Electromagnetic Signatures: 

A Foundational Inquiry into Their Theoretical Underpinnings, Phenomenological Diversity, and Transformative Potential Across Scientific Frontiers and Global Systems

Prolegomenon: Situating Electromagnetic Signatures within the Nexus of Modern Science

The concept of the electromagnetic signature, far from being a mere descriptive label, constitutes a fundamental epistemological conduit through which the physical world is interrogated and understood. It resides at the intricate confluence of classical electrodynamics [Maxwell, 1865], quantum field theory [Dirac, 1927; Feynman, 1949], information science [Shannon, 1948], and applied technology, serving as the irreducible electromagnetic emanation—be it actively generated, passively reflected, or subtly altered—that encodes the intrinsic properties and dynamic states of matter and energy systems. As human scientific endeavor probes ever deeper into the fabric of reality, exemplified by advancements in areas from particle physics to observational cosmology, and engineers increasingly complex technological ecosystems, the rigorous comprehension, meticulous measurement, and sophisticated interpretation of electromagnetic signatures emerge not merely as advantageous, but as categorically indispensable. This treatise undertakes a profound examination of electromagnetic signatures, delving into their theoretical genesis within fundamental physical laws, exploring their diverse manifestations across the electromagnetic continuum, dissecting the advanced methodologies requisite for their detection and analysis, and critically evaluating their pervasive and often paradigm-shifting applications—from cosmophysical observation [Penzias & Wilson, 1965] and quantum metrology [Degen et al., 2017] to global security paradigms [Skolnik, 2001] and the future architectures of intelligent systems [Haykin, 2005]. We move beyond introductory exposition to engage with the core principles, inherent complexities, and ultimate potential of electromagnetic signatures to reshape scientific understanding and technological capability at the most fundamental levels.

Chapter 1: The Theoretical Edifice – Maxwellian Electrodynamics, Quantum Constraints, and the Genesis of Signatures

A truly deep understanding of electromagnetic signatures necessitates immersion in the foundational theoretical frameworks that govern their existence and behavior. At the macroscopic scale, the edifice rests upon Maxwell's Equations [Maxwell, 1865], the elegant quartet of coupled partial differential equations that achieved the grand unification of electricity, magnetism, and optics. These equations, expressed in differential or integral form, dictate the spatio-temporal evolution of the electric field (E) and magnetic field (B) vectors in response to charge densities (ρ) and current densities (J), while simultaneously revealing the inherent wave nature of electromagnetism propagating at the speed of light c in vacuum [Jackson, 1998].  

∇ ⋅ E = ρ/ε₀ ∇ ⋅ B = 0 ∇ × E = -∂B/∂t ∇ × B = μ₀J + μ₀ε₀∂E/∂t

Crucially, the interaction of these propagating electromagnetic waves with matter is governed by the constitutive relations, which encapsulate the material's response through its permittivity (ε), permeability (μ), and conductivity (σ). These parameters, often complex-valued, tensor quantities, and frequency-dependent (dispersive), dictate phenomena such as reflection, refraction, absorption, and scattering – the very processes that imbue an object or medium with its characteristic signature [Bohren & Huffman, 1983]. The Poynting vector (S = (1/μ₀)E × B) quantifies the directional energy flux of the electromagnetic field, essential for understanding power emission and reception [Poynting, 1884]. Furthermore, boundary conditions at material interfaces, derived directly from Maxwell's equations, rigorously define how fields transition between media, forming the mathematical basis for calculating scattering patterns and radar cross-sections (RCS) [Balanis, 2012].  

