Parth Ganeriwala

Fast-scan cyclic voltammetry (FSCV) is a widely used electrochemical technique for real-time monitoring of neurotransmitters; however, quantitative interpretation remains dependent on calibration curves generated in simplified in vitro environments that may not accurately reflect the complexity of biological matrices. To address the need for automated FSCV interpretation, we developed a two-stage artificial intelligence (AI) framework integrating analyte classification and concentration prediction. FSCV data were collected for serotonin (5-HT), dopamine (DA), ascorbic acid (AA), and copper (Cu²⁺) using analyte-specific waveforms, generating a dataset of 30,811 cyclic voltammograms from 192 independent experimental runs. Classification models were evaluated using strict run-level partitioning, while regression performance was additionally assessed using run-level and leave-one-electrode-out (LOEO) validation. Four classification architectures were evaluated: a lightweight image-based convolutional neural network (CNN), a feature-engineered multilayer perceptron (MLP), a transfer-learning ResNet18 model, and a one-dimensional CNN (1D-CNN) trained directly on raw voltammograms. The 1D-CNN achieved the highest classification accuracy (99.8%), outperforming transfer learning (98.68%), the lightweight CNN (97.38%), and the feature-based MLP (76.3 ± 0.6%) while maintaining substantially lower model complexity than ResNet18. Input-channel ablation indicated that waveform-specific acquisition protocols contributed to classification performance. For quantitative analysis, analyte-specific artificial neural networks (ANNs) were trained to predict concentrations from voltammetric morphology, achieving coefficients of determination (R²) of 0.9958, 0.9970, 0.9849, and 0.9561 for 5-HT, DA, Cu²⁺, and AA, respectively, under the original random sample-level partitioning. However, mean R² values decreased to 0.74–0.88 under run-level validation and 0.61–0.86 under LOEO validation, with conventional peak-current calibration outperforming the ANNs under LOEO conditions. Integration of classification and regression models into an automated inference pipeline enabled sequential analyte identification and concentration estimation from FSCV data. In an additional end-to-end evaluation using the 1D-CNN classifier and analyte-specific ANN regressors, incorrect analyte routing, although infrequent (approximately 0.8%), produced substantial concentration-prediction errors. These results demonstrate the potential of morphology-aware deep learning for automated FSCV interpretation while highlighting the need for improved quantitative reliability and further validation using independent electrodes, mixed-analyte samples, and complex biological matrices.

The Indus Valley Civilization (IVC) left behind an undeciphered script containing critical yet inaccessible historical knowledge, posing significant challenges for archaeologists and linguists. Existing automated recognition techniques typically treat Indus graphemes categorically, overlooking their fine-grained spatial configurations and internal structural variations. To overcome this, we introduce FLAIR (Few-shot Learning with Adaptive Inference for Recognition), a novel framework designed to capture subtle visual distinctions in Indus script graphemes through segmentation-guided prototype learning. FLAIR integrates prototypical networks with our segmentation encoder, ProtoSegment , aligning learned spatial prototypes to accurately model intricate grapheme features. We present a new expert-annotated IVC benchmark dataset comprising 39 grapheme classes derived from seal images. Using this benchmark, we show that FLAIR significantly outperforms traditional machine learning methods—especially on rare and visually complex graphemes—while generalizing effectively from minimal data. We further validate FLAIR using controlled experiments on the Omniglot dataset to simulate real-world data scarcity. Our approach achieves state-of-the-art performance in digitizing and recognizing Indus Valley graphemes, laying a foundation for scalable analysis and future decipherment efforts. Code, datasets, and trained models are made publicly available at https://github.com/ParthGaneriwala/few-shot-ivc .

Formal methods have proven helpful in modeling complex systems and ensuring they function correctly. This paper offers some basic guidelines for HFES practitioners in choosing whether to employ formal methods. It also touches on emerging approaches to modeling human behavior, attempting to answer two questions: (1) When are formal methods worth the effort? and (2) How do formal methods apply to humans? A table of questions is offered to facilitate a discussion of tradeoffs in choosing a formal methods approach. Distribution statement A. Approved for public release: distribution unlimited.

Electrochemical sensors, particularly those based on ion transfer at the interface between two immiscible electrolyte solutions (ITIES), offer significant advantages such as high selectivity, ease of fabrication, and cost effectiveness for toxic metal ion detection. However, distinguishing between cyclic voltammograms (CVs) of analytes with closely spaced half-wave potentials, such as Cd²⁺ and Cu²⁺, remains a challenge, especially for non-expert users. In this work, we present a novel methodology that integrates advanced artificial intelligence (AI) models with ITIES-based sensing to automate and enhance metal ion detection. Our approach first employed a convolutional neural network to classify CVs as either ideal or faulty with an accuracy exceeding 95 percent. Ideal CVs were then further analyzed for metal ion identification, achieving a classification accuracy of 99.15 percent between Cd²⁺ and Cu²⁺ responses. Following classification, an artificial neural network was used to quantitatively predict metal ion concentrations, yielding low mean absolute errors of 0.0158 for Cd²⁺ and 0.0127 for Cu²⁺. This integrated AI–ITIES system not only provides a scientific methodology for differentiating analyte responses based on electrochemical signatures but also substantially lowers the expertise barrier for sensor signal interpretation. To our knowledge, this is the first report of the AI-assisted differentiation and quantification of metal ions from ITIES-based CVs, establishing a robust framework for the future development of user-friendly, automated electrochemical sensing platforms for environmental and biological applications.

