UMBC Computer Science and Electrical Engineering Department

Permanent URI for this collectionhttp://hdl.handle.net/11603/50

The Computer Science and Electrical Engineering Department aims to maintain a program of excellence in teaching, research, and service for all of its programs. At the undergraduate level, we will provide students with a firm foundation of both the theory and practice of Computer Science and Computer Engineering. Our curricula also give students the social, ethical, and liberal education needed to make significant contributions to society. Students receiving a bachelor’s degree are ready to enter the work force as productive computer scientists or computer engineers, or to continue their education at the graduate or professional level.

At the graduate level, we are committed to developing the research and professional capabilities of students in Computer Science, Computer Engineering, Electrical Engineering and Cybersecurity. Our programs provide a deeper mastery of the basics of these fields, as well as opportunities to collaborate on leading-edge research with our faculty. Our faculty are engaged in both practical and theoretical research, often in partnership with government agencies, private industry and non-governmental organizations. The aim of this research is to advance knowledge within our disciplines and also to contribute to solving problems faced by our society.

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Evaluating Causal AI Techniques for Health Misinformation Detection
(IEEE, 2025-03-17) Clark, Ommo; Joshi, Karuna
The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeling and K-Means clustering, we identify clusters prone to misinformation. While the model achieved a 73% accuracy rate, its recall for misinformation was limited to 12%, reflecting challenges such as class imbalance and linguistic nuances. The findings underscore the importance of advanced NLP models, such as transformer based architectures like BERT, and propose the integration of causal reasoning to enhance the interpretability and robustness of AI systems for public health interventions.
Strengthening Workforce Education: Excellence in Programming Securely (SWEEPS)
(ACM, 2025-02-18) Kariuki, Deborah; Ngambeki, Ida; Dai, Jun; Bishop, Matt; Sun, Xiaoyan; Dark, Melissa; Daugherty, Jenny; Lowrie, Alex; Geissler, Markus; Nico, Phillip; Noor, Arshad
This paper presents and advocates for an initiative to expand access to secure programming education. The Strengthening Workforce Education: Excellence in Programming Securely (SWEEPS) initiative, funded by the National Centers of Academic Excellence in Cybersecurity (NCAE-C) program, seeks to advance secure programming and help achieve security aims. SWEEPS establishes a secure programming curriculum and workforce development coalition of seven institutions across two CAE (Center of Academic Excellence) regions (Northeast and Southwest) and five states (California, Massachusetts, Maryland, Indiana, and North Carolina). This coalition includes industry-based stakeholders collaborating with the US Army and government agencies on various projects. SWEEPS draws on prior work establishing critical concepts in secure programming, assessment tools, learning aids, and system infrastructure. The initiative offers a series of interconnected, stackable learning experiences tailored for early to mid-career professionals looking to enhance their cybersecurity skills. These experiences, which include practical one-day workshops and comprehensive year-long graduate certificates, provide a reassuring path for upskilling in secure programming. This paper recommends the efficacy of stackable training approaches in secure programming by exploring the practices of targeting and training individuals with diverse proficiency levels of programming experience who would benefit from increased knowledge and training.
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
(2025-02-13) Richards, Luke E.; Yaros, Jessie; Babcock, Jasen; Ly, Coung; Cosbey, Robin; Doster, Timothy; Matuszek, Cynthia
To create usable and deployable Artificial Intelligence (AI) systems, there requires a level of assurance in performance under many different conditions. Many times, deployed machine learning systems will require more classic logic and reasoning performed through neurosymbolic programs jointly with artificial neural network sensing. While many prior works have examined the assurance of a single component of the system solely with either the neural network alone or entire enterprise systems, very few works have examined the assurance of integrated neurosymbolic systems. Within this work, we assess the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient and more interpretable models. We perform this investigation using Scallop, an end-to-end neurosymbolic library, across classification and reasoning tasks in both the image and audio domains. We assess assurance across adversarial robustness, calibration, user performance parity, and interpretability of solutions for catching misaligned solutions. We find end-to-end neurosymbolic methods present unique opportunities for assurance beyond their data efficiency through our empirical results but not across the board. We find that this class of neurosymbolic models has higher assurance in cases where arithmetic operations are defined and where there is high dimensionality to the input space, where fully neural counterparts struggle to learn robust reasoning operations. We identify the relationship between neurosymbolic models' interpretability to catch shortcuts that later result in increased adversarial vulnerability despite performance parity. Finally, we find that the promise of data efficiency is typically only in the case of class imbalanced reasoning problems.