However, a complete picture demands consideration of the quantum realm. Quantum Electrodynamics (QED), the relativistic quantum field theory of electromagnetic interactions pioneered by Dirac, Feynman, Schwinger, and Tomonaga, provides the ultimate description [Dirac, 1927; Feynman, 1949; Schwinger, 1948]. Here, electromagnetic fields are quantized, with interactions mediated by the exchange of virtual and real photons. Phenomena like spontaneous emission [Weisskopf & Wigner, 1930], stimulated emission, and absorption, critical to signatures from atomic and molecular systems (e.g., spectroscopy, laser interactions [Schawlow & Townes, 1958]), are explicable only through QED. The quantization of the electromagnetic field also introduces fundamental noise limits (e.g., quantum shot noise [Schottky, 1918]) in detection systems, constraining the ultimate sensitivity achievable [Caves, 1981]. While full QED calculations are often intractable for macroscopic systems, semi-classical approaches (quantized matter, classical fields, or vice-versa) provide vital insights [Scully & Zubairy, 1997], particularly when dealing with coherent states (lasers) or strong field interactions.  

Chapter 2: The Electromagnetic Continuum – A Spectrum of Signature Modalities and Information Regimes

Electromagnetic signatures are not monolithic; they manifest with extraordinary diversity across the vast electromagnetic spectrum, each frequency regime offering unique informational content dictated by the scale and nature of the underlying physical interactions.

• Radio Frequency (RF) and Microwave Signatures (kHz to ~300 GHz): This regime is dominated by coherent processes, collective electron dynamics in conductors, and macroscopic structural interactions.
  
  

  • Radar Signatures: Defined by the Radar Cross-Section (RCS), a complex quantity dependent on frequency, polarization, aspect angle, and the object's geometry and material composition [Ruck et al., 1970]. Scattering physics here ranges from the Rayleigh regime (σ ∝ λ⁻⁴) through the resonant Mie regime [Mie, 1908] to the quasi-optical regime, each demanding different analytical or computational techniques (e.g., Method of Moments [Harrington, 1968], Finite Element Method [Jin, 2014], Physical Optics). Stealth technology fundamentally involves RCS reduction through shaping and radar-absorbing materials (RAMs) engineered via careful control of complex ε and μ [Knott et al., 1993].

  • Communication Signals (SIGINT): Intercepted transmissions reveal modulation schemes, protocols, network topologies, and potentially conveyed information content [Wiley, 2006]. Analysis involves sophisticated demodulation, decoding, and traffic analysis techniques, often leveraging cyclostationary feature detection [Gardner, 1991].

  • Passive RF/Microwave Radiometry: Exploits naturally emitted thermal radiation (described by the Rayleigh-Jeans approximation of Planck's law at these frequencies) or reflected ambient signals to image scenes or retrieve geophysical parameters like soil moisture and sea surface salinity [Ulaby et al., 1981].

• Terahertz (THz) Signatures (~0.1 to 10 THz): Bridging the "THz gap" between electronics and photonics [Tonouchi, 2007], this regime probes low-frequency vibrational modes (phonons), rotational transitions in molecules, and is highly sensitive to water content.
  
  

  • THz Spectroscopy and Imaging: Offers unique contrast mechanisms for non-destructive evaluation [Kawase, 2004], security screening (spectroscopic identification of explosives/drugs) [Federici et al., 2005], and biomedical imaging (e.g., mapping hydration states in tissues) [Pickwell & Wallace, 2006]. The development of efficient THz sources (e.g., quantum cascade lasers [Köhler et al., 2002]) and sensitive detectors remains a critical research frontier.  

  • 
    

• Infrared (IR) Signatures (~10 THz to 430 THz): Dominated by vibrational transitions in molecules and thermal blackbody radiation.
  
  

  • Thermal Signatures: Governed by Planck's law of blackbody radiation [Planck, 1901], the Stefan-Boltzmann law (total emitted power), and Wien's displacement law (peak wavelength). Surface emissivity (ε), often spectrally dependent, is crucial [Wolfe & Zissis, 1989]. IR signatures enable thermography, night vision, target detection, industrial process monitoring, and Earth observation (e.g., MODIS sensor data) [Justice et al., 1998]. Atmospheric transmission windows heavily influence sensor design [Hudson, 1969].

  • Vibrational Spectroscopy (FTIR): Absorption or reflection features correspond to specific molecular bonds, enabling chemical identification in remote sensing [Goetz et al., 1985], laboratory analysis, and process control.  

