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Point cloud registration is a critical component of many tasks including the estimation of sensor motion and 3D reconstruction. The Iterative Closest Point (ICP) algorithm and its variants were initially used to solve such problems and since then, various global methods have been proposed to improve registration when data outliers exist. Realistic point cloud datasets contain detection errors (outliers) as well as spatial errors, making correct registration fragile when the outlier rejection and point data association methods fail. Therefore, in this article, a registration technique based on the multi-object Cardinalized Optimal Linear Assignment (COLA) metric is presented, which jointly penalizes both detection and spatial errors in a meaningful manner. This allows robust scan registration to take place in the presence of unknown point correspondences and inter-scan translation and orientation as well as point cloud detection and spatial errors. The resulting Particle Swarm Optimization (PSO)-COLA registration algorithm is capable of determining inter-scan point correspondences, but can also run based on point correspondences determined by other algorithms, such as the application of Fast Point Feature Histograms (FPFH) descriptors. In both cases, the PSO-COLA algorithm is shown to outperform current local and global point cloud registration algorithms in the presence of data outliers and spatial uncertainty. Multiple experiments and comparisons are based on the Stanford Bunny and Dragon, ModelNet40 and ETH Apartment datasets.

Unmanned aerial systems/vehicles (UAS/UAVs) are increasingly utilized for inspecting high-voltage (HV) Tx lines. Operating on batteries, these UAVs are equipped with various electrical sensors, microprocessors, and motors. All of these sensors are susceptible to electric (E) and magnetic (H) fields, which may affect sensing, communication, and control operations of UAS. This paper is first-of-its-kind large scale study that gathered field data (E/H) across 5 different Tx lines ( $69 kV_{AC}$ , $230 kV_{AC}$ , $345 kV_{AC}$ , $500 kV_{AC}$ , and $250 kV_{DC}$ ). Additional flight data on a microwave tower was also gathered. Multiple types of DJI UAVs (M2EA, M30, and M300) are used to carry E/H field sensors, and radio frequency (RF) sensors as payload for this study. Specific measurements include the E field in V/m, H field in mG, Battery voltage in V, Battery current in A, Battery percentage, Battery Temperature in °F, latitude, and longitude. The overall goal of the paper is to quantify E/H field distribution in HVTx lines which aided in realization of FAA rule making on how close UAVs could fly safely. The preliminary findings indicate that significantly higher E/H field level were reported near AC than DC Tx lines. The paper discusses conditions influencing E/H field strength during UAV operations. Additionally, hybrid machine learning (HML) models (RFR-SVM, RFR-KNN) were used to forecast battery drain. The results indicate that the hybrid RFR-KNN model yields lower MAPE values compared to stand-alone and other hybrid models.

Goal: Dynamically monitoring serotonin in real-time within target brain regions would significantly improve the diagnostic and therapeutic approaches to a variety of neurological and psychiatric disorders. Current systems for measuring serotonin lack immediacy and portability and are bulky and expensive. Methods: We present a new miniaturised device, named SmartFSCV, designed to monitor dynamic changes of serotonin using fast-scan cyclic voltammetry (FSCV). This device outputs a precision voltage potential between −3 to +3 V, and measures current between −1.5 to +1.5 μA with nano-ampere accuracy. The device can output modifiable arbitrary waveforms for various measurements and uses an N-shaped waveform at a scan-rate of 1000 V/s for sensing serotonin. Results: Four experiments were conducted to validate SmartFSCV: static bench test, dynamic serotonin test and two artificial intelligence (AI) algorithm tests. Conclusions: These tests confirmed the ability of SmartFSCV to accurately sense and make informed decisions about the presence of serotonin using AI.