Your smart home exchanged 3M messages: defining and analyzing smart device passive mode
(HAL, 2025-03) Badolato, Christian; Kullman, Kaur; Papadakis, Nikolaos; Bhatt, Manav; Bouloukakis, Georgios; Engel, Don; Yus, Roberto
The constant connectedness of smart home devices and their sensing capabilities pose a unique threat to individuals’ privacy. While users may expect devices to exhibit minimal activity while they are not performing their intended functions, this is not necessarily the case, and traditional idle mode designations are insufficient to address the current landscape of smart home devices. To address this we propose a passive mode designation based on a comprehensive categorization of smart home devices. We then measure the network traffic of thirty-two devices in their respective passive modes. We find that 97% of the devices exhibit near-constant network activity in these modes (exchanging over 3M messages in 24 hours), with many of the devices initiating and responding to LAN communications with other devices, which potentially exposes users to privacy leakages.
LLM-Supported Safety Annotation in High-Risk Environments
(Open Review, 2025-02-13) Eskandari, Mohammad; Indukuri, Murali; Lukin, Stephanie M.; Matuszek, Cynthia
This paper explores how large language model-based robots assist in detecting anomalies in high-risk environments and how users perceive their usability and reliability in a safe virtual environment. We present a system where a robot using a state-of-the-art vision-language model autonomously annotates potential hazards in a virtual world. The system provides users with contextual safety information via a VR interface. We conducted a user study to evaluate the system's performance across metrics such as trust, user satisfaction, and efficiency. Results demonstrated high user satisfaction and clear hazard communication, while trust remained moderate.
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
(2025-02-27) Barron, Ryan; Eren, Maksim; Serafimova, Olga M.; Matuszek, Cynthia; Alexandrov, Boian S.
Agentic Generative AI, powered by Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and Vector Stores (VSs), represents a transformative technology applicable to specialized domains such as legal systems, research, recommender systems, cybersecurity, and global security, including proliferation research. This technology excels at inferring relationships within vast unstructured or semi-structured datasets. The legal domain here comprises complex data characterized by extensive, interrelated, and semi-structured knowledge systems with complex relations. It comprises constitutions, statutes, regulations, and case law. Extracting insights and navigating the intricate networks of legal documents and their relations is crucial for effective legal research. Here, we introduce a generative AI system that integrates RAG, VS, and KG, constructed via Non-Negative Matrix Factorization (NMF), to enhance legal information retrieval and AI reasoning and minimize hallucinations. In the legal system, these technologies empower AI agents to identify and analyze complex connections among cases, statutes, and legal precedents, uncovering hidden relationships and predicting legal trends-challenging tasks that are essential for ensuring justice and improving operational efficiency. Our system employs web scraping techniques to systematically collect legal texts, such as statutes, constitutional provisions, and case law, from publicly accessible platforms like Justia. It bridges the gap between traditional keyword-based searches and contextual understanding by leveraging advanced semantic representations, hierarchical relationships, and latent topic discovery. This framework supports legal document clustering, summarization, and cross-referencing, for scalable, interpretable, and accurate retrieval for semi-structured data while advancing computational law and AI.
