Target recognition and localization in communication systems present significant challenges. Often, computationally expensive algorithms that utilize the entire sensor array are necessary. In this letter, we propose a novel methodology that leverages spectral factorization to address these challenges. By employing the Nystrom method on a randomly selected subset of array sensor outputs, we construct an advanced fast-modified root-MUSIC algorithm. This approach enables the efficient detection and localization of multiple narrowband far-field sources with enhanced resolution and reduced computational complexity. Through extensive computer simulations, we demonstrate that our proposed method achieves estimation performance comparable to traditional root-MUSIC techniques while utilizing only one-fourth of the sensor array outputs. By integrating spectral factorization and the Nystrom method, our methodology effectively mitigates computational overhead, providing researchers with a compelling solution for precise target detection and localization in communication systems.

Carbon dots (CDs) are neoteric forms of carbon nanostructures, and play a fundamental role in early diagnosis and controlling of neurological disorders (NDs).

Low-Dimensional Nanohybrids (LDNHs) have emerged as potent multifunctional platforms for neurosensing and neuromodulation, providing elevated spatial-temporal precision, versatility, and biocompatibility. This review examines the intersection of LDNHs with artificial intelligence, brain–computer interfaces (BCIs), and closed-loop neurotechnologies, highlighting their transformative potential in personalized neuro-nano-medicine. Utilizing stimuli-responsive characteristics, optical, thermal, magnetic, and electrochemical LDNHs provide real-time feedback-controlled manipulation of brain circuits. Their pliable and adaptable structures surpass the constraints of inflexible bioelectronics, improving the neuronal interface and reducing tissue damage. We also examined their use in less invasive neurological diagnostics, targeted therapy, and adaptive intervention systems. This review delineates recent breakthroughs, integration methodologies, and fundamental mechanisms, while addressing significant challenges such as long-term biocompatibility, deep-tissue accessibility, and scalable manufacturing. A strategic plan is provided to direct future research toward clinical use. Ultimately, LDNHs signify a transformative advancement in intelligent, tailored, and closed-loop neurotechnologies, integrating materials science, neurology, and artificial intelligence to facilitate the next era of precision medicine.