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OOC ADC: A Novel Three-Step Technique for Analog-to-Digital Conversion

Guo-Liu Zhang

March 22, 2024
This paper explore the Off-on Cycle ADC (OOC ADC), an innovative
analog-to-digital conversion (ADC) technique that significantly simplifies the
analog-to-digital conversion process by accomplishing it through just three
steps: inputting the analog signal, triggering the probe’s dip-and-reset motion
(PDR), and outputting the binary digital signal. Initially, the paper
establishes a theoretical foundation for subsequent ADC design by introducing
the Off-on Cycle principle (OOC principle) in binary encoding rules.
Subsequently, it illustrates the design concepts and operational principles of
the OOC ADC through the illustrative example of a 4-bit precision OOC ADC
design. Finally, the paper compares the superior performance of the OOC ADC over
existing ADCs in terms of conversion rate, resolution, process complexity, and
power consumption. The research findings in this paper have the potential to
drastically reduce the technical difficulties involved in analog-to-digital
conversion, facilitate technological advancements in the realm of digital signal
processing.
Smooth Scenario-Based Model Predictive Control for Autonomous Collision
Avoidance in...

Dhanika Mahipala

and 1 more

March 25, 2024
The Scenario-Based Model Predictive Control (SB-MPC) is an autonomous collision
avoidance algorithm primarily designed for open and coastal waters. One of the
challenges in adapting SB-MPC for autonomous inland waterway collision avoidance
is the inability to use a derivative based optimization strategy due to
non-smooth components in its cost function. Hence, we propose a novel algorithm,
Smooth Scenario-Based Model Predictive Control (Smooth-SBMPC) specifically
designed for highly constrained and complex navigational environments inherent
to inland waterways. The effectiveness of Smooth-SBMPC is validated through a
comprehensive simulation study, providing insights into its performance in
complex navigational environments.
Geometrical Pruning of the First Order Regular Perturbation Kernels of the
Manakov Eq...

Astrid Barreiro

and 2 more

March 25, 2024
We propose an approach for constraining the set of nonlinear coefficients of the
conventional first-order regular perturbation (FRP) model of the Manakov
Equation. We identify the largest contributions in the FRP model and provide
geometrical insights into the distribution of their magnitudes in a
three-dimensional space. As a result, a multi-plane hyperbolic constraint is
introduced. A closed-form upper bound on the constrained set of nonlinear
coefficients is given. We also report on the performance characterization of the
FRP with multiplane hyperbolic constraint and show that it reduces the overall
complexity with minimal penalties in accuracy. For a 120 km standard single-mode
fiber transmission, at 60 Gbaud with DP-16QAM, a complexity reduction of 93% is
achieved with a performance penalty below 0.1 dB.
Dynamic Selection of Physical Channels for Adaptive Improvement of Link Quality
Using...

Ryan S. Westafer

and 3 more

March 25, 2024
This paper describes integration of a software defined antenna (SDA) and a
software defined radio (SDR) to enable dynamic and automatic selection of
physical propagation channels. The SDA, an agile aperture antenna (A3), provides
monolithic microdiversity, i.e. multiple antenna states within a single
structure and within a space having maximum dimension of approximately one
wavelength. In this way physical channel selection occurs outside, and therefore
augmenting, the front-end electronics. The independent controllable parameters
for channel access include: frequency, polarization (full Poincaré sphere), and
pattern (in two dimensions). Several different tests were conducted to maximize
a quality figure of merit calculated by the radio. Both library-based and
evolutionary search techniques were used, resulting in short term channel
improvements on the order of 10 dB, and long term fully optimized improvements
on the order of 20 dB.
Dataset for OPEC Crude Oil Trade Network

Saumya Vilas Roy

and 1 more

March 25, 2024
Quantification and analysis of global oil trade networks reveals deep insights
into a nation's development and influence at a global scale. Further, it allows
us to predict future trends and changes to adapt state policy as the crude oil
market influences the balance of power among the developed and emerging
economies alike as it is central for energy needs as well for industrial
progress. This document is a dataset descriptor for the dataset of crude oil
exports from OPEC nations to importing nations/regions from a period of
(2016-2022) structured for easy formation of nodes and edges sourced from
various sources referenced below also it contains the average closing price per
barrel and the global demand of crude oil during a fiscal year to note and
understand complex relations between the global oil trade.The data-set is
available at https://dx.doi.org/10.21227/m8ds-nd06.The authors can be contacted
for the access to dataset as well.
Passive Actuator-Less Gripper for Pick-and-Place of a Piece of Fabric