We research how deep learning convolutional neural networks can be used to automatically classify the unique data set of black-and-white naval ships images from the Wright and Logan photographic collection held by the National Museum of the Royal Navy. We contrast various types of deep learning methods: pretrained models such as ConvNeXt, ResNet and EfficientNet, and ConvMixer. We also thoroughly investigate the impact of data preprocessing and externally obtained images on model performance. Finally, we research how the models estimated can be made transparent using visually appealing interpretability techniques such as Grad-CAM. We find that ConvNeXt has the best performance for our data set achieving an accuracy of 79.62% for 0-notch classification and an impressive 94.86% for 1-notch classification. The results indicate the importance of appropriate image preprocessing. Image segmentation combined with soft augmentation significantly contributes to model performance. We consider this research to be original in several aspects. Notably, it distinguishes itself through the uniqueness of the acquired dataset. Additionally, its distinctiveness extends to the analytical modeling pipeline, which encompasses a comprehensive range of modeling steps, including data preprocessing (incorporating external data, image segmentation, and image augmentation) and the use of deep learning techniques such as ConvNeXt, ResNet, EfficientNet, and ConvMixer. Furthermore, the research employs explanatory tools like Grad-CAM to enhance model interpretability and usability. We believe the proposed methodology offers lots of potential for documenting historic image collections.

In this article, we present a comprehensive sensitivity analysis and geophysical retrieval product demonstration to assess the enhanced information content in atmospheric temperature and water vapor, harnessed in hyperspectral microwave measurements. A particular focus of this study is devoted to quantifying and comparing the impact on retrieval performance resulting from novel spectral bands of the microwave thermal spectrum, by means of data addition and data denial trade studies. Various spectral configurations are assessed, each reflecting specific technology solutions intended to maximize geophysical product performance within feasible size, weight, power, and cost constraints. Our results indicate that the use of a hyperspectral sampling in the oxygen and water vapor sounding lines alone provides significant improvements in the lower and free tropospheric thermodynamic fields (up to $\sim$40%), when compared against the program of record (i.e., the Advanced Technology Microwave Sounder, ATMS). Our experiments also demonstrate the essential role played by extending the coverage in the window regions, leading to an overall improvement of up to $\sim$50% in the Earth's planetary boundary layer thermodynamic fields. This work concludes with an overview on the state of the art in hyperspectral microwave technology and a discussion on future applications of interest to numerical weather prediction and climate science. The work presented in this study focuses on ocean, clear-sky demonstrations. All-sky, all-surface investigations will be the focus of a follow-up study, as we advance our capability to simulate more complex scenarios and improve scene variability.

Goal-based agents need to be resilient to perturbations in the world. Existing resilience definitions emphasize maintenance-type goals and, consequently, describe how well systems can recover and return to a desirable operating state after a perturbation. An alternative formulation of resilience is required for achievement-type goals that emphasize the ability to progress towards a goal state. This manuscript proposes a new formalism of resilience as a computational construct that accounts for an agent’s sensors, effectors, communication channels, and computational resources. Two metrics for comparing the resilience of different algorithms are derived, namely power and efficiency. Three case studies demonstrate how the metrics can be used to characterize power-efficiency tradeoffs in algorithm design. A common property of the resilient algorithms in the case studies is that they have the ability to exploit many possible world trajectories, often at the cost of failing to find optimal trajectories in unperturbed conditions.

The limited availability of peer-reviewed scientific evidence in the cannabis industry has led many companies to rely on techniques derived from non-peer-reviewed sources for their practices. This study begins by examining the literature on the cultivation of C. sativa, investigating optimal conditions and their effects on growth, and characterising the requirements for greenhouse monitoring and control. A systematic review of current technological approaches is then conducted. The review demonstrates that technology-based control of greenhouse environments has the potential to surpass manual or traditional rule-based management techniques by reducing costs and increasing yields. However, the adoption of these technologies is impeded by the lack of publicly available labelled data on growth, pest and disease, environmental, and yield data of multiple indoor cultivation cycles. Currently, much of the research in this field is conducted privately by companies in the cannabis industry. This study recognises substantial gaps in research surrounding C. sativa cultivation and emphasises the opportunity for new research to address the absence of available C. sativa datasets and peer-reviewed scientific studies outside of private endeavours.

