The University at Buffalo is a flagship campus of the State University of New York system (SUNY), excelling in engineering, AI, computing, biomedical sciences, medicine, and public health. It is a major center for research and innovation in upstate New York. It fosters research that advances regional and global impact. NECLA partnered with the University at Buffalo on research into adversarial training for visual content generation on large-scale vision systems, data-efficient learning, and multimodal AI for healthcare. We contributed to the refinement of dual projection GANs through collaboration, improving realism and diversity in synthesized images, with implications for both biometric security and creative applications. Please read about our latest news and collaborative publications with the State University of New York at Buffalo.

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3D Finger Vein Biometric Authentication with Photoacoustic Tomography

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Biometric authentication is the recognition of human identity via unique anatomical features. The development of novel methods parallels widespread application by consumer devices, law enforcement, and access control. In particular, methods based on finger veins, as compared to face and fingerprints, obviate privacy concerns and degradation due to wear, age, and obscuration. However, they are two-dimensional (2D) and are fundamentally limited by conventional imaging and tissue-light scattering. In this work, for the first time, to the best of our knowledge, we demonstrate a method of three-dimensional (3D) finger vein biometric authentication based on photoacoustic tomography. Using a compact photoacoustic tomography setup and a novel recognition algorithm, the advantages of 3D are demonstrated via biometric authentication of index finger vessels with false acceptance, false rejection, and equal error rates <1.23%, <9.27%, and <0.13%, respectively, when comparing one finger, a false acceptance rate improvement >10× when comparing multiple fingers, and <0.7% when rotating fingers ±30.

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ELI: Empowering LTE with Interference Awareness in Unlicensed Spectrum

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The advent of LTE into the unlicensed spectrum has necessitated the understanding of its operational efficiency when sharing spectrum with different radio access technologies. Our study reveals that LTE, owing to its inherent transmission characteristics, suffers significant performance degradation in the presence of interference caused by hidden terminals. This motivates the need for interference-awareness in LTE’s channel access in unlicensed spectrum. To address this problem, we propose ELI. ELI’s three-pronged solution equips the LTE base station with novel techniques to: (a) accurately detect and measure interference caused by hidden terminals, (b) collect interference statistics from clients across different channels with affordable overhead, and (c) leverage interference-awareness to improve its channel access performance. Our evaluations show that ELI can achieve 1.5-2x throughput gains over baseline schemes. Finally, ELI is LTE-LAA/MulteFire-standard compliant and can be deployed over the existing LTE-LAA implementation without any modifications.

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