Digital Commons Network^™

Eeg And Imu Gait Signal Processing: A Comparative Assessment Of The "Reza" Exponential Filter And Classic Filters, Reza Pousti, Daniel M. Russell, Derek C. Monroe, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential "Reza" filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain-body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5-50 Hz, while IMU axes were averaged and band-pass filtered at 0.5-5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5-50 Hz; IMU:0.5-5 …

The Sequence (S) Index As A Marker Of Diminished Step-To-Step Transition Efficiency In Older Adults, Elham Alijanpour, Ashwini Kulkarni, Peter G. Adamczyk, Daniel M. Russell

Rehabilitation Sciences Faculty Publications

Introduction: Age-related declines in walking efficiency are often attributed to musculoskeletal and neuromuscular changes, yet the mechanisms underlying these inefficiencies remain poorly understood. One key contributor is the step-to-step transition (STST), during which the center of mass (CoM) is redirected between limbs. The Sequence (S) index quantifies the temporal overlap between push-off and collision forces during STST, with higher values indicating greater mechanical energy loss. This study investigated whether aging affects STST efficiency, as quantified by the S index, and examined the timing and impulse characteristics underlying observed differences.

Methods: Thirteen young and eleven older healthy adults walked at five …

Patch-Moe Mamba: A Patch-Ordered Mixture-Of-Experts State Space Architecture For Medical Image Segmentation, Diego Adame, Fabian Vazquez Jr., Jose A. Nunez, Huimin Li, Jinghao Yang, Erik Enriquez, Dongchul Kim, Haoteng Tang, Bin Fu, Pengfei Gu

Computer Science Faculty Publications

CNN- and Transformer-based architectures have achieved strong performance in medical image segmentation, but CNNs are limited in modeling long-range dependencies, while Transformers often suffer from quadratic computational and memory complexity. State space models, especially Mamba-based networks, offer an efficient alternative with linear sequence complexity. However, existing Mamba segmentation models still face two limitations: pixel-wise directional scanning can disrupt local 2D spatial structure, and simple summation-based fusion of scan directions cannot adapt well to diverse object sizes, shapes, and boundaries. To address these issues, we propose Patch-MoE Mamba, a patch-ordered mixture-of-experts state space architecture for medical image segmentation. It introduces a …

Guidelines For Standard Basic Notations In Applied Statistics, Abhaya Indrayan, Shivani Saini Ms

COBRA Preprint Series

Whereas some statistical notations are standard and uniformly used by different workers, many are not. Varying notations lead to confusion among the readers, particularly those engaged with applied statistics material, such as medical professionals. This communication proposes that all basic notations be standardized so that the same notations are consistently used by different authors, in different books, journals, and articles for the benefit of those who are not rigorously trained statisticians. Guidelines for such standard notations are also provided. The lead must be taken by statisticians.

Snif: Explainability-Driven Feature Selection Through State-Of-The-Art Shap–Nca Intersection Framework For Malware Detection, Sadia Nazim, Syed Shujaa Hussain, Bilal Yousuf, Jawahir Che Mustapha

Research Outputs: 2025-Present

Modern malware detection rely on Deep neural networks to achieve high detection performance by leveraging a large feature space. However, these models often suffer from high dimensionality, limited interpretability, and reduced reliability due to redundant features. As a result, they obscure the true contribution of features to the rationale for the prediction. To address these challenges, this study proposes an explainability-aware feature pruning technique to improve the performance and efficiency of deep neural networks. The key focus of this study is the development of the novel SHAP–NCA Intersection Framework (SNIF), which integrates explainability with feature selection for efficient and trustworthy …

Six Decades Of Constant Change In Key Earth System Indicators, John Aber

Faculty Publications

Fifteen core indicators of global change and human impacts have been changing at an astonishingly constant rate for nearly 70 years.  Correlations among these indicators support the prevailing scientific understanding of climate change, and suggest how difficult it will be to change course, but there are two trends that, if continued, could lead to a stable global population and a carbon-neutral global economy.

Neuromuscular Adaptations Following Eight Weeks Of French Contrast Training In Elite Youth Football Players: Implications For Athletic Performance, Okan Demir, Tolga Altug, Christopher B. Taber, Mehmet Bayansalduz

Exercise Science Faculty Publications

Neuromuscular training is increasingly recognised for enhancing football performance and athletes’ capacity to tolerate the repetitive high-intensity demands of match play. The French Contrast Method (FCM) combines heavy resistance, plyometric, and ballistic exercises within a post-activation performance enhancement framework, yet evidence in elite youth players during the competitive season remains limited. This study examined the effects of an eight-week in-season FCM program on reactive strength, neuromuscular performance, maximal strength, anaerobic performance, and short-distance sprint ability. Twenty-eight elite male youth football players were randomly assigned to an experimental group (FCM plus regular football training; n = 14) or a control group …