  • 
    

• Visible and Ultraviolet (UV) Signatures (~430 THz to 30 PHz): Involve electronic transitions in atoms and molecules.  

  • Optical Signatures: Reflection spectra (color), fluorescence, phosphorescence, Raman scattering [Raman & Krishnan, 1928] provide information on composition, surface texture, and physiological state (e.g., vegetation health via NDVI [Rouse et al., 1974]). LIDAR (Light Detection and Ranging) uses pulsed lasers for high-resolution topographical mapping and atmospheric profiling [Measures, 1984].  

  • 
    

  • UV Signatures: Associated with higher energy electronic transitions. Important in astronomy (hot stars, interstellar medium characterization) [Spitzer, 1978], sterilization, material science, and forensic analysis. Solar-blind UV detection focuses on signatures invisible against solar background radiation [Ben-Mubarak et al., 2011].  

  • 
    

• X-ray and Gamma Ray Signatures (> 30 PHz): Probe core electron transitions and nuclear processes.
  
  

  • X-ray Signatures: Generated via bremsstrahlung or characteristic inner-shell atomic transitions [Compton & Allison, 1935]. Used in medical imaging (radiography, CT), materials analysis (XRD, XRF), and security screening. Synchrotron radiation provides high-intensity, tunable X-ray sources [Winick & Doniach, 1980].  

  • 
    

  • Gamma Ray Signatures: Result from nuclear decay [Rutherford & Soddy, 1902], particle annihilation [Dirac, 1930], and extreme astrophysical events (e.g., gamma-ray bursts [Klebesadel et al., 1973]). Used in nuclear medicine (PET scans [Ter-Pogossian et al., 1975]), astrophysics (e.g., Compton Gamma Ray Observatory [Gehrels et al., 1993]), and nuclear material detection.

Chapter 3: Advanced Methodologies for Signature Interrogation and Interpretation

Extracting meaningful information from raw electromagnetic data necessitates a sophisticated arsenal of signal processing, statistical analysis, and computational intelligence techniques, often pushing the boundaries of these fields.  

• Spectral and Temporal Decomposition:
  
  

  • Fourier Analysis: The cornerstone for transforming time-domain signals into the frequency domain (spectra), revealing periodicities and frequency content via the Fast Fourier Transform (FFT) [Cooley & Tukey, 1965]. Indispensable for analyzing stationary signals and linear time-invariant systems.

  • Time-Frequency Analysis: Techniques like the Short-Time Fourier Transform (STFT) and, more powerfully, the Wavelet Transform [Daubechies, 1992; Mallat, 1989] provide simultaneous time and frequency localization. Wavelets offer multi-resolution analysis, adeptly capturing transient features and non-stationarities. Hilbert-Huang Transform (HHT) offers an empirical alternative for nonlinear and non-stationary data [Huang et al., 1998].  

  • 
    

• Statistical Signal Processing and Estimation:
  
  

  • Detection Theory: Rigorous frameworks like Neyman-Pearson criterion [Neyman & Pearson, 1933] and Bayesian inference [Jaynes, 2003] are fundamental for optimal decision-making in noise. Matched filtering remains a cornerstone for detecting known signals in additive white Gaussian noise [North, 1943].

  • Parameter Estimation: Techniques like Maximum Likelihood Estimation (MLE) [Fisher, 1922] and Bayesian estimators provide methods to determine key signature parameters. The Cramer-Rao Lower Bound (CRLB) provides a fundamental limit on the variance of any unbiased estimator [Cramér, 1946]. Kalman filtering [Kalman, 1960] and particle filtering [Gordon et al., 1993] are essential for tracking dynamic systems and non-Gaussian processes.  

  • 
    

• Computational Intelligence and Machine Learning:
  
  

  • Pattern Recognition and Classification: Algorithms like Support Vector Machines (SVM) [Cortes & Vapnik, 1995], and increasingly, Deep Learning (DL) architectures [LeCun et al., 2015] are employed for automatic signature classification and identification (e.g., Automatic Target Recognition - ATR [Novak et al., 1992], modulation recognition [Dobre et al., 2007]).  