An Improved Autoencoder Approach for Nuclei Image Segmentation
(2025-02-26) Ayanzadeh, Aydin
There is a dire need to enable an early diagnosis system to enhance the therapeutic outcome for patients by applying a medical image analysis application. This study proposes an improved auto-encoder model by integrating Squeeze and Excitation (SE) blocks on the different phases of the model for semantic segmentation, which U-Net inspires. We redesigned the model's skip-connection by utilizing Residual Squeez and Excitation (RSE) by employing SE block in a residual way to reduce the semantic gaps and discrepancy between encoder and decoder features. Then, we integrate the Dense Squeeze and Excitation (DSE) block in the model's bottleneck with a densely connected structure. We increase the model's accuracy compared to vanilla U-Net by integrating the discussed module in the model to enhance its capability for feature extraction and obtain more high-level features from the input feature. To evaluate our model's performance, we conducted our experiment on the 2018 Data Science Bowl dataset and compared it with the different approaches that are inspired by U-Net. Our proposed model achieved the Dice and IoU of 92.15% and 85.92% , respectively, surpassing most of the current stateof-the-art models.
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response
(2025-02-25) Shichman, Mollie; Bonial, Claire; Blodgett, Austin; Hudson, Taylor; Ferraro, Francis; Rudinger, Rachel
Large Language Models (LLMs) have the potential for substantial common sense reasoning. However, these capabilities are often emergent in larger models. This means smaller models that can be run locally are less helpful and capable with respect to certain reasoning tasks. To meet our problem space requirements, we fine-tune smaller LLMs to disaster domains, as these domains involve complex and low-frequency physical common sense knowledge. We introduce a pipeline to create Field Ready Instruction Decoding Agent (FRIDA) models, where domain experts and linguists combine their knowledge to make high-quality seed data that is used to generate synthetic data for fine-tuning. We create a set of 130 seed instructions for synthetic generation, a synthetic dataset of 25000 instructions, and 119 evaluation instructions relating to both general and earthquake-specific object affordances. We fine-tune several LLaMa and Mistral instruction-tuned models and find that FRIDA models outperform their base models at a variety of sizes. We then run an ablation study to understand which kinds of synthetic data most affect performance and find that training physical state and object function common sense knowledge alone improves over FRIDA models trained on all data. We conclude that the FRIDA pipeline is capable of instilling general common sense, but needs to be augmented with information retrieval for specific domain knowledge.
Ransomware Evolution: Unveiling Patterns Using HDBSCAN
(CEUR, 45589) Bhandary, Prajna; Joyce, Robert J.; Nicholas, Charles
This research presents an innovative approach to enhancing ransomware detection by leveraging Windows API calls and PE header information to develop precise signatures capable of identifying ransomware families. Our methodology introduces a novel application of hierarchical clustering using the HDBSCAN algorithm, in conjunction with the Jaccard similarity metric, to cluster ransomware into discrete families and generate corresponding signatures. This technique, to our knowledge, marks a pioneering effort in applying hierarchical density-based clustering to over 1.1 million malicious samples, specifically focusing on ransomware and using the clusters to automatically generate signatures. We show that identifying unique Windows API function patterns within these clusters enables the differentiation and characterization of various ransomware families. Furthermore, we conducted a case study focusing on the distinctive function combinations within prominent ransomware families such as GandCrab, WannaCry, Cerber, Gotango, and CryptXXX, unveiling unique behaviors and API function usage patterns. Our scalable implementation demonstrates the ability to efficiently cluster large volumes of malicious files and automatically generate robust, actionable function signatures for each. Validation of these signatures on an independent malware dataset yielded a precision rate of 98.34% and specificity rate of 99.72%, affirming their effectiveness in detecting known ransomware families with minimal error. These findings underscore the potential of our methodology in bolstering cybersecurity defenses against the evolving landscape of ransomware threats
Global Relevance of Online Health Information Sources: A Case Study of Experiences and Perceptions of Nigerians
(2024-11-13) Clark, Ommo; Joshi, Karuna; Reynolds, Tera L.
