9 research outputs found
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
This work proposes a first-of-its-kind SLAM architecture fusing an
event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for
drone navigation. Each sensor is processed by a bio-inspired Spiking Neural
Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning,
as observed in the brain. In contrast to most learning-based SLAM systems%,
which a) require the acquisition of a representative dataset of the environment
in which navigation must be performed and b) require an off-line training
phase, our method does not require any offline training phase, but rather the
SNN continuously learns features from the input data on the fly via STDP. At
the same time, the SNN outputs are used as feature descriptors for loop closure
detection and map correction. We conduct numerous experiments to benchmark our
system against state-of-the-art RGB methods and we demonstrate the robustness
of our DVS-Radar SLAM approach under strong lighting variations
Computational Thinking in Context Across Curriculum:Students’ and Teachers’ Perspectives
NWOAlgorithms and the Foundations of Software technolog
Astrocyte Regulated Neuromorphic Central Pattern Generator Control of Legged Robotic Locomotion
Neuromorphic computing systems, where information is transmitted through
action potentials in a bio-plausible fashion, is gaining increasing interest
due to its promise of low-power event-driven computing. Application of
neuromorphic computing in robotic locomotion research have largely focused on
Central Pattern Generators (CPGs) for bionics robotic control algorithms -
inspired from neural circuits governing the collaboration of the limb muscles
in animal movement. Implementation of artificial CPGs on neuromorphic hardware
platforms can potentially enable adaptive and energy-efficient edge robotics
applications in resource constrained environments. However, underlying rewiring
mechanisms in CPG for gait emergence process is not well understood. This work
addresses the missing gap in literature pertaining to CPG plasticity and
underscores the critical homeostatic functionality of astrocytes - a cellular
component in the brain that is believed to play a major role in multiple brain
functions. This paper introduces an astrocyte regulated Spiking Neural Network
(SNN)-based CPG for learning locomotion gait through Reward-Modulated STDP for
quadruped robots, where the astrocytes help build inhibitory connections among
the artificial motor neurons in different limbs. The SNN-based CPG is simulated
on a multi-object physics simulation platform resulting in the emergence of a
trotting gait while running the robot on flat ground.
computational power savings is observed in comparison to a state-of-the-art
reinforcement learning based robot control algorithm. Such a
neuroscience-algorithm co-design approach can potentially enable a quantum leap
in the functionality of neuromorphic systems incorporating glial cell
functionality
Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals
Neuromodulation techniques have emerged as promising approaches for treating
a wide range of neurological disorders, precisely delivering electrical
stimulation to modulate abnormal neuronal activity. While leveraging the unique
capabilities of artificial intelligence (AI) holds immense potential for
responsive neurostimulation, it appears as an extremely challenging proposition
where real-time (low-latency) processing, low power consumption, and heat
constraints are limiting factors. The use of sophisticated AI-driven models for
personalized neurostimulation depends on back-telemetry of data to external
systems (e.g. cloud-based medical mesosystems and ecosystems). While this can
be a solution, integrating continuous learning within implantable
neuromodulation devices for several applications, such as seizure prediction in
epilepsy, is an open question. We believe neuromorphic architectures hold an
outstanding potential to open new avenues for sophisticated on-chip analysis of
neural signals and AI-driven personalized treatments. With more than three
orders of magnitude reduction in the total data required for data processing
and feature extraction, the high power- and memory-efficiency of neuromorphic
computing to hardware-firmware co-design can be considered as the
solution-in-the-making to resource-constraint implantable neuromodulation
systems. This could lead to a new breed of closed-loop responsive and
personalised feedback, which we describe as Neuromorphic Neuromodulation. This
can empower precise and adaptive modulation strategies by integrating
neuromorphic AI as tightly as possible to the site of the sensors and
stimulators. This paper presents a perspective on the potential of Neuromorphic
Neuromodulation, emphasizing its capacity to revolutionize implantable
brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page
Resilience and Effective Learning in First-Year Undergraduate Computer Science
Many factors have been shown to be important for supporting effective learning and teaching — and thus progression and success — in higher education. While factors such as key introductory-level (CS1) knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. University study can be a period of significant transition for many students; therefore an individual’s positive psychology may have considerable impact upon their response to these challenges. This work investigates the relationships between effective learning and success (first-year performance and attendance) and two measures of positive psychology: Grit and the Nicolson McBride Resilience Quotient (NMRQ).Data was captured by integrating Grit (N=58) and Resilience (N=50) questionnaires and related coaching into the first-year of the undergraduate computer science programme at a single UK university. Analyses demonstrate that NMRQ is significantly linked to attendance and performance for individual subjects and year average marks; however, this was not the case for Grit. This suggests that development of targeted interventions to support students in further developing their resilience could support their learning, as well as progression and retention. Resilience could be used, in concert with other factors such as learning analytics, to augment a range of existing models to predict future student success, allowing targeted academic and pastoral support
Resilience and Effective Learning in First-Year Undergraduate Computer Science
Many factors have been shown to be important for supporting effective learning and teaching — and thus progression and success — in higher education. While factors such as key introductory-level (CS1) knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. University study can be a period of significant transition for many students; therefore an individual’s positive psychology may have considerable impact upon their response to these challenges. This work investigates the relationships between effective learning and success (first-year performance and attendance) and two measures of positive psychology: Grit and the Nicolson McBride Resilience Quotient (NMRQ).Data was captured by integrating Grit (N=58) and Resilience (N=50) questionnaires and related coaching into the first-year of the undergraduate computer science programme at a single UK university. Analyses demonstrate that NMRQ is significantly linked to attendance and performance for individual subjects and year average marks; however, this was not the case for Grit. This suggests that development of targeted interventions to support students in further developing their resilience could support their learning, as well as progression and retention. Resilience could be used, in concert with other factors such as learning analytics, to augment a range of existing models to predict future student success, allowing targeted academic and pastoral support
Testing the Performance of Simple Moving Average With the Extension of Short Selling
Master thesis Business Administration - University of Agder 2016In this thesis, we test the performance of market timing based on simple moving average, which
is one of the most popular trading strategies used by investors and practitioners to date. Previous
studies have found evidence both in favour and against the effectiveness of the strategy, while
a definite conclusion is yet to be commonly recognized. To address this, we reassess a previous
study done on US portfolios with stocks from the NYSE, AMEX and NASDAQ, further
investigate the effectiveness of the strategy in Norwegian portfolios constructed by stocks from
the Oslo Stock Exchange (OSE). This thesis contributes with a new extension, possibly for the
first time, testing the moving average strategy with short selling the underlying portfolio when
triggered a sell signal. We use value-weighted portfolios with monthly returns from both the
US and Norwegian market sorted by size, book to market and momentum. Our results revealed
both lower risk and return in general by the moving average strategy compared with buying and
holding, providing no evidence supporting superior performance of the strategy in neither US
nor Norwegian portfolios. Shorting the underlying portfolio showed similar results, however,
one interesting finding is the behaviour of the short strategy, which tend to amplify the normal
simple moving average strategy’s performance