Abstract: Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards.

Abstract: Present disclosure relates to a method of diagnosing malaria infection in a patient by Surface Enhanced Raman Spectroscopy (SERS). The method includes obtaining a sample from said patient, mixing the sample with a suspension of magnetic nanoparticles, wherein said magnetic nanoparticles adsorb hemozoin present in the sample onto their surface, obtaining the SERS spectra of the sample, and correlating the obtained SERS spectra to the presence or amount of hemozoin in the sample, wherein the presence of hemozoin is indicative of malaria infection.

Abstract: Present disclosure relates to a method of diagnosing malaria infection in a patient by Surface Enhanced Raman Spectroscopy (SERS). The method includes obtaining a sample from said patient, mixing the sample with a suspension of magnetic nanoparticles, wherein said magnetic nanoparticles adsorb hemozoin present in the sample onto their surface, obtaining the SERS spectra of the sample, and correlating the obtained SERS spectra to the presence or amount of hemozoin in the sample, wherein the presence of hemozoin is indicative of malaria infection.