2013/07/31 by Mohammad H. Asghari, Bahram Jalali · 53 citations
Computer Science · Engineering · Physics and Astronomy · #Analog signal #Analog transmission #Bandwidth (computing) #Blind Source Separation Techniques #Compression ratio #Computer hardware #Computer science #Computer vision #Data compression #Data transmission #Digital signal processing #Electronic engineering #Engineering #Image and Signal Denoising Methods #Lossless compression #Neural Networks and Reservoir Computing #Optics #Physics #Signal compression #Signal processing #Telecommunications #Time domain #physics.data-an #physics.ins-det #physics.optics
paper · pdf · doi:10.1364/ao.52.006735
published in Applied Optics 52(27), 6735 (Optica Publishing Group) · M. H. Asghari and B. Jalali, "Anamorphic transformation and its application to time-bandwidth compression," submitted on May 28th, 2013 to Applied Optics journal- Accepted. To be presented at IEEE Photonic Conference, September 2013 and in IEEE GlobalSIP, December 2013
arxiv created 2013/08/19 · openalex publication_date 2013/09/12 · arxiv updated 2015/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A general method for compressing the modulation time-bandwidth product of analog signals is introduced. As one of its applications, this physics-based signal grooming, performed in the analog domain, allows a conventional digitizer to sample and digitize the analog signal with variable resolution. The net result is that frequency components that were beyond the digitizer bandwidth can now be captured and, at the same time, the total digital data size is reduced. This compression is lossless and is achieved through a feature selective reshaping of the signal's complex field, performed in the analog domain prior to sampling. Our method is inspired by operation of Fovea centralis in the human eye and by anamorphic transformation in visual arts. The proposed transform can also be performed in the digital domain as a data compression algorithm to alleviate the storage and transmission bottlenecks associated with "big data."