Online health information sources (OHIS) offer potential for improving access to health information especially in areas with limited healthcare infrastructure. However, OHIS predominantly originates from Western societies potentially ignoring the specific needs and cultural contexts of diverse populations. There is limited research on the global suitability of OHIS content. This study explores the global relevance of OHIS for diverse populations through a case study examining user experiences of Nigerians living in multiple countries. Findings reveal OHIS usage patterns are influenced by the country of residence and local health services availability. The study highlights the need for culturally inclusive OHIS content to ensure equitable health information access globally. Ultimately, for OHIS to serve a global audience effectively, there needs to be reliable information sources that acknowledge and cater to different users' cultural backgrounds, including prevalent health issues, medical practices, beliefs, languages, and healthcare expectations.
Exploring CS Education Policy Through the Lens of State Governance Models: Access, Accountability and Authority
(ACM, 2025-02-18) Mak, Janice; Torrejon Capurro, Carolina; Garvin, Megean
Computer science (CS) education policy efforts have accelerated since 2016 through the work of various governmental, advocacy, and CS-focused organizations. CS policy implementation is typically led by CS education state supervisors (CSEdSS) at state education agencies (SEA), whose responsibilities may encompass training CS teachers, allocating resources, and informing teacher certification. Despite efforts to expand K-12 CS education through a set of 10 nationally-recommended policies, equity issues persist in terms of access for historically marginalized students to learn CS. Moreover, while states may adopt the same policy, each state has their own model of state education governance (SEG). These models determine authority and accountability - how education decision-making and policies are made and implemented. This study explores the relationship between SEG models and impact of CS education policy implementation by exploring the average rate of growth in access to high school (HS) CS and percent of CS education policies adopted across SEG models. Data sources include publicly available data of secondary CS access and CSEdSS survey and focus groups. Preliminary findings indicate the need to consider SEG models when enacting CS education policy to balance accountability with authority when enacting CS education policy related to expanding equitable and accessible K-12 CS education.
PowerPhone: Unleashing the Acoustic Sensing Capability of Smartphones
(ACM, 2023-10-02) Cao, Shirui; Li, Dong; Lee, Sunghoon Ivan; Xiong, Jie
Acoustic sensing on smartphones has gained extensive attention from both industry and research communities. Prior studies suffer from one fundamental limit, i.e., audio sampling rates on smartphones are constrained at 48 kHz. In this work, we present PowerPhone, a software reconfiguration to support higher sampling rates on both microphones and speakers of smartphones. We reverse-engineered more than 100 smartphones and found that their sampling rates can be reconfigured to 192 kHz. We conducted benchmark experiments and showcased field studies to demonstrate the unleashed sensing capability using our reconfigured smart-phones. First, we improve the sensing resolution from 7 cm to 1cm and enable multi-finger gesture recognition on smart-phones. Second, we push the sensing granularity of subtle movements to 2 μm and show the feasibility of turning the smartphone into a micrometer-level machine vibration meter. Third, we increase the sensing range to 6 m and showcase room-scale human presence detection using a smartphone. Finally, we demonstrate that PowerPhone can enable new applications that were previously infeasible. Specifically, we can detect the home appliance status by analyzing ultrasonic leakages above 24 kHz from the wireless charger while charging a smartphone. Our open-source artifacts can be found at: https://powerphone.github.io.
