fundamental building block
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2022 ◽  
Author(s):  
Simon Geirnaert ◽  
Tom Francart ◽  
Alexander Bertrand

The goal of auditory attention decoding (AAD) is to determine to which speaker out of multiple competing speakers a listener is attending based on the brain signals recorded via, e.g., electroencephalography (EEG). AAD algorithms are a fundamental building block of so-called neuro-steered hearing devices that would allow identifying the speaker that should be amplified based on the brain activity. A common approach is to train a subject-specific decoder that reconstructs the amplitude envelope of the attended speech signal. However, training this decoder requires a dedicated 'ground-truth' EEG recording of the subject under test, during which the attended speaker is known. Furthermore, this decoder remains fixed during operation and can thus not adapt to changing conditions and situations. Therefore, we propose an online time-adaptive unsupervised stimulus reconstruction method that continuously and automatically adapts over time when new EEG and audio data are streaming in. The adaptive decoder does not require ground-truth attention labels obtained from a training session with the end-user, and instead can be initialized with a generic subject-independent decoder or even completely random values. We propose two different implementations: a sliding window and recursive implementation, which we extensively validate based on multiple performance metrics on three independent datasets. We show that the proposed time-adaptive unsupervised decoder outperforms a time-invariant supervised decoder, representing an important step towards practically applicable AAD algorithms for neuro-steered hearing devices.


Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 348
Author(s):  
Francisco de Melo ◽  
Horácio C. Neto ◽  
Hugo Plácido da Silva

Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily “fooled” by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today’s demanding security systems—mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC’s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.


ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 155
Author(s):  
Sebastiano Chiodini ◽  
Marco Pertile ◽  
Stefano Debei

Obstacle mapping is a fundamental building block of the autonomous navigation pipeline of many robotic platforms such as planetary rovers. Nowadays, occupancy grid mapping is a widely used tool for obstacle perception. It foreseen the representation of the environment in evenly spaced cells, whose posterior probability of being occupied is updated based on range sensors measurement. In more classic approaches, the cells are updated to occupied at the point where the ray emitted by the range sensor encounters an obstacle, such as a wall. The main limitation of this kind of methods is that they are not able to identify planar obstacles, such as slippery, sandy, or rocky soils. In this work, we use the measurements of a stereo camera combined with a pixel labeling technique based on Convolution Neural Networks to identify the presence of rocky obstacles in planetary environment. Once identified, the obstacles are converted into a scan-like model. The estimation of the relative pose between successive frames is carried out using ORB-SLAM algorithm. The final step consists of updating the occupancy grid map using the Bayes’ update Rule. To evaluate the metrological performances of the proposed method images from the Martian analogous dataset, the ESA Katwijk Beach Planetary Rover Dataset have been used. The evaluation has been performed by comparing the generated occupancy map with a manually segmented ortomosaic map, obtained by drones’ survey of the area used as reference.


2021 ◽  
Author(s):  
Christian Borgs ◽  
Jennifer T. Chayes ◽  
Devavrat Shah ◽  
Christina Lee Yu

Matrix estimation or completion has served as a canonical mathematical model for recommendation systems. More recently, it has emerged as a fundamental building block for data analysis as a first step to denoise the observations and predict missing values. Since the dawn of e-commerce, similarity-based collaborative filtering has been used as a heuristic for matrix etimation. At its core, it encodes typical human behavior: you ask your friends to recommend what you may like or dislike. Algorithmically, friends are similar “rows” or “columns” of the underlying matrix. The traditional heuristic for computing similarities between rows has costly requirements on the density of observed entries. In “Iterative Collaborative Filtering for Sparse Matrix Estimation” by Christian Borgs, Jennifer T. Chayes, Devavrat Shah, and Christina Lee Yu, the authors introduce an algorithm that computes similarities in sparse datasets by comparing expanded local neighborhoods in the associated data graph: in effect, you ask friends of your friends to recommend what you may like or dislike. This work provides bounds on the max entry-wise error of their estimate for low rank and approximately low rank matrices, which is stronger than the aggregate mean squared error bounds found in classical works. The algorithm is also interpretable, scalable, and amenable to distributed implementation.


2021 ◽  
Vol 6 (24) ◽  
pp. 125-130
Author(s):  
Kai Ren Khew ◽  
Shamsulhadi Bandi ◽  
Norhazren Izatie Mohd

Common to the construction industry, distinct parties of different backgrounds and disciplines were assembled to complete a project. Despite being unfamiliar and sharing little in common except the project’s goal, this ad-hoc syndicate was expected to effectively communicate the expectation of the project’s stakeholders. While it was possible for project parties to get along and gradually form an effective relationship, contrasting interests and values were the norms, which resulted in conflicting attitudes and behaviour. Among the most critical constituents of a successful relationship, trust was identified as the fundamental building block to allay conflict or cushioning its impacts it. Through advancements in project management thinking in recent times were able to offer pragmatic solutions to relationship issues in construction, it appeared that digital transformation was not widely inculcated in the approaches. Against this backdrop, this paper aimed to gain insightful disclosure on digital development that promotes and accelerates trust-building in different contexts. This is methodically carried out by identifying and reviewing 100 pieces of relevant literature from the Scopus database, followed by deductive content analysis to observe the interplay between digital development and trust in a wider context. The analyses revealed two types of digital approaches having bright prospects for infusion in construction which are Gamification and Virtual Reality (VR). The outcome from this analysis provides the much-needed departing point for humanizing digital development in construction and signifies a paradigm shift in dealing with trust issues in construction.


2021 ◽  
Author(s):  
Chunsheng Chen ◽  
Yongli He ◽  
Huiwu Mao ◽  
Li Zhu ◽  
Xiangjing Wang ◽  
...  