High Temperature Creep Deformation Mechanism Of Fe28.2ni18.8mn32.9al14.1cr6 High Entropy Alloy And Its Modified Alloys, Edwin S. Jiang, I. Baker

Dartmouth College Ph.D Dissertations

Fe28.2Ni18.8Mn32.9Al14.1Cr6 eutectic high entropy alloy exhibits a good combination of room-temperature and high temperature properties, including tensile strength, ductility, and corrosion resistance, making it a promising candidate for structural applications in extreme environments. However, the creep deformation behavior of this alloy— and high-entropy alloys (HEAs) in general—remains insufficiently understood. This dissertation systematically investigates the high-temperature creep deformation mechanisms of Fe28.2Ni18.8Mn32.9Al14.1Cr6 and its derivatives.

Creep mechanisms and associated microstructural changes were examined across a wide range of strain rates using strain-rate jump and constant-stress tests. Two dominant regimes were identified: dislocation glide/solute-drag at low strain rates, and dislocation climb at high …

Basis Design For Electronic Structure And Beyond, Weishi Wang

Dartmouth College Ph.D Dissertations

At the intersection of quantum physics, quantum chemistry, and materials science, electronic structure is the study of electrons in solid-state and molecular systems. Electronic-structure computation relies on discretizing the many-electron Hamiltonian with a finite single-particle basis set. However, basis-set construction is conventionally treated as an ad hoc preprocessing step. This thesis develops an expressive and flexible framework for active, system-oriented basis-set design and numerical modeling strategies that treat basis functions as tunable representations to encode electronic ground-state information.

We first introduce a multi-layered, differentiable basis-construction framework that embeds a set of primitive parameters into mixed-contracted Gaussian-type orbitals. We then develop …

Active Galactic Nuclei Across Scales: From Blazar Jets To Dark Matter Halos, Stephanie Ann Podjed

Dartmouth College Ph.D Dissertations

Within ΛCDM, galaxies form and evolve within dark matter halos, with both central and satellite galaxies contributing to the total stellar mass content. Virtually every massive galaxy harbors a supermassive black hole (SMBH) at its center, with a small fraction of the population growing by actively accreting material; we call this an active galactic nucleus (AGN). The processes giving rise to an AGN phase (i.e., growth of a SMBH) releases enormous amounts of energy in the form of radiation, outflows, and relativistic jets that are able to significantly affect the formation and evolution of galaxies. Blazars are an extreme class …

Early-Time/High-Frequency Electromagnetic Induction Sensing For Minimal-Metal And None-Metallic Subsurface Targets, Michele Louise Maxson

Dartmouth College Ph.D Dissertations

Conventional electromagnetic induction (EMI) systems operate within the quasi-static regime, where measurements are dominated by conduction currents and are primarily sensitive to highly conductive targets and bulk soil properties. As a result, these systems exhibit limited sensitivity to low-conductivity and layered media, such as permafrost, composite materials, and minimum-metal landmines, where diagnostically relevant information resides in early-time/high-frequency electromagnetic responses, generally above 100 kHz. This limitation reflects a mismatch between conventional EMI system design and the underlying target physics, restricting the ability of standard EMI approaches to resolve fine-scale non-metallic subsurface structure.

In this thesis, I investigate early-time/high-frequency sensitivity as a …

Designing Narrative-Based Ai Assistance For Sensemaking In Collaborative Environments: Case Studies In Education And Dementia Care, Dylan Edward Moore

Dartmouth College Ph.D Dissertations

This thesis addresses a gap in the human-computer interaction literature regarding the design, development, and evaluation of narrative-based AI assistance for collaborative, complex problem solving. I explore this design space through three case studies across the domains of education and dementia care. This work encompasses multi-year industry partnerships and longitudinal fieldwork, user-centered design, dataset curation, model training, and system evaluation.

Specifically, the first case study considers a story-based web platform for teaching AI literacy through peer-generated, personalized narrative scaffolding. Learners on the platform showed significant knowledge gains and other learning-related outcomes. To describe the novel design of this system, I …

Knowing The User: Representation, Contextualization, And Predictive Internalization In Personalized Models, Zhongyu Ouyang

Dartmouth College Ph.D Dissertations

Intelligent systems increasingly anticipate which items a person will prefer, adapt generated text to a familiar style, and carry relevant information across interactions. These capabilities invite the impression that a model knows its user. In the technical sense studied here, a model knows a user when it carries information specific to that user, uses that information in the relevant context, and generalizes beyond the observations used to build the user state. Knowing is thus a measurable relation among user evidence, model state, context, and held-out user-specific behavior. The research community generally studies this family of systems under the term personalization, …