In this paper, we investigate a shortened horn antenna with high gain that is enabled by a 3D-printed gradient index (GRIN) lens composed of high permittivity zirconia $(ZrO_{2})$ . The baseline H-plane sectoral horn antenna is designed with length that is 1/3 of the optimal horn antenna and exhibits a low gain due to the high flaring rate of the horn. Increased gain is achieved by adding a flat GRIN lens at the horn aperture. High permittivity $ZrO_{2}\,\,(\varepsilon _{r} = 23)$ enables lens miniaturization; however, when interfaced with air, reflections at the air interface increase the impedance mismatch. Two different methods for mitigating the reflections are studied. One is a simple $\lambda /4$ matching layer that matches the bulk permittivity of $ZrO_{2}$ to air. As expected, the $\lambda /4$ layers reduce the reflections in part of the 13–18 GHz band but produce high reflection in other parts. The second approach is a GRIN lens with integrated tapered matching layer to match phase and impedance simultaneously. Three tapering methods are studied (exponential, Klopfenstein, linear) for impedance matching. Analytical expressions of the minimum thickness and permittivity distribution are derived. The lens is discretized for print and three types of unit cells are proposed to create a wide range of permittivities ranging from bulk ceramic to air. A $ZrO_{2}$ lens prototype printed with an XJet Carmel 1400 is measured and results show good agreement with simulations, including gain performance equivalent to a horn of 2.4x longer length. The measured gain and beamwidth of the lens are 5.4dB higher and 52° narrower than those of the shortened horn alone at 15 GHz, respectively.

Inspection-based techniques are being widely used for aging management of passive structures in nuclear power plants (NPPs). This paper presents a condition-based maintenance approach to detect corrosion in NPPs secondary piping structures using distributional features extracted from the total unwrapped phase signal of accelerometers. To demonstrate a condition-based maintenance approach, a scaled-down experimental testbed and data-driven methodology are developed and validated for detecting surrogate corrosion processes in a piping structure. The surrogate corrosion process is emulated by removing different levels of mass from the pipe-bend section and measuring the vibration of the pipe bend using five tri-axial accelerometers. The data collected are processed using the Hilbert-Huang transform. Distributional features of the total unwrapped phase from each accelerometer are used to develop a binary classifier to predict a degraded pipe against baseline pipes (healthy state). The binary classification model is extended to a multiclass classification to predict the level of mass removal. Binary and multiclass classification are performed using traditional machine learning algorithms such as logistic regression, neural networks, random forest, and support vector machine. All the algorithms achieved at least 99% prediction accuracy in each direction of the tri-axial accelerometer and significantly outperformed intrinsic mode function-based prediction methods. Performance of classification algorithms were evaluated using data for different number of the tri-axial accelerometers, including a single sensor. The overall prediction accuracy for all these cases was over 99%. Also, the models were interpreted using Shapley additive explanation values to understand the contribution of each input feature in diagnosing the percentage of mass removal. Finally, the performance of a transfer learning model is demonstrated to adopt a machine learning model on to an another elbow, and the prediction performance over 99% is achieved with partial retraining.

We show theoretically that the characteristic modes of dielectric resonator antennas (DRAs) must be capacitive in the low frequency limit and that as a consequence of this constraint and the Poincaré Separation Theorem, the modes of any DRA consisting of partial elements of an encompassing super-structure with the same spatial material properties cannot resonate at a lower frequency than the encompassing structure. Thus, design techniques relying on complex sub-structures to miniaturize the antenna, including topology optimization and meandered windings, cannot apply to DRAs. Due to the capacitive nature of the DRA modes, it is also shown that the Q factor of any DRA sub-structure will be bounded from below by that of the super-structure at frequencies below the first self-resonance of the super-structure. We demonstrate these bounding relations with numerical examples.