  • 
    

  • Deep Learning Architectures: Convolutional Neural Networks (CNNs) [Krizhevsky et al., 2012] excel at processing spatial hierarchies in image-like data. Recurrent Neural Networks (RNNs), particularly LSTMs [Hochreiter & Schmidhuber, 1997], are suited for sequential data. Generative Adversarial Networks (GANs) [Goodfellow et al., 2014] offer potential for data augmentation and signature synthesis. Explainable AI (XAI) methods are critical for validation and trust [Ribeiro et al., 2016].  

  • 
    

  • Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) [Hotelling, 1933] and manifold learning [Tenenbaum et al., 2000] are vital for visualizing and processing high-dimensional signature data.  

  • 
    

• Computational Electromagnetics (CEM): Predictive modeling based on first principles. Techniques like Finite-Difference Time-Domain (FDTD) [Yee, 1966], Finite Element Method (FEM) [Silvester & Ferrari, 1996], and Method of Moments (MoM) [Harrington, 1968] solve Maxwell's equations numerically for complex systems, enabling predictive signature analysis and design optimization [Taflove & Hagness, 2005].  

Chapter 4: Manifestations and Ramifications Across Domains

The practical applications derived from the interrogation of electromagnetic signatures are profoundly transformative, reshaping capabilities across diverse scientific and societal domains.

• National Security, Defense, and Intelligence: A domain perennially shaped by EM signature technology [Schleher, 1999].
  
  

  • Intelligence, Surveillance, Reconnaissance (ISR): Advanced radar (SAR/ISAR) [Jakowatz et al., 1996], SIGINT systems, and MASINT rely heavily on signature exploitation.

  • Electronic Warfare (EW): A complex interplay of electronic attack, protection, and support, fundamentally reliant on understanding and manipulating EM signatures [Adamy, 2015].  

  • 
    

  • Stealth and Counter-Stealth: A dynamic field involving signature reduction techniques [Jenn, 2005] and advanced detection methods leveraging multi-physics or low-frequency approaches [Willis, 2005].

  • Cyber-Electromagnetic Activities (CEMA): Exploiting the EM emanations from digital devices (e.g., TEMPEST concerns [Anderson, 1995]) and the susceptibility of systems to EM interference or attack.  

  • 
    

• Earth Observation and Environmental Science: Providing synoptic, quantitative views of planetary systems [Elachi & van Zyl, 2006].
  
  

  • Remote Sensing: Utilizing sensors across the spectrum (e.g., Landsat program [NASA], Sentinel missions [ESA]) to monitor critical environmental variables [Campbell & Wynne, 2011]. InSAR measures surface deformation with millimeter precision [Massonnet & Feigl, 1998].  

  • 
    

  • Climate Science: Signature data (e.g., atmospheric absorption spectra, thermal emissions, scatterometry) provides crucial inputs and validation for climate models [Goody & Yung, 1989].

• Biomedical Engineering and Healthcare: Enabling non-invasive diagnostics and targeted therapeutics [Webb, 2007].
  
  

  • Medical Imaging: Foundational techniques like MRI [Lauterbur, 1973; Mansfield & Grannell, 1973], X-ray CT [Hounsfield, 1973], PET [Ter-Pogossian et al., 1975] rely directly on interpreting EM interactions.

  • Diagnostics: ECG/EEG/MEG provide windows into electrophysiological activity [Niedermeyer & Lopes da Silva, 2005]. Emerging EM techniques like EIT [Bayford, 2006], microwave imaging [Fear & Stuchly, 2000], and THz imaging [Pickwell & Wallace, 2006] show diagnostic promise.  

  • 
    

  • Therapeutics: Utilizing focused EM energy delivery, such as RF ablation [Goldberg et al., 2001].

• Astrophysics and Cosmology: The primary means of observing the distant universe [Kraus, 1986].
  
  

  • Multi-Wavelength Astronomy: Combining observations across the EM spectrum provides a comprehensive understanding of celestial objects and phenomena, from radio views of the CMB [Smoot et al., 1992] to gamma-ray bursts.