Akira Seino

and 3 more

March 25, 2024
In this paper, we propose a Passive Actuator-Less Gripper (PALGRIP) for picking
a piece of fabric from a stack of fabric parts and placing the picked fabric
part. The picking of a piece of fabric from a stack is a simple but difficult
process to automate. The proposed gripper can pick a piece of fabric from the
stack by simply pressing the fingertips of the gripper against the stack. The
fingers are closed and opened by the relative motion between the fingers and the
housing of the gripper. The grasping motion of the gripper is generated by two
mechanisms: a passive pinching mechanism and a selflocking mechanism. These
mechanisms allow the fingers to perform open and close movements and to maintain
the fingers in either open or closed state. The kinematics of the mechanisms are
analyzed to design the gripper. The relation between the movement of the fingers
and the force required to operate the gripper is also investigated through
static force analysis and the experiment. Finally, experiments using PALGRIP are
conducted, and the experimental results illustrate how the pick-and-place
operations are carried out using the prototype of PALGRIP. The proposed gripper
allows the robot to automate fabric pick-andplace operations easily by attaching
it to the robot's endpoint.
Triaxial 3D-Channeled Soft Optical Sensor for Tactile Robots

Matteo Lo Preti

and 3 more

March 19, 2024
Soft optical transducers have the potential to fulfill the need for advanced
tactile sensing in robotics. We present a fingertipshaped soft sensor with
optically transparent channels that relies on soft materials and sensor
morphology to measure an applied triaxial force. The proposed 3D-channeled
sensor has a volume of 2.5 cm3 , and experimental results reveal a fifteen-fold
increase in voltage compared to its bulk analogous, showcasing a sensitivity of
0.34 N/mV and 0.09 N/mV to tangential and normal forces. A prototype with a
diameter of 2 mm (0.4x) indicates the feasibility of scaling down the sensor.
Force magnitude is estimated with a linear model and then decomposed into its
Fxy and Fz with an R2 of 0.93 and 0.98 within a sensing range of 4.05 N and 8.50
N, respectively. A coordinate transformation from a covariant to a cartesian
reference frame is used to retrieve the direction of the tangential component of
the force. The sensor was integrated into a compliant robotic hand as a
proof-of-concept to demonstrate its real-time operation and suitability for
grasping, paving the way for advancements in soft tactile sensors that can be
embedded in soft robots.
Distributed Nonlinear Model Predictive Control for a Quadrotor UAV

Bilal Mubdir

and 1 more

March 19, 2024
A Distributed Nonlinear Model Predictive Control (DNMPC) approach is proposed to
control the simplified decoupled dynamics of a quadrotor UAV. The performance of
DNMPC is compared, in terms of tracking and execution time, to that of standard
control configurations based on centralized MPC and PID control aiming to show
the suitability of each configuration in terms of performance and the
practicality of using a particular configuration in real-time applications. The
results show the advantage of using DN-MPC in terms of ease of tuning and
computational cost over more centralized feedback control approaches.
Indoor Localization based on Short-Range Radar and Rotating Landmarks

Kolja Thormann

and 2 more

March 19, 2024
A novel concept for indoor self-localization based on relative position
measurements to rotating artificial landmarks (with known positions) using
short-range radar is proposed. This includes a complete processing pipeline for
extracting distance and angle measurements from the raw radar data, which
consists of a neural network for distance estimation, a basic angle-of-arrival
estimator, and a particle filter for position tracking. Due to the ability of
radar to measure range rate, i.e., the velocity in the direction of a detection,
it is possible to robustly detect the landmarks by detecting and localizing
their micro-Doppler pattern. This mean localization is possible even under
difficult conditions (e.g., light changes). Experiments with a wheeled mobile
robot and common office fans as landmarks demonstrate the effectiveness of the
approach for indoor localization.
Planning Stories Neurally

