We report the design and performance of medium slope efficiency (64%) Holmium-doped fiber amplifiers (HDFAs) with novel alternative in-band pump wavelengths in the 1720–2000 nm spectral region. We demonstrate through simulations that pump wavelengths of 1840–1860 nm can yield significantly improved output power, gain, and optical-optical conversion efficiency compared to the previous technical and industry standard pump wavelength of 1940 nm. Our simulations are verified by experimental data.

A document by Eiman Kanjo . Click on the document to view its contents.

Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. In this study, a simultaneous Bayesian framework was extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation (SSL-SA) is proposed with transformation analysis which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting were embedded in the framework to achieve speedy convergence as well as high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis were embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared to conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyse the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denosing.

A document by Kuan-Jung Chiang . Click on the document to view its contents.

Soil moisture can vary spatially at the scale of agricultural fields (~10 -100 m), which is generally too fine to resolve using passive radiometric methods. Active radar provides an opportunity for finer resolution measurements; in particular, the interferometric synthetic aperture radar (InSAR) closure phase parameter is sensitive to changing soil moisture. We have developed a model showing that systematic non-zero closure phase can result from scattering from objects at different depths in a medium of time-varying dielectric, such as from changes in soil moisture. The model predicts that interference between surface and subsurface reflections are needed for closure phase to be non-zero. We find that, under certain circumstances, we can estimate soil moisture from closure phase using a data reduction approach that includes a cumulative sum of closure phase over time and removal of a trend. The correlation between cumulative closure phase and soil moisture suggests that the closure phase is related to the change in soil moisture. We examined a large test region in Oklahoma, where the detrended cumulative closure phase from Sentinel-1 data demonstrates some agreement with in situ soil moisture measurements. In other areas, the match is weaker, implying a terrain dependence for the quality of fit. Cumulative InSAR closure phase promises to provide a valuable new method to remotely estimate soil moisture.

In this study, we explored a potential design for a 64-tap 16-bit Finite Impulse Response (FIR) Filter, including several optimizations. We used RTL code, synthesized gate-level netlist, and ran PrimeTime analysis for each component and the overall circuit. We present the methodology associated with this approach, and its operational results. The design chosen uses a 16-bit floating point ALU with a dual-clock, first in first out memory architecture. The final FIR core was functional, as measured via timing and power analysis, throughput, maximum clock frequency, area, error rate, and accuracy.

Coronavirus (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most infected people will experience moderate to severe respiratory illnesses and recover without the need for specialized treatment. Some, on the other hand, will become quite ill and seek medical care. Older people and those with poor health conditions such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and get very sick or die at any age. Pneumonia is also an infection in one or both lungs caused by bacteria, viruses, or fungi. The infection leads to inflammation in the air sacs of the lungs, which are called alveoli. The alveoli get clogged with fluid or pus, making breathing difficult. So, for detecting the COVID-19 and Pneumonia in now a days become costly and time taking process especially for common people and radiologists. This paper introduces AUTO- ML process to detect the diseasesc(COVID or Pneumonia) inside the human lung.

Driving energy consumption plays a major role in the navigation of autonomous mobile robots in off-road scenarios. However, real-time constraints often limit the accuracy of the energy estimations, especially in scenarios where accurate wheel-terrain interactions are complex to model. This paper reports on first results of an adaptive deep meta-learning energy-aware path planner that can provide energy estimates of a mobile robot traversing complex uneven terrains with varying and unknown terrain properties. A novel feature of the method is the integration into the meta-learning framework of a 1D convolutional neural network to analyze the terrain sequentially, in the same temporal order as it would be experienced by the robot when moving, and efficiently adapt its energy estimates to the local terrain conditions based on a small number of local measurements. The performance of the method is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains and unstructured geometries. We provide evidence of the benefit of the proposed approach to retain 83% r2 score of the original simulator at 0.55% of the computing time. Finally, we compare the method with alternative state-of-the-art deep learning solutions. In this way, we show indications of its improved robustness to provide more informed energy estimations and energy-efficient paths when navigating over challenging uneven terrains.