  • Spectroscopy: Detailed analysis of spectral lines remains the bedrock for determining cosmic composition, temperature, motion, and magnetic fields [Tennyson, 2005].  

  • 
    

• Materials Science and Non-Destructive Evaluation (NDE): Probing material structure and integrity [Shull, 2002].
  
  

  • Microwave, THz, and eddy current methods provide complementary techniques for detecting hidden flaws or characterizing material properties.  

  • 
    

Chapter 5: Horizon Scanning – Emerging Frontiers, Grand Challenges, and Future Trajectories

The field of electromagnetic signatures is poised for further revolutionary advances, driven by profound convergences in fundamental physics, materials science, computation, and engineering.

• Quantum Sensing and Metrology: Exploiting quantum coherence and entanglement promises sensitivities surpassing classical limits (the standard quantum limit) [Giovannetti et al., 2004; Degen et al., 2017]. Potential applications include quantum radar/illumination [Lloyd, 2008], atomic magnetometers [Budker & Romalis, 2007], and NV-center probes [Taylor et al., 2008].  

• 
  

• Metamaterials and Metasurfaces: Engineering subwavelength structures enables unprecedented control over electromagnetic waves [Engheta & Ziolkowski, 2006; Capasso, 2011], leading to possibilities in perfect absorption [Landy et al., 2008], invisibility cloaking [Schurig et al., 2006], and advanced antennas/sensors [Yu & Capasso, 2014].  

• 
  

• AI-Driven Signature Analysis and Synthesis: Machine learning, particularly deep learning, will move beyond classification to enable real-time adaptive sensing, physics-informed learning for signature prediction [Raissi et al., 2019], anomaly detection in complex EM environments, and generative modeling for signature manipulation.

• Ubiquitous Sensing and Networked Signatures: Dense networks of sensors, including passive RF sensing [Bocca et al., 2014], will create massive, multi-modal EM data streams, necessitating new paradigms for distributed information fusion and interpreting collective EM behavior.

• Fundamental Limits and Grand Challenges: Pushing detection towards quantum limits; developing scalable, high-fidelity CEM for multi-scale, multi-physics problems; mastering the increasingly complex spectral environment (coexistence and interference); ensuring the security, trustworthiness [Ribeiro et al., 2016], and ethical deployment of potent signature technologies.

Conclusion: Towards a Unified Science of Electromagnetic Information

The study of electromagnetic signatures transcends disciplinary boundaries, embodying a fundamental quest to decode the information encoded within electromagnetic fields across all scales of nature and technology. From the quantum fluctuations defining ultimate detection limits [Caves, 1981] to the cosmic signals traversing billions of light-years [Penzias & Wilson, 1965], these signatures constitute a universal language. The continued refinement of theoretical models [Jackson, 1998; Peskin & Schroeder, 1995], the development of radically new sensing modalities leveraging quantum physics [Degen et al., 2017] and engineered matter [Engheta & Ziolkowski, 2006], and the sophisticated application of computational intelligence [LeCun et al., 2015] will unlock currently unimaginable capabilities. Harnessing this potential—for scientific discovery, technological innovation, global security, and planetary stewardship—requires not only technical brilliance but also profound foresight and unwavering ethical consideration. The future trajectory points towards a truly unified science of electromagnetic information, where the ability to read, interpret, manipulate, and even write electromagnetic signatures will fundamentally shape the intellectual landscape and technological fabric of the 21st century and beyond.

Selected References:

• Adamy, D. L. (2015). EW 104: Electronic Warfare Against a New Generation of Threats. Artech House.

• Anderson, R. J. (1995). Security Engineering: A Guide to Building Dependable Distributed Systems (Chapter on Emissions Security/TEMPEST). Wiley. (Note: While a book, it covers the TEMPEST concept well).

• Balanis, C. A. (2012). Advanced Engineering Electromagnetics (2nd ed.). Wiley.  

• Bayford, R. H. (2006). Bioimpedance tomography: principles, developments and clinical applications. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1849), 3335-3344.  