Maintaining the viscosity and density of drilling fluids within their optimal performance margins is of utmost importance in running a safe and efficient operation when drilling oil and gas wells. Safety and efficiency may be adversely affected by current measurement practices of these two key properties as they still mostly rely on onsite personnel to carry out periodic manual measurements. These laborious measurement protocols limit the quantity and quality of the produced data. Here we describe an automated system for viscosity/density measurements of drilling fluids based on a tuning fork electromechanical resonator. The resonator works as the frequency-defining element of an oscillator circuit, and a digital gain control algorithm is used to control the feedback loop and actuate the device. The self-oscillating circuit allows the measurement to be repeated several times per second, enabling higher measurement precision and real-time monitoring in changing flows. The resonator was integrated into an in-tank system, which performed continuous measurements at the suction tank of the mud circulating system during drilling operations. Measurements in three different wells are presented. The results were in very good agreement with manual measurements reported from standard mud reports and further highlighted opportunities in detecting operational problems from the real-time data streams obtained.

Objective: There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. Methods: The present study reports the application of a neural network classifier to the processing of previously collected data on very-low-power radiofrequency propagation through the wrist to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low Bone Mineral Density (BMD), characterized by a Dual X-Ray Absorptiometry (DXA) T-score below – 1, measured within one year. Group 2 included 40 healthy and mostly young subjects without major clinical risk factors such as a (family) history of bone fracture. We process the complex radiofrequency spectrum from 30 kHz to 2 GHz. Instead of averaging data for both wrists, we process them independently along with the wrist circumference and then combine the results, which greatly increases the sensitivity. Measurements along with data processing require less than 1 min. Results: For the two dichotomic groups identified above, the neural network classifier of the radiofrequency spectrum reports a sensitivity of 83% and a specificity of 94%. Significance: These results are obtained without including any additional clinical risk factors. They justify that the radio transmission data are usable on their own as a predictor of bone density. This approach has the potential for screening patients at risk for fragility fractures in the office, given the ease of implementation, small device size, and low costs associated with both the technique and the equipment.

A novel scheme for transmitting broadband phase shift keyed signals from electrically small antennas using energy-synchronous direct antenna modulation is described. We outline fundamental operating principles of the method and experimentally compare its performance to that of a conventional band-limited transmit antenna with the same electrical size and radiation efficiency. Transmitted waveforms are analyzed in the time domain both at RF and baseband. Results show significant increases in signal quality, suggesting a larger effective transmit bandwidth and greater potential throughput when the proposed direct antenna modulation scheme is used.

Due to many technical and programmatic changes, distributed spacecraft missions (DSMs) and constellations are becoming more common, both in national space agencies as well as in industry and academia. These changes are the results of various driving factors, such as maturing technologies, minimizing costs, and new science requirements. But they are also made possible by the availability of easier and more frequent launches and the capability to handle increased requirements in terms of scalable mission operations and “big” data analytics on the ground and onboard. With the increase in this type of missions and with the need to connect and interrelate all the data that will be generated by these various missions as well as with the data acquired from ground and airborne sensors, there is a need to define more accurately all the terms used in relation to DSMs. This article presents a terminology including various definitions that describe DSMs and related concepts, their organization, physical configuration, and functional configuration, as well as a taxonomy from which DSMs can be designed.

Direct antenna modulation (DAM) methods using time-varying matching networks offer the possibility to reduce the detrimental effects on broadband signal transmission from a narrowband antenna. We propose time- and frequency-domain models for the effects of a DAM scheme for the transmission of ON-OFF-keyed RF signals and test the validity of the proposed models through over-the-air measurements at 27.12 MHz. Random bit sequences are transmitted separately using both a switched-mode DAM transmitter and a conventional transmitter and received in the far field. Comparison of measured data with our frequency-domain models shows that the DAM transmission method partially removes the effects of narrowband filtering due to the transmit antenna. Models and measurements also demonstrate that the removal of this filter improves the signal quality as observed in eye diagrams of the demodulated received signals. Finally, a simple time-domain model of the DAM transmitter is shown to predict the radiated transient signal under varying synchronization conditions.