This paper introduces a novel procedure for quick estimation of the parameters of a sum of sinusoidal signals based on one-bit measurements. Amplitude, phases and, frequencies of the signal components are assumed unknown, as well as the threshold level of the comparator used to produce measurement results. To provide enough information at the one-bit quantizer input, a sinewave is assumed to dither one of the two comparator's inputs. To ease the procedure's application, only the peak-to-peak amplitude of this dither signal is assumed known. Theoretical, simulation-based and experimental results validate the presented approach.

A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using wireless and cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of available resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from these connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient. Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. Autonomous driving cars and personal companions are some of the examples that benefit from Embodied AI nowadays. In this paper, we attempt to do a concise review of this field. We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions that are being used in different components of it (e.g. Vision, NLP, RL). We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible. Finally, we will address its challenges and identify its potentials for future research.

In this paper, an approach for helping undergraduate students understand complex algorithms through structured flowcharts (SFCs) and their implementation using graphical programming (GPR) on LabVIEW and myRio is presented. Space vector pulse width modulation (SVPWM) is an important algorithm for students studying power electronics. But, when studying this algorithm, a lot of students often find difficulty in comprehending it. In this paper, we have presented a method which helps students understand SVPWM easily and enables them to implement it on hardware with less difficulty. This article also intends to show the effectiveness of LabVIEW and myRio as constructive educational tools. The proposed approach can also be applied to teaching other complex algorithms. The proposed approach makes use of SFCs to help students develop a theoretical foundation of SVPWM in a theory class. The theory session is followed by the practical implementation of SVPWM using GPR on LabVIEW and testing it on a test-bed (a 3-phase motor connected to myRio) in two laboratory session.

We demonstrate a radio frequency (RF) phase-encoded signal generator as well as a user-defined RF arbitrary waveform generator (AWG) based on a soliton crystal micro-comb generated by an integrated MRR with a free spectral range of ~49 GHz. Owing to the soliton crystal’s robust and stable generation as well as the high intrinsic efficiency, RF phase-encoded signal generators and AWGs with simple operation and fast reconfiguration are realized. The soliton crystal micro-comb provides 60 wavelengths for RF phase-encoded signal generators, achieving a phase encoding speed of 5.95 Gb/s and a high pulse compression ratio of 29.6. Over 80 wavelengths are employed for the AWGs, achieving tunable square waveforms with a duty cycle ratio ranging from 10% to 90%, sawtooth waveforms with tunable slope ratios from 0.2 to 1, and symmetric concave quadratic chirp waveforms. Our system has great potential to achieve RF and microwave photonic signal generation and processing with low cost and footprint.

The ever-emerging mobile market induced a blooming interest in stylus-based interactions. Most state-of-the-art styluses either provide no haptic feedback or only deliver one type of sensation, such as vibration or skin stretch. Improving these devices with display abilities of a palette of tactile feels can pave the way for rendering realistic surface sensations, resulting in more natural virtual experiences. However, integrating necessary actuators and sensors while keeping the compact form factor of a stylus for comfortable user interactions challenges their design. Consequently, it limits the scientific knowledge of relevant parameters for rendering compelling artificial textures for stylus-based interactions. Here, we developed FeelPen, a haptic stylus that can display multimodal texture properties (compliance, roughness, friction, and temperature) on touchscreens. We created a set of artificial textures and conducted a user study in which human participants rated these textures based on various perceptual adjectives describing their haptic properties. Afterward, we extracted the dominant perceptual dimensions of these textures using the semantic differential method. Our results revealed four major tactile dimensions, with the first two related to surface textural properties, and the third and fourth dimensions linked to material softness and coldness, respectively. Our unique design and experimental results have the potential to open new perspectives for stylus-based interactions on future touchscreens.

