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Howard University

Academic institutionnorthamerica · us
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Research library14linked papers
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Selected work

Representative Papers

Reference-Free Spectral Analysis of EM Side-Channels for Always-on Hardware Trojan Detection

Jan 28, 2026

This work proposes the first reference-free detection method for always-on hardware Trojans (HTs), addressing the challenge of identifying such malicious circuits in the absence of a golden reference model. The approach leverages multi-window short-time Fourier transform (STFT) to extract time-frequency features from electromagnetic side-channel emissions and employs Gaussian mixture models (GMMs) to capture the statistical structure of circuit behavior. Detection is achieved by exploiting the distinct statistical stability patterns in the time-frequency spectra between Trojan-free and HT-infected circuits. Experimental validation on an AES-128 implementation demonstrates that the method accurately distinguishes circuits with always-on HTs from benign ones, significantly enhancing hardware security assurance in reference-free scenarios.

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Generalized Normal Constraint (GNC): A Complete Geometric Generalization of the NNC Method

Jul 01, 2026

This work addresses the critical limitation of existing multi-objective optimization methods—such as weighted sum, NNC, and NBI—which frequently fail to fully capture the Pareto front in problems with three or more objectives, often exhibiting omission rates exceeding 50%. To overcome this, the authors propose the Generalized Normal Constraint (GNC) method, which establishes a unified geometric and computational framework. By integrating Pareto front mesh construction with a normal constraint mechanism, GNC structurally guarantees 100% coverage of the feasible Pareto region for any n-objective problem. This approach fundamentally resolves the factorial degradation in coverage that plagues conventional techniques as the number of objectives increases. Theoretically ensuring solution set completeness, GNC significantly outperforms current methods and offers a more comprehensive solution for multi-objective optimization in fields such as engineering design and economic decision-making.

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Latest Papers

Generalized Normal Constraint (GNC): A Complete Geometric Generalization of the NNC Method

Jul 01, 2026

This work addresses the critical limitation of existing multi-objective optimization methods—such as weighted sum, NNC, and NBI—which frequently fail to fully capture the Pareto front in problems with three or more objectives, often exhibiting omission rates exceeding 50%. To overcome this, the authors propose the Generalized Normal Constraint (GNC) method, which establishes a unified geometric and computational framework. By integrating Pareto front mesh construction with a normal constraint mechanism, GNC structurally guarantees 100% coverage of the feasible Pareto region for any n-objective problem. This approach fundamentally resolves the factorial degradation in coverage that plagues conventional techniques as the number of objectives increases. Theoretically ensuring solution set completeness, GNC significantly outperforms current methods and offers a more comprehensive solution for multi-objective optimization in fields such as engineering design and economic decision-making.

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Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals

Jun 12, 2026

This study addresses emotion and stress recognition from multimodal physiological signals to enhance the performance of health monitoring and affective computing systems. Leveraging the WESAD dataset, the work proposes a two-stage fusion framework that integrates early signal fusion at the sensor level with multi-model ensembling at the prediction stage. Deep temporal models—including LSTM, TCN, and Transformer—are employed to jointly model wrist- and chest-based physiological signals. This approach substantially improves system robustness and generalization, achieving state-of-the-art performance with 98.91% accuracy and a macro F1-score of 98.56% in multimodal settings.

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