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are expected to handle increasingly complex situations. However, learning‐enabled algorithms, which are critical to these systems, present significant challenges, including complexity, difficulty in verification, and a lack of certification pathways. A systematic approach integrating architectural analysis with human–machine interaction is crucial to ensuring the safety of these systems. This research emphasizes the early incorporation of human interactions in the design of architectural models to meet safety requirements. These interactions are modeled in the Soar cognitive architecture, which combines symbolic decision logic and numeric decision preferences, refined by reinforcement learning. The agent is then automatically translated into the formal verification environment, nuXmv, where its properties are verified. Our framework integrates systems modeling, formal verification, and simulation to check operational correctness, enhancing the reliability and safety of learning‐enabled autonomous agents.

System and software design benefits greatly from formal modeling, allowing for automated analysis and verification early in the design phase. Current methods excel at checking information flow and component interactions, ensuring consistency, and identifying dependencies within Systems Modeling Language (SysML) models. However, these approaches often lack the capability to perform physics-based reasoning about a system's behavior represented in SysML models, particularly in the electromechanical domain. This significant gap critically hinders the ability to automatically and effectively verify the correctness and consistency of the model's behavior against well-established underlying physical principles. Therefore, this paper presents an approach that leverages existing research on function representation, including formal languages, graphical representations, and reasoning algorithms, and integrates them with physics-based verification techniques. Four case studies (coffeemaker, vacuum cleaner, hairdryer, and wired speaker) are inspected to illustrate the model's practicality and effectiveness in performing physics-based reasoning on systems modeled in SysML. This automated physics-based reasoning is broken into two main categories: (i) structural, which is performed on BDD and IBD, and (ii) functional, which is then performed on activity diagrams. This work advances the field of automated reasoning by providing a framework for verifying structural and functional correctness and consistency with physical laws within SysML models.

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments.

The increasing complexity of autonomous systems has amplified the need for accurate and reliable labeling of runway and taxiway markings to ensure operational safety. Precise detection and labeling of these markings are critical for tasks such as navigation, landing assistance, and ground control automation. Existing labeling algorithms, like the Automated Line Identification and Notation Algorithm (ALINA), have demonstrated success in identifying taxiway markings but encounter significant challenges when applied to runway markings. This limitation arises due to notable differences in line characteristics, environmental context, and interference from elements such as shadows, tire marks, and varying surface conditions. To address these challenges, we modified ALINA by adjusting color thresholds and refining region of interest (ROI) selection to better suit runway-specific contexts. While these modifications yielded limited improvements, the algorithm still struggled with consistent runway identification, often mislabeling elements such as the horizon or non-relevant background features. This highlighted the need for a more robust solution capable of adapting to diverse visual interferences. In this paper, we propose integrating a classification step using a Convolutional Neural Network (CNN) named AssistNet. By incorporating this classification step, the detection pipeline becomes more resilient to environmental variations and misclassifications. This work not only identifies the challenges but also outlines solutions, paving the way for improved automated labeling techniques essential for autonomous aviation systems.

With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system for object detection and tracking. The challenge in the creation of software to solve the task is in discovering the need for a dataset, annotation of the dataset, selection of features, integration and refinement of existing algorithms, while evaluating performance metrics through training and testing. This research effort focuses on the development of a machine learning pipeline emphasizing the inclusion of assurance methods with increasing automation. In the process, a new dataset was created by collecting videos of moving object such as Roomba vacuum cleaner, emulating search and rescue (SAR) for indoor environment. Individual frames were extracted from the videos and labeled using a combination of manual and automated techniques. This annotated dataset was refined for accuracy by initially training it on YOLOv4. After the refinement of the dataset it was trained on a second YOLOv4 and a Mask R-CNN model, which is deployed on a Parrot Mambo drone to perform real-time object detection and tracking. Experimental results demonstrate the effectiveness of the models in accurately detecting and tracking the Roomba across multiple trials, achieving an average loss of 0.1942 and 96% accuracy.

The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. In this paper, we present AssistTaxi, a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing. Researchers can utilize AssistTaxi to benchmark their algorithms, assess performance, and explore novel approaches for runway and taxiway analysis. Addition-ally, the dataset serves as a valuable resource for validating and enhancing existing algorithms, facilitating innovation in autonomous operations for aviation. We also propose an initial approach to label the dataset using a contour based detection and line extraction technique.