• Ben-Mubarak, M., et al. (2011). Overview of Solar Blind UV Technology & Applications. Proc. SPIE 8031, Advanced Photon Counting Techniques V.

• Bocca, M., et al. (2014). Device-free localization: A survey. ACM Computing Surveys (CSUR), 47(1), 1-38.

• Bohren, C. F., & Huffman, D. R. (1983). Absorption and Scattering of Light by Small Particles. Wiley.  

• Budker, D., & Romalis, M. (2007). Optical magnetometry. Nature Physics, 3(4), 227-234.  

• Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing (5th ed.). Guilford Press.  

• Capasso, F. (2011). Metamaterials: A new route for manipulating light. Physics Today, 64(5), 28-33.

• Caves, C. M. (1981). Quantum-mechanical noise in an interferometer. Physical Review D, 23(8), 1693.

• Compton, A. H., & Allison, S. K. (1935). X-Rays in Theory and Experiment. Van Nostrand.  

• Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297-301.  

• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.  

• Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press.

• Daubechies, I. (1992). Ten Lectures on Wavelets. SIAM.

• Degen, C. L., Reinhard, F., & Cappellaro, P. (2017). Quantum sensing. Reviews of Modern Physics, 89(3), 035002.  

• Dirac, P. A. M. (1927). The Quantum Theory of the Emission and Absorption of Radiation. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 114(767), 243-265.  

• Dirac, P. A. M. (1930). A Theory of Electrons and Protons. Proceedings of the Royal Society A, 126(801), 360-365.

• Dobre, O. A., et al. (2007). Survey of automatic modulation classification techniques: classical approaches and new trends. IET Communications, 1(2), 137-156. 

• Elachi, C., & van Zyl, J. J. (2006). Introduction to The Physics and Techniques of Remote Sensing. Wiley.

• Engheta, N., & Ziolkowski, R. W. (Eds.). (2006). Metamaterials: Physics and Engineering Explorations. Wiley-IEEE Press.  

• Fear, E. C., & Stuchly, M. A. (2000). Microwave detection of breast cancer. IEEE Transactions on Microwave Theory and Techniques, 48(11), 1854-1863.  

• Federici, J. F., et al. (2005). THz imaging and sensing for security applications—explosives, weapons and drugs. Semiconductor Science and Technology, 20(7), S266.  

• Feynman, R. P. (1949). Space-Time Approach to Quantum Electrodynamics. Physical Review, 76(6), 769.  

• Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London. Series A, 222(594-604), 309-368.  

• Gardner, W. A. (1991). Exploitation of spectral correlation in signal detectors. Signal Processing, 23(3), 209-227.

• Gehrels, N., et al. (1993). The Compton Gamma Ray Observatory. Scientific American, 269(6), 68-77.

• Giovannetti, V., Lloyd, S., & Maccone, L. (2004). Quantum-enhanced measurements: beating the standard quantum limit. Science, 306(5700), 1330-1336.

• Goetz, A. F. H., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147-1153.  

• Goldberg, S. N., et al. (2001). Radio-frequency tissue ablation: importance of local temperature along the electrode tip exposure in determining lesion shape and size. Academic Radiology, 8(11), 1064-1074.  

• Goodfellow, I., et al. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.

• Goody, R. M., & Yung, Y. L. (1989). Atmospheric Radiation: Theoretical Basis (2nd ed.). Oxford University Press.  

• Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107-113.  

• Harrington, R. F. (1968). Field Computation by Moment Methods. Macmillan.

• Haykin, S. (2005). Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201-220.  

• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

• Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417.  

• Hounsfield, G. N. (1973). Computerized transverse axial scanning (tomography): Part 1. Description of system. The British Journal of Radiology, 46(552), 1016-1022.  

• Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995.  

• Hudson, R. D. (1969). Infrared System Engineering. Wiley.  

• Jackson, J. D. (1998). Classical Electrodynamics (3rd ed.). Wiley. 

• Jakowatz, C. V., et al. (1996). Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach. Springer Science & Business Media.

• Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge University Press.

