (range: 0.58–9.56) compared to the global shelf seas ratio of 0.18 and not related to NPP, suggesting an export of standing stock of carbon biomass, likely produced before the cruise. Transfer efficiency (i.e., the amount of exported flux that reaches the bottom) was also high (max: 0.99, 1.0 and 33.04 for POC, Chl a and bSi, respectively) but did not show a clear spatial pattern. We observed a significant positive correlation between bottom turbidity (a proxy for BNL presence) and export POC flux, suggesting the possibility that sinking organic matter is contributing to BNL formation on the AB

Polymerase II Promoter Strength Determines Efficacy of microRNA Adapted shRNAs

Since the discovery of RNAi and microRNAs more than 10 years ago, much research has focused on the development of systems that usurp microRNA pathways to downregulate gene expression in mammalian cells. One of these systems makes use of endogenous microRNA pri-cursors that are expressed from polymerase II promoters where the mature microRNA sequence is replaced by gene specific duplexes that guide RNAi (shRNA-miRs). Although shRNA-miRs are effective in directing target mRNA knockdown and hence reducing protein expression in many cell types, variability of RNAi efficacy in cell lines has been an issue. Here we show that the choice of the polymerase II promoter used to drive shRNA expression is of critical importance to allow effective mRNA target knockdown. We tested the abundance of shRNA-miRs expressed from five different polymerase II promoters in 6 human cell lines and measured their ability to drive target knockdown. We observed a clear positive correlation between promoter strength, siRNA expression levels, and protein target knockdown. Differences in RNAi from the shRNA-miRs expressed from the various promoters were particularly pronounced in immune cells. Our findings have direct implications for the design of shRNA-directed RNAi experiments and the preferred RNAi system to use for each cell type