New Kids on the Block: Estimating Use of Next Generation Gram-negative Antibiotics Across Greater than 700 Hospitals in the United States
(Oxford University Press, 2025-02-12) Harris, Anthony D; Goodman, Katherine E; Pineles, Lisa; Walker, Morgan; Bork, Jacqueline T; Heil, Emily L; Claeys, Kimberly C; Brooks, Justin; Kadri, Sameer; Maron, Bradley A; Baghdadi, Jonathan D
Background In recent years, new broad-spectrum antibiotics targeting Gram-negative organisms have been introduced, including cefiderocol, ceftazidime-avibactam, ceftolozane-tazobactam, eravacycline, imipenem-relebactam, omadacycline, and meropenem-vaborbactam. This study aimed to describe new antibiotic use across a large national cohort. Methods We performed a retrospective cohort study of hospital discharges from June 2022 to May 2023 using the Premier Healthcare Database. Antibiotic utilization was ascertained from daily charges. Clinical indication(s) were inferred from International Classification of Diseases, 10th revision, diagnosis codes. Antibiotic therapy was considered definitive if continued >3 days. Piperacillin-tazobactam was used as a comparator. Results Across 832 hospitals, 3 890 557 admissions (61.9% of all admissions) included an antibiotic prescription. New antibiotics were prescribed in 9768 admissions (0.25% of antibiotic-prescribing admissions) across 537 hospitals. Ceftolozane-tazobactam was prescribed in 4157 admissions (42.6% of 9768), ceftazidime-avibactam in 3660 (37.5%), eravacycline in 1213 (12.4%), cefiderocol in 1060 (10.9%), meropenem-vaborbactam in 456 (4.7%), omadacycline in 104 (1.1%), and imipenem-relebactam in 99 (1.0%). In contrast, piperacillin-tazobactam was prescribed in 731 719 (18.8%) and colistin in 570 (0.01%) admissions. Forty-six percent (n = 4647/9768) of new antibiotics were started in the first 3 days of hospital admission, and 70% (n = 6799/9768) were used as definitive therapy. Sepsis (76%), pneumonia (46%), and urinary tract infection (39%) were the most common clinical indications. On average, patients treated with new antibiotics had 8 more comorbid conditions than patients receiving piperacillin-tazobactam. Conclusions Ceftazidime-avibactam and ceftolozane-tazobactam remain the most frequently prescribed new antibiotics, with uptake of subsequently approved agents trailing. New antibiotics are most commonly used as treatment for sepsis among patients with multiple comorbidities.
Is DOGE a cybersecurity threat? A security expert explains the dangers of violating protocols and regulations that protect government computer systems
(The Conversation, 2025-02-06) Forno, Richard
News reports paint a frightening picture of DOGE staff trampling time-tested ? and in many cases legally required ? management and security practices.
Behavioral, Cognitive, and Functional Risk Factors for Repeat Hospital Episodes Among Medicare-Medicaid Dually Eligible Adults Receiving Long-Term Services and Supports
(Sage, 2024-09-26) Fakeye, Oludolapo; Rana, Prashant; Han, Fei; Henderson, Morgan; Stockwell, Ian
Repeat hospitalizations adversely impact the well-being of adults dually eligible for Medicare and Medicaid in the United States. This study aimed to identify behavioral, cognitive, and functional characteristics associated with the risk of a repeat hospital episode (HE) among the statewide population of dually eligible adults in Maryland receiving long-term services and supports prior to an HE between July 2018 and May 2020. The odds of experiencing a repeat HE within 30 days after an initial HE were positively associated with reporting difficulty with hearing (adjusted odds ratio, AOR: 1.10 [95% confidence interval: 1.02-1.19]), being easily distractible (AOR: 1.09 [1.00-1.18]), being self-injurious (AOR: 1.33 [1.09-1.63]), and exhibiting verbal abuse (AOR: 1.15 [1.02-1.30]). Conversely, displaying inappropriate public behavior (AOR: 0.62 [0.42-0.92]) and being dependent for eating (AOR: 0.91 [0.83-0.99]) or bathing (AOR: 0.79 [0.67-0.92]) were associated with reduced odds of a repeat HE. We also observed differences in the magnitude and direction of these associations among adults 65 years of age or older relative to younger counterparts.