Global Navigation Satellite Systems (GNSS) have been the primary positioning solution for Unmanned Aerial Vehicles (UAVs) due to their worldwide coverage, high precision, and lightweight receivers. However, GNSS is prone to electromagnetic interference and malicious assaults, including jamming or spoofing because of its low signal-to-noise ratio (SNR). Using redundant navigation systems is essential to ensure the continuity and protection of UAV operations. In recent years, the phased array radio system (PARS) has established itself as a local navigation solution. PARS is robust towards malicious assaults because of a much higher SNR than GNSS regarding directed and encrypted transmission. An essential factor of PARS is that the orientation of the radio antenna at a ground station needs to be precisely determined to obtain the correct positioning of UAVs. This paper presents a method for extending a previously proposed calibration algorithm to estimate the ground antenna orientation with an inertial navigation system (INS) aided by redundant positioning sensors (GNSS, PARS, or barometer) using a multiplicative extended Kalman filter (MEKF) so that the calibration can be activated during flights whenever GNSS is available. In other words, the proposed navigation system is essentially an aided-INS that switches between two modes depending on the availability of GNSS: calibration and GNSS aiding mode when GNSS is available (Mode 1) and PARS and barometer aiding mode when GNSS is unavailable (Mode 2). Considering that the navigation system needs to include the effect of Earth’s curvature for a long-distance flight, PARS horizontal measurement and the barometer measurement were treated independently, and the navigation equations were propagated in Earth Centred Earth Fixed (ECEF) frame. The independent treatment of barometer measurement, and the propagation in ECEF frame were also beneficial when using multiple ground antennas to have a common reference point and reference frame. The proposed method was validated using data (Inertial Measurement Unit (IMU), GNSS, PARS, Pixhawk autopilot (including barometer) measurements) collected during a field test. In the validation, GNSS was made available at the middle of the flight and the calibration mode was activated for 200s. The proposed navigation system successfully estimated the precise orientation of multiple ground antennas and the navigation solutions were verified using GNSS and Pixhawk autopilot solutions as ground truth.

In recent years, there has been a growing trend in research on smart contract applications. Smart contracts potential in the energy landscape is visible in their major applications such as peer-to-peer energy trading, electric vehicle charging, energy market management, and many more. Many studies have been conducted that produced a lot of literature in this area and many startups and companies have surfaced that exhibit large scope of its application in the energy sector. However, in comparison to other domains, there is still more development required. The literature available focuses on the different technical aspects and use cases, but there’s no such scientific article providing gathered details of the smart contracts development process that invites the attention of researchers in the energy domain for development or provides basic knowledge of available tools. It is therefore necessary to contribute with academic articles that summarize this information, thus opening up paths of development in this field and strengthening the community. This paper is the first step towards the implementation of this idea and that is intended to be extended in the future.

Daugman’s design of IrisCode continues fascinating the research world with its practicality, efficiency, and outstanding performance. The limits of Daugman’s recognition system, however, remain unquantified. Multiple approaches to scale performance have been explored in the past. Despite them, the problem of finding the capacity of IrisCode remains open. In an attempt to fill the gap in understanding the performance limits of Daugman’s algorithm, we turn to an analysis of the relationship between the size of the population that the IrisCode can effectively cover and the iris sample quality. Given Daugman’s IrisCode algorithm, the problem of finding its capacity is cast as a basic Rate-Distortion/Channel Coding problem. The Hamming, Plotkin, and Elias-Bassalygo upper bounds on the population of a binary code under the constraint of a minimum Hamming Distance between any two codewords is applied to relate the number of iris classes that the IrisCode algorithm can sustain and the quality of iris data expressed in terms of Hamming Distance.

Road traffic crashes in urban areas pose significant safety and efficiency challenges. This paper presents an approach, merging case studies and literature reviews, to mitigate urban road traffic crashes and enhance safety. The study commences with an extensive literature review, delving into existing research on road traffic crashes. This review establishes the groundwork for comprehending urban road safety solutions, insights into ongoing research trends, and guiding case study selection. Three case studies parallel analogous cities where Intelligent Transportation Systems and other smart city initiatives targeting road safety succeeded. Each case dissects safety interventions, accomplishments, and lessons learned. Scrutinizing these studies aims to unveil trends underpinning a holistic road safety strategy. From literature and case studies, a comprehensive road safety strategy emerges. It leverages advanced technology, data insights, and proactive measures to enhance road safety, curtail accidents, and optimize urban traffic. This research yields an all-encompassing road safety strategy for policymakers, planners, and safety practitioners to elevate urban road safety. It promotes secure, efficient, technologically advanced urban areas, endorsing sustainable transportation, elevating residentsâ\euro™ lives, and positioning cities as urban mobility leaders. Furthermore, this study contributes to the academic literature on road safety and urban transportation. Through case studies and literature reviews, it supplements knowledge about effective road traffic crash mitigation strategies. These insights have the potential to guide future research, advancing road safety and urban transportation domains.

A document by Hien Nguyen . Click on the document to view its contents.