• Jenn, D. C. (2005). Radar and Laser Cross Section Engineering (2nd ed.). AIAA.

• Jensen, J. R. (2006). Remote Sensing of the Environment: An Earth Resource Perspective (2nd ed.). Prentice Hall.

• Jin, J. M. (2014). The Finite Element Method in Electromagnetics (3rd ed.). Wiley-IEEE Press.

• Justice, C. O., et al. (1998). The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228-1249.  

• Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.  

• Kawase, K. (2004). Terahertz imaging for drug detection and large-scale integrated circuit inspection. Optics Express, 12(18), 4171-4180.  

• Klebesadel, R. W., Strong, I. B., & Olson, R. A. (1973). Observations of Gamma-Ray Bursts of Cosmic Origin. The Astrophysical Journal, 182, L85.  

• Knott, E. F., Shaeffer, J. F., & Tuley, M. T. (1993). Radar Cross Section (2nd ed.). Artech House.  

• Köhler, R., et al. (2002). Terahertz semiconductor-heterostructure laser. Nature, 417(6885), 156-159.

• Kraus, J. D. (1986). Radio Astronomy (2nd ed.). Cygnus-Quasar Books.  

• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25.  

• Landy, N. I., et al. (2008). Perfect metamaterial absorber. Physical Review Letters, 100(20), 207402.  

• Lauterbur, P. C. (1973). Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance. Nature, 242(5394), 190-191.  

• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.  

• Lloyd, S. (2008). Enhanced sensitivity of photodetection via quantum illumination. Science, 321(5895), 1463-1465.

• Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.  

• Mansfield, P., & Grannell, P. K. (1973). NMR ‘diffraction’ in solids? Journal of Physics C: Solid State Physics, 6(22), L422.  

• Massonnet, D., & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earth’s surface. Reviews of Geophysics, 36(4), 441-500.

• Maxwell, J. C. (1865). A Dynamical Theory of the Electromagnetic Field. Philosophical Transactions of the Royal Society of London, 155, 459-512.  

• Measures, R. M. (1984). Laser Remote Sensing: Fundamentals and Applications. Wiley.

• Mie, G. (1908). Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen. Annalen der Physik, 330(3), 377-445.  

• Neyman, J., & Pearson, E. S. (1933). On the Problem of the Most Efficient Tests of Statistical Hypotheses. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 231(694-706), 289-337.  

• Niedermeyer, E., & Lopes da Silva, F. (Eds.). (2005). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (5th ed.). Lippincott Williams & Wilkins.  

• North, D. O. (1943). An analysis of the factors which determine signal/noise discrimination in radar. RCA Laboratories Report PTR-6C. (Classic internal report, widely cited).

• Novak, L. M., et al. (1992). Effects of polarization and resolution on SAR ATR. IEEE Transactions on Aerospace and Electronic Systems, 28(4), 910-919.

• Pendry, J. B., Holden, A. J., Stewart, W. J., & Youngs, I. (1999). Extremely low frequency plasmons in metallic mesostructures. Physical Review Letters, 76(25), 4773.

• Penzias, A. A., & Wilson, R. W. (1965). A Measurement of Excess Antenna Temperature at 4080 Mc/s. The Astrophysical Journal, 142, 419-421.  

• Peskin, M. E., & Schroeder, D. V. (1995). An Introduction to Quantum Field Theory. Addison-Wesley.

• Pickwell, E., & Wallace, V. P. (2006). Biomedical applications of terahertz technology. Journal of Physics D: Applied Physics, 39(17), R301.  

• Planck, M. (1901). Ueber das Gesetz der Energieverteilung im Normalspectrum. Annalen der Physik, 309(4), 553-563.

• Poynting, J. H. (1884). On the Transfer of Energy in the Electromagnetic Field. Philosophical Transactions of the Royal Society of London, 175, 343-361.  

• Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686-707.  

• Raman, C. V., & Krishnan, K. S. (1928). A New Type of Secondary Radiation. Nature, 121(3048), 501-502.

• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.  

• Rouse Jr, J. W., et al. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309.  