Rachelyn Farrell

and 1 more

March 19, 2024
Symbolic planning algorithms and large language models have different strengths
and weaknesses for story generation, suggesting hybrid models might leverage
advantages from both. Others have proposed using a language model in combination
with a partial order planning style algorithm to avoid the need for a
handwritten symbolic domain of actions. This paper offers a complementary
approach. We use a state space planning algorithm to plan coherent multi-agent
stories in symbolic domains, with a language model acting as a guide to estimate
which events are worth exploring first. We evaluate an initial implementation of
this method on a set of benchmark problems and find that the LLM's guidance is
helpful to the planner in most domains.
Automated Loop Fusion for Image Processing

Madushan Abeysinghe

and 3 more

March 19, 2024
In this paper, we develop a method for automatically selecting groups of loops
to fuse in an image processing data flow graph, here referred to as a "fusing
configuration". The method is designed for use on Digital Signal Processors
(DSP), many of which rely on statically scheduled Very Long Instruction Word
architecture. Selection is guided by a heuristic instruction scheduler that
serves as a performance model for a candidate configuration. We show that for
synthetically generated graphs of size 2 to 10 nodes, this approach is capable
of selecting the optimal fusing configuration in 80% of graphs and selects a
configuration that achieves within 10% of the performance of the optimal
configuration for 90% of graphs.
A Lyapunov-based Approach to Nonlinear Programming and Its Application to
Nonlinear M...

Kyunghwan Choi

and 1 more

March 19, 2024
A tuning-parameter-free and matrix-inversionfree solution of nonlinear
programming (NLP) problems is proposed. The key idea is to design an update law
based on Lyapunov analysis to satisfy the first-order necessary conditions for
optimality. To this aim, first, the Lyapunov function is defined as the
summation of the norms of these conditions. Then, the desired optimization
variables and Lagrange multipliers, which minimize the Lyapunov function the
most, are found analytically, thereby rapidly approaching the necessary
conditions. The proposed method neither requires tuning parameters nor matrix
inversions; thus, it can be implemented easily with less iterations and
computational load than conventional methods, such as sequential quadratic
programming (SQP) and augmented Lagrangian method (ALM). The effectiveness of
the proposed method is applied to and validated by using it to solve a nonlinear
model predictive torque control (NMPTC) problem in electrical drives. The
results are compared with those of SQP and ALM.
Vectorized Highly Parallel Density-based Clustering for Applications with Noise

Joseph Xavier Arnold

and 7 more

March 19, 2024
Clustering in data mining involves grouping similar objects into categories
based on their characteristics. As the volume of data continues to grow and
advancements in highperformance computing evolve, a critical need has emerged
for algorithms that can efficiently process these computations and exploit the
various levels of parallelism offered by modern supercomputing systems.
Exploiting Single Instruction Multiple Data (SIMD) instructions enhances
parallelism at the instruction level and minimizes data movement within the
memory hierarchy. To fully harness a processor's SIMD capabilities and achieve
optimal performance, adapting algorithms for better compatibility with vector
operations is necessary. In this paper, we introduce a vectorized implementation
of the Density-based Clustering for Applications with Noise (DBSCAN) algorithm
suitable for the execution on both shared and distributed memory systems. By
leveraging SIMD, we enhance the performance of distance computations. Our
proposed Vectorized HPDBSCAN (VHPDBSCAN) demonstrates a performance improvement
of up to two times over the state-of-the-art parallel version, Highly Parallel
DBSCAN (HPDBSCAN), on the ARM-based A64FX processor on two different datasets
with varying dimensions. Additionally, we evaluate VHPDBSCAN's energy
consumption on the A64FX and Intel Xeon processors. The results show that the
proposed implementation reduces energy consumption by a factor of two on the
A64FX Central Processing Unit (CPU) and by approximately 19.5% on the Intel Xeon
8368 CPU compared to previous methods.
UWB Security and Enhancements

K. Reaz

and 13 more

March 19, 2024
Ultra-Wideband (UWB) technology re-emerges as a groundbreaking ranging
technology with its precise microlocation capabilities and robustness. However,
the security aspects of UWB technology demand thorough scrutiny due to its
widespread use in both consumer and industrial sectors. This white paper
highlights the security dimensions of UWB technology, focusing in particular on
the intricacies of device fingerprinting for authentication, examined through
the lens of state-of-the-art machine learning techniques. Furthermore, we
explore various potential enhancements to the UWB standard that could realize a
sovereign UWB data network. We argue that UWB data communication holds
significant potential in healthcare and ultra-secure environments, where the use
of the common unlicensed 2.4 GHz band-centric wireless technology is limited or
prohibited. A sovereign UWB network could serve as an alternative, providing
secure localization and short-range data communication in such environments.
Data-Driven Insights: Boosting Algorithms to Uncover Electricity Theft Patterns
in AM...

