Asynchronous Multi View SLAM

Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice . In this work, we propose ageneralized SLAM formulation which accounts for asynchronoussensor observations . Our framework integrates a continuous-time motion model torelate information across asynchronous multi-frames during tracking, localmapping, and loop closing .…

Deep Learning Aided 5G Channel Estimation

Deep learning has demonstrated the important roles in improving the systemperformance and reducing computational complexity for $5$G-and-beyond networks . We propose a new channel estimation method with the assistance of deep learning in order to support the least squares estimation, which is alow-cost method but having relatively high channel estimation errors .…

Multi objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks

Many existing deep learning models are vulnerable to adversarial examplesthat are imperceptible to humans . We propose to search for deep neural architectures thatare robust to five types of well-known adversarial attacks using amulti-objective evolutionary algorithm . To reduce the computational cost, anormalized error rate of a randomly chosen attack is calculated as the robustness for each newly generated neural architecture at each generation .…

Frequency Dynamics Constrained Sequential Service Restoration in Inverter Dominated Microgrids

This paper proposes a service restoration model for unbalanced distributionsystems and inverter-dominated microgrids . After extreme events, the damaged distribution systems can be sectionalized into several isolated MGs to restore critical loads and tripped distributed generations by black-start DGs . The high penetration of inverter based DGs reduce the system inertia, which results in low-inertia issues and large frequency fluctuation during therestoration .…

Predictive Processing in Cognitive Robotics a Review

Predictive processing has become an influential framework in cognitivesciences . This framework turns the traditional view of perception upside down,claiming that the main flow of information processing is realized in a top-downhierarchical manner . In the field of cognitive robotics there is no clear-cut distinction on whichschemes have been implemented and under which assumptions .…

Diverse Complexity Measures for Dataset Curation in Self driving

Modern self-driving autonomy systems heavily rely on deep learning . As aconsequence, their performance is influenced significantly by the quality and richness of the training data . It is of key importance to have a mechanism to identify “whatto label” Active learning approaches identify examples to label, but theirinterestingness is tied to a fixed model performing a particular task .…

On the detection and identification of edge disconnections in a multi agent consensus network

In this paper we investigate the problem of the sudden disconnection of anedge in a discrete-time multi-agent consensus network . If the graph remainsstrongly connected, the system still achieves consensus, but ingeneral, unless the information exchange between each pair of agents issymmetric, the agents’ states converge to a drifted value of the original consensus value .…

Observer Design for Systems of Conservation Laws with Lipschitz Nonlinear Boundary Dynamics

The problem of state estimation for a system of coupled hyperbolic PDEs andODEs with Lipschitz nonlinearities with boundary measurements is considered . Aninfinite dimensional observer with a linear boundary injection term is used to solve the state estimation problem . The observer is designed to achieve global exponentialstability of estimation error with respect to a suitable norm .…

Trilevel Neural Architecture Search for Efficient Single Image Super Resolution

This paper proposes a trilevel neural architecture search (NAS) method forefficient single image super-resolution (SR) Unlike current NAS methods, we exploit a sortedsparsestmax activation to let the three-level neural structures contribute sparsely . Our NAS algorithm provides SR models that are significantly lighter in terms of the number of parameters and FLOPS withPSNR value comparable to the current state-of-the-art .…

Generalized Image Reconstruction over T Algebra

Principal Component Analysis (PCA) is well known for its capability ofdimension reduction and data compression . The vectorization of images makes some correlation constraints of neighboring pixels and spatial information lost . We used small neighborhoods of each pixel to form compounded pixels and use a tensorial version of PCA, called TPCA, to compress and reconstruct acompounded image of compounded pixels .…

Real Time LSM Trees for HTAP Workloads

A Log-Structured Merge (LSM) Tree is a natural fit for a lifecycle-aware storage engine due to its high write throughput and level-oriented structure . LASER is almost 5x faster than Postgres (a purerow-store) and two orders of magnitude faster than MonetDB for real-time data analytics workloads .…

Brightening the Optical Flow through Posit Arithmetic

The positdata format is proposed as a drop-in replacement for IEEE 754 float format . The average error in LuKa withSoftPosit is an order of magnitude lower than with SoftFloat . We then present the integration of the hardware implementation of a posit adder and multiplier in a RISC-V open-source platform .…

Artificial Intelligence for Emotion Semantic Trending and People Emotion Detection During COVID 19 Social Isolation

Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined . Early detection of emotional feelings and their trends helpimplement timely intervention strategies . Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics .…

The Complexity of Bicriteria Tree Depth

This work considers the following extension of the tree-depth problem: for agiven input graph $G$ and integers $k$ and $b$ find a rooted forest $F$ ofheight at most $k and $width$ of height $K$ The problem is NP-hard and obtaining a polynomial-time additive$2b$-approximation algorithm .…

KCP Kernel Cluster Pruning for Dense Labeling Neural Networks

Pruning has become a promising technique used to compress and accelerateneural networks . The prevailing filter channel pruning method removes the entire filterchannel . In this study, we proposed kernel cluster pruning (KCP) to prune denselabeling networks . We developed a clustering technique to identify the leastrepresentational kernels in each layer .…

Simultaneous Embedding of Colored Graphs

A set of colored graphs are compatible, if for every color $i$ the number ofvertices of the same color is the same in every graph . A simultaneous embedding of $k$ compatibly colored graphs, each with $n$ vertices, can always be computed with a sublinear number of bends per edge .…

NNStreamer Efficient and Agile Development of On Device AI Systems

A new trend with the wide-spread of deep neuralnetwork applications is on-device AI . It is to process neural networks on mobile devices or edge/IoT devices instead of cloud servers . We propose NNStreamer, a software system that handles neural networks asfilters of stream pipelines, applying the stream processing paradigm to deepneural network applications .…

Attentional Multi layer Feature Fusion Convolution Network for Audio visual Speech Enhancement

Audio-visual speech enhancement system is regarded to be one of promisingsolutions for isolating and enhancing speech of desired speaker . Conventional methods focus on predicting clean speech spectrum via a naive convolutionneural network based encoder-decoder architecture . Paper proposes attentionalaudio-visual multi-layer feature fusion model, in which soft thresholdattention unit are applied on feature mapping at every layer of decoder .…

Generating Simulation based Contacts matrices for Disease Transmission Modelling at Special Settings

A significant amount of disease transmission occurs through human-to-human or social contacts . Understanding who interacts with whom intime and space is essential for disease transmission modelling, prediction, and assessment of prevention strategies in different environments and special settings . This paper presents a methodology for generating contacts matrices by using high fidelity simulations which mimic actual workflow and movements of individuals in time and space .…

Slider On the Design and Modeling of a 2D Floating Satellite Platform

A floating robotic emulation platform for a virtual demonstration of satellite motion in space is presented . The robotic platform is characterized by its friction-less, levitating, yet planar motion over a hyper-smooth surface . The entire design, including 3D printingCAD model and different testbed elements, is provided in an open-sourcerepository and a test campaign is used to showcase its capabilities and illustrate its operations .…