• Ruck, G. T., Barrick, D. E., Stuart, W. D., & Krichbaum, C. K. (1970). Radar Cross Section Handbook (Vols. 1 & 2). Plenum Press.  

• Rutherford, E., & Soddy, F. (1902). The Cause and Nature of Radioactivity.—Part I. Philosophical Magazine Series 6, 4(21), 370-396.  

• Schawlow, A. L., & Townes, C. H. (1958). Infrared and Optical Masers. Physical Review, 112(6), 1940.  

• Schleher, D. C. (1999). Electronic Warfare in the Information Age. Artech House.

• Schottky, W. (1918). Über spontane Stromschwankungen in verschiedenen Elektrizitätsleitern. Annalen der Physik, 362(23), 541-567.  

• Schurig, D., et al. (2006). Metamaterial electromagnetic cloak at microwave frequencies. Science, 314(5801), 977-980.  

• Schwinger, J. (1948). On Quantum-Electrodynamics and the Magnetic Moment of the Electron. Physical Review, 73(4), 416.  

• Scully, M. O., & Zubairy, M. S. (1997). Quantum Optics. Cambridge University Press.  

• Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.  

• Shull, P. J. (2002). Nondestructive Evaluation: Theory, Techniques, and Applications. Marcel Dekker.

• Silvester, P. P., & Ferrari, R. L. (1996). Finite Elements for Electrical Engineers (3rd ed.). Cambridge University Press. 

• Skolnik, M. I. (2001). Introduction to Radar Systems (3rd ed.). McGraw-Hill.  

• Smith, D. R., Padilla, W. J., Vier, D. C., Nemat-Nasser, S. C., & Schultz, S. (2000). Composite medium with simultaneously negative permeability and permittivity. Physical Review Letters, 84(18), 4184.  

• Smoot, G. F., et al. (1992). Structure in the COBE differential microwave radiometer first-year maps. The Astrophysical Journal, 396, L1-L5.  

• Spitzer, L. (1978). Physical Processes in the Interstellar Medium. Wiley.

• Taflove, A., & Hagness, S. C. (2005). Computational Electrodynamics: The Finite-Difference Time-Domain Method (3rd ed.). Artech House.  

• Taylor, J. M., et al. (2008). High-sensitivity diamond magnetometer with nanoscale resolution. Nature Physics, 4(10), 810-816.

• Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319-2323.  

• Tennyson, J. (2005). Astronomical Spectroscopy: An Introduction to the Atomic and Molecular Physics of Astronomical Spectra. Imperial College Press. 

• Ter-Pogossian, M. M., et al. (1975). A positron-emission transaxial tomograph for nuclear imaging (PETT). Radiology, 114(1), 89-98.  

• Tonouchi, M. (2007). Cutting-edge terahertz technology. Nature Photonics, 1(2), 97-105.

• Ulaby, F. T., Moore, R. K., & Fung, A. K. (1981). Microwave Remote Sensing: Active and Passive (Vol. 1). Artech House.  

• Webb, A. (2007). Introduction to Biomedical Imaging. Wiley-IEEE Press.

• Weisskopf, V., & Wigner, E. (1930). Berechnung der natürlichen Linienbreite auf Grund der Diracschen Lichttheorie. Zeitschrift für Physik, 63(1-2), 54-73.

• Wiley, R. G. (2006). ELINT: The Interception and Analysis of Radar Signals. Artech House.

• Willis, N. J. (2005). Bistatic Radar. SciTech Publishing.

• Winick, H., & Doniach, S. (Eds.). (1980). Synchrotron Radiation Research. Plenum Press.

• Wolfe, W. L., & Zissis, G. J. (Eds.). (1989). The Infrared Handbook (Revised ed.). Environmental Research Institute of Michigan.

• Yee, K. (1966). Numerical solution of initial boundary value problems involving Maxwell's equations in isotropic media. IEEE Transactions on Antennas and Propagation, 14(3), 302-307.  

• Yu, N., & Capasso, F. (2014). Flat optics with designer metasurfaces. Nature Materials, 13(2), 139-150.