Inam Ullah Khan

and 3 more

March 19, 2024
This study introduces a sophisticated supervised machine learning method for
electric theft detection utilizing a customized Histogram Gradient Boosting
(HGB) algorithm. Comprehensive preprocessing, including imputation,
normalization, outlier management, and resampling, ensures the timeseries data
is accurately prepared for analysis. The SMOTE-ENN algorithm corrects class
imbalances, preparing the data for the feature optimization stage where crucial
features are selected and extracted. The HGB algorithm, enhanced through
Bayesian optimization, is central to the training process, resulting in a model
that precisely classifies electricity consumption patterns as genuine or
fraudulent. The robustness of the model is assessed against other recognized
boosting methods, such as Adaptive Boosting (ADB), Gradient Boosting Decision
Tree (GBDT), and LightGBM, alongside various ensemble and traditional machine
learning models. Utilizing key performance metrics like accuracy, F1 score, and
AUC for validation, the proposed model yields very promising results, with a 93%
accuracy, 95% F1 score, and 98% AUC, outperforming the comparison group under
similar dataset and hyperparameter conditions. This underscores the model's
potential as a highly accurate tool for combating electricity theft within an
advanced metering infrastructure (AMI).
Software Metrics in Agile Software Development: A Review Report

Muhammad Faizan Berlas

March 19, 2024
Modern software systems intensively use the Agile software development processes
for their development and maintenance. The Agile development methodology
encourages customer satisfaction, early incremental delivery, and overall
development simplicity. Agile development methods accept changes in requirements
and technology and use a more adaptive or iterative approach to planning. With
the adaptation of Agile process models in the development of modern software
systems, there is a need for continuous improvement in the Agile processes.
Agile development processes are modified and upgraded by utilizing software
metrics. There are several proposed software metrics for measuring performance
and quality in Agile software development. These include customer satisfaction,
story point estimation, velocity, test coverage, defects in production, and
other metrics. Each software metric has its own merits and demerits. This
research aims to provide a comprehensive review of published research work on
software metrics. Specifically, it summarizes the software metrics used for
measuring the performance of Agile process models. This research paper will help
understand the usefulness of various software metrics used in Agile development,
and it will serve as a foundation for future research in software metrics for
Agile software development.
Model-Based Systems Engineering Applied to Engineering Learning Analytic Systems
(ELA...

Pallavi Singh

and 2 more

March 19, 2024
Engineering education is a complex that involves multiple stakeholders,
including students, educators, administrators, and industry partners. It is
continuously evolving to meet the demands of modern industry and society. The
traditional teaching and learning methodologies are being replaced by a more
integrated skillset that focuses on developing students' cognitive, social, and
emotional skills. The shift towards this integrated approach is gaining
momentum, and it is important to have a framework that has been proven to solve
complex systems. The usage of systems engineering tools to model engineering
education systems is not often seen. In this paper, a novel application of
Model-Based Systems Engineering using Systems Modeling Language (SysML) to
develop an Engineering Learning Analytic System (ELAS) framework that consists
of multi-dimensional elements related to educational systems. The core of this
study involves a rigorous Requirements Verification and Validation (V&V) process
to ensure stakeholder needs which systematically were map with system
capabilities. ELAS model simulations provided predictive insights into soft
skill development, enabling decision-making via targeted interventions that
could significantly enhance students' skill sets. ELAS highlights that a
data-driven approach, enabled by SysML, significantly enhances the ability to
enact timely by relevant interventions at various levels of the educational
management process. The proposed ELAS model offers a strategic blueprint for
continuous improvement within educational institutions, demonstrating a pathway
toward a responsive and self-improving educational system. The refining of the
ELAS model, for broadening simulation scopes, and further integrating predictive
analytics into administrative decision-making processes is an ongoing endeavor.
Multi-Discounting Reinforcement Learning Based on Reward Decomposition

Pengbin Chen

and 4 more

March 18, 2024
A document by pengbin chen. Click on the document to view its contents.
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