As high-precision instrumentation is becoming more portable and cost-effective, an opportunity has arisen to move the electrochemical analysis techniques out of the laboratory and into novel application environments. The increased demand in electrochemical systems driven by a new age of technology sees the need for devices that are adaptive to the following: 1) portable, allowing for handheld and user-friendly field-based testing; 2) embeddable, allowing for long-term in situ assays; and 3) low cost, allowing for largescale, parallel simultaneous experiments. To address these needs, we propose a potentiostat platform which allows for a variety of electrochemical assays to be performed on a miniaturized, battery-powered, low-cost device. This device incorporates the three key components of a potentiostat: output stage, input stage, and control/communications, into a single 27 mm × 20 mm footprint. The device is evaluated through several bench tests which confirmed the accuracy of both the precision voltage output and current measurement input of the device. Subsequently, three electrochemical experiments were conducted to evaluate the realworld performance and application of the device. These experiments allowed for the confirmation of the devices capability to accurately perform chronoamperometry, cyclic voltammetry, and anodic stripping square wave voltammetry. These experiments clearly indicated that the device operates as an analytically useful potentiostat, outputting accurate voltages, and precisely measuring the resulting current.

Zero clients are hardware-based devices without a central processing unit (CPU) that deliver virtual desktops (VDs) from remote computing systems to users. We measured the performance of applications accessed through zero clients to study the feasibility of using this approach to provide a desktop-pc experience across a network. Performance evaluation is complicated because monitoring software cannot be downloaded to the zero clients. Therefore, we introduce a new methodology and metric to measure zero-client VD performance that is based on network-traffic analysis. We conducted objective and subjective studies to determine the sensitivity of application-specific metrics to different network conditions. The results show that the packet loss rate (PLR) impacts zero-client performance for some applications such as video streaming. Subjective tests showed a greater user sensitivity to the PLR for video streaming than for image viewing or Skype. A strong correlation was found between the objective and subjective measurements but the rate at which these measurements changed with increasing PLR differed depending on the application.

This paper describes a liquid metal-based multifunctional antenna capable of wideband frequency tuning, dual band operation, and polarization reconfiguration. The radiating elements consist of parasitically-excited plugs of room-temperature liquid metal in 3D printed channels. Syringe pumps flow the gallium-alloy plugs in proximity to a capacitive feeding structure. This non-contact feeding scheme separates the metal flow path from the SMA connector and lends mechanical robustness at the feed while allowing impedance matching over a wide range of frequencies. Sliding the plug along a right-angle bend enables linear polarization reconfiguration, while simultaneously placing plugs in both orthogonal channels can generate circular or 45° linear polarization. Dual band operation is also supported by infusing plugs of two dissimilar lengths into the two channels. Simulation and measurement results demonstrate that this antenna can tune its impedance over a decade (10:1 frequency range) maintaining a 2:1 VSWR and achieve a polarization diversity >12 dB. The pumped plugs can circulate at a peak velocity of 50 mm/s, currently limited only by our pumping equipment. Repeatability analysis is also performed by cycling the plug actuation more than 1100 times. More complex designs can exploit this design concept to develop new types of highly versatile, multi-functional antennas.

Many problems faced by decision makers today involve the management of large scale, complex systems that can be modeled as state-based control problems, specifically discrete Markov decision process (MDP). Typical examples include transportation systems, defense systems, healthcare networks, financial organizations, and general infrastructure problems. In all of these problems, decision makers have difficulty in forecasting the state of their system in the future and capturing the dynamics of the states over time. In this paper, we discuss, via numerous examples, practical experiences in trying to build such models. Much of the literature discusses theoretical issues of solution convergence and algorithm performance; unfortunately, much of this research does not help with the practical business of building an actual MDP model. Thus, numerous books begin with statement of the nature: "given the state space S. . .." A critical question to the practitioner is the creation of this state space "S." We focus on this first step in the MDP modeling process, an often neglected and difficult step, and we discuss the practical implications and issues associated with the state definition, illustrating these issues with numerous examples. This paper is not meant to be a survey of "state-based" applications or MDP applications, but an overview of experiences building many of these models in diverse applications.