Abstract The biological visual system encodes information into spikes and processes them parallelly by the neural network, which enables the perception with high throughput of visual information processing at an energy budget of a few watts. The parallelism and efficiency of bio-visual system motivates electronic implementation of this biological computing paradigm, which is challenged by the lack of bionic devices, such as spiking neurons that can mimic its biological counterpart. Here, we present a highly bio-realistic spiking visual neuron based on an Ag/TaOX/ITO memristor. Such spiking visual neuron collects visual information by a photodetector, encodes them into action potentials through the memristive spiking encoder, and interprets them for recognition tasks based on a network of neuromorphic transistors. The firing spikes generated by the memristive spiking encoders have a frequency range of 1-200 Hz and sub-micro watts power consumption, very close to the biological counterparts. Furthermore, a spiking visual system is demonstrated, replicating the distance-dependent response and eye fatigue of biological visual systems. The mimicked depth perception shows a recognition improvement by adapting to sights at different distance. Our design presents a fundamental building block for energy-efficient and biologically plausible artificial visual systems.


Author(s):  
Yue-Liang Wu

Starting from the motional property of functional field based on the action principle of path integral formulation while proposing maximum coherence motion principle and maximum locally entangled-qubits motion principle as guiding principles, we show that such a functional field as fundamental building block appears naturally as an entangled qubit-spinor field expressed by a locally entangled state of qubits. Its motion brings about the appearance of Minkowski space–time with dimension determined by the motion-correlation [Formula: see text]-spin charge and the emergence of [Formula: see text]-spin/hyperspin symmetry as fundamental symmetry. Intrinsic [Formula: see text]-spin charge displays a periodic feature as the mod 4 qubit number, which enables us to classify all entangled qubit-spinor fields and space–time dimensions into four categories with respect to four [Formula: see text]-spin charges [Formula: see text]. An entangled decaqubit-spinor field in 19-dimensional hyper-space–time is found to be a hyperunified qubit-spinor field which unifies all discovered leptons and quarks and brings on the existence of mirror lepton–quark states. The inhomogeneous hyperspin symmetry [Formula: see text] as hyperunified symmetry in association with inhomogeneous Lorentz-type symmetry [Formula: see text] and global scaling symmetry provides a unified fundamental symmetry. The maximum locally entangled-qubits motion principle is shown to lay the foundation of hyperunified field theory, which enables us to comprehend long-standing questions raised in particle physics and quantum field theory.


2021 ◽  
Author(s):  
Kalaiyarasi.D ◽  
Pritha.N ◽  
Srividhya.G ◽  
Padmapriya.D

The multiplier is a fundamental building block in most digital ICs’ arithmetic units. The multiplier architecture in modern VLSI circuits must meet the main parameters of low power, high speed, and small area requirements. In this paper, a 4-bit multiplier is constructed using the Dadda algorithm with enhanced Full and Half adder blocks to achieve a smaller size, lower power consumption, and minimum propagation delay. The Dadda Algorithm-designed multiplier is used in the first phase to reduce propagation delay while adding partial products in three stages provided by AND Gates. In the second phase, each stage of the Dadda tree algorithm is built with an enhanced Full and half adders to reduce the design area, propagation delay, and power consumption while still meeting the requirements of the current scenario by using MUX logic. In an average of Conventional array Multipliers, the proposed Dadda multiplier achieved an 84.68% reduction in delay, 70.89% reduction in power, 84.68% increase in Maximum Usable Frequency (MUF), and 95.55% reduction in Energy per Samples (EPS).


Author(s):  
Johannes Blum ◽  
Stefan Funke ◽  
Sabine Storandt

AbstractShortest path planning is a fundamental building block in many applications. Hence developing efficient methods for computing shortest paths in, e.g., road or grid networks is an important challenge. The most successful techniques for fast query answering rely on preprocessing. However, for many of these techniques it is not fully understood why they perform so remarkably well, and theoretical justification for the empirical results is missing. An attempt to explain the excellent practical performance of preprocessing based techniques on road networks (as transit nodes, hub labels, or contraction hierarchies) in a sound theoretical way are parametrized analyses, e.g., considering the highway dimension or skeleton dimension of a graph. Still, these parameters may be large in case the network contains grid-like substructures—which inarguably is the case for real-world road networks around the globe. In this paper, we use the very intuitive notion of bounded growth graphs to describe road networks and also grid graphs. We show that this model suffices to prove sublinear search spaces for the three above mentioned state-of-the-art shortest path planning techniques. Furthermore, our preprocessing methods are close to the ones used in practice and only require expected polynomial time.


2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110154
Author(s):  
Zane Griffin Talley Cooper

How did the 3.5-inch Winchester hard disk drive become the fundamental building block of the modern data center? In attempting to answer this question, I theorize the concept of "data peripheries" to attend to the awkward, uneven, and unintended outsides of data infrastructures. I explore the concept of data peripheries by first situating Big Data in one of its many unintended outsides—an unassuming dog kennel in Indiana housed in a former permanent magnet manufacturing plant. From the perspective of this dog kennel, I then build a history of the 3.5-inch Winchester hard disk drive, and weave this hard drive history through the industrial histories of rare earth mining and permanent magnet manufacturing, focusing principally on Magnequench, a former General Motors subsidiary, and its sale and movement of operations from Indiana to China in the mid-1990s and early 2000s. I then discuss how mobilities of rare earths, both as materials and political discourse, shape Big Data futures, and conclude by speculating on how using the situated lenses of data peripheries (such as this Indiana dog kennel) can open up new methods for studying the material entanglements of Big Data writ large.


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