Behind the Curtain: Comparing Predictive Models Performance in 2 Publicly Insured Populations
(Wolters Kluwer Health, 2024-11) Sun, Ruichen; Henderson, Morgan; Goetschius, Leigh; Han, Fei; Stockwell, Ian
Introduction: Predictive models have proliferated in the health system in recent years and have been used to predict both health services utilization and medical outcomes. Less is known, however, on how these models function and how they might adapt to different contexts. The purpose of the current study is to shed light on the inner workings of a large-scale predictive model deployed in 2 distinct populations, with a particular emphasis on adaptability issues. Methods: We compared the performance and functioning of a predictive model of avoidable hospitalization in 2 very different populations: Medicaid and Medicare enrollees in Maryland. Specifically, we assessed characteristics of the risk scores from March 2022 for the 2 populations, the predictive ability of the scores, and the driving risk factors behind the scores. In addition, we created and assessed the performance of an “unadapted” model by applying coefficients from the Medicare model to the Medicaid population. Results: The model adapted to, and performed well in, both populations, despite demographic differences in these 2 groups. However, the most salient risk factors and their relative weightings differed, sometimes dramatically, across the 2 populations. The unadapted Medicaid model displayed poor performance relative to the adapted model. Conclusions: Our findings speak to the need to “peek behind the curtain” of predictive models that may be applied to different populations, and we caution that risk prediction is not “one size fits all”: for optimal performance, models should be adapted to, and trained on, the target population.
PhysiFi: WiFi Sensing for Monitoring Therapeutic Robotic Systems
(2025) Akpabio, Wonder; Bulut, Eyuphan
Patients recovering from limb-impairing strokes require consistent and precise physical therapy (PT) to regain mobility and functionality. Autonomous rehabilitation robots are increasingly adopted during recovery, offering a scalable solution to reduce the burden on physical therapists while assisting patients in performing prescribed exercises accurately. However, the effectiveness of these treatments often relies on professional supervision to ensure patients follow the robot’s movements properly, which could be challenging considering the ongoing shortage of physical therapists. Current PT monitoring systems primarily rely on camera-based technologies, which usually raise concerns due to potential privacy violations and high deployment costs, or wearable devices that are intrusive and uncomfortable for patients. To address these limitations, we propose PhysiFi, a novel approach that leverages ubiquitous WiFi signals available in most indoor environments, such as homes, rehabilitation centers, and assisted living facilities. By analyzing Channel State Information (CSI) from ambient WiFi signals and employing deep learning models, PhysiFi can track and recognize exercises performed by patients with rehabilitation robots. Our experiments demonstrate that PhysiFi can accurately identify prescribed exercises and evaluate whether patients are following the robot’s movements correctly, providing a non-intrusive, privacy-preserving, and costeffective alternative for monitoring physical therapy sessions.
Rainfall Frequency Analysis Based on Long-Term High-Resolution Radar Rainfall Fields: Spatial Heterogeneities and Temporal Nonstationarities
(AGU, 2024) Smith, James A.; Baeck, Mary Lynn; Miller, Andrew; Claggett, Elijah L.
Rainfall frequency analysis methods are developed and implemented based on high-resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub-daily rainfall extremes. The 22-year radar rainfall data set for the Baltimore study region combines reflectivity-based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non-stationary formulations of peaks-over-threshold and annual peak methods. Increasing trends in short-duration rainfall extremes are inferred from both peaks-over-threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short-duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100-year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short-duration rainfall in urban regions. Analyses of high-resolution rainfall fields show that sub-daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub-daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties.
Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning
(Wiley, 2024) Gokhale, Tejas
Models that learn from data are widely and rapidly being deployed today for real-world use, but they suffer from unforeseen failures that limit their reliability. These failures often have several causes such as distribution shift; adversarial attacks; calibration errors; scarcity of data and/or ground-truth labels; noisy, corrupted, or partial data; and limitations of evaluation metrics. But many failures also occur because many modern AI tasks require reasoning beyond pattern matching and such reasoning abilities are difficult to formulate as data-based input–output function fitting. The reliability problem has become increasingly important under the new paradigm of semantic “multimodal” learning. In this article, I will discuss findings from our work to provide avenues for the development of robust and reliable computer vision systems, particularly by leveraging the interactions between vision and language. This article expands upon the invited talk at AAAI 2024 and covers three thematic areas: robustness of visual recognition systems, open-domain reliability for visual reasoning, and challenges and opportunities associated with generative models in vision.
Realizing 3D Visualization U-sing Crossed-Beam Volumetric Displays
(ACM, 1998-08-01) Ebert, David; Bedwell, Edward; Maher, Stephen; Smoliar, Laura; Downing, Elizabeth

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