Lensless complex amplitude demodulation based on deep learning in holographic data storage

To increase the storage capacity in holographic data storage (HDS), the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS. In this study, we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase. By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks (CNNs) to demodulate amplitude and phase respectively. The experimental system is simple, stable, and robust, and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase. To our investigation, this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations

Studies on High-Speed and Large-Capacity Data Transfer of Holographic Data Storage

早大学位記番号:新9092早稲田大

Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G

To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient baseband processors. Traditional complementary metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges in transistor scaling and the von Neumann bottleneck. To address these challenges, in-memory computing-based baseband processors using resistive random-access memory (RRAM) present an attractive solution. In this paper, we propose and demonstrate RRAM-implemented in-memory baseband processing for the widely adopted multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key operations, including discrete Fourier transform (DFT) and MIMO detection using linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step. In addition, RRAM-based channel estimation module is proposed and discussed. By prototyping and simulations, we demonstrate the feasibility of RRAM-based full-fledged communication system in hardware, and reveal it can outperform state-of-the-art baseband processors with a gain of 91.2$\times$ in latency and 671$\times$ in energy efficiency by large-scale simulations. Our results pave a potential pathway for RRAM-based in-memory computing to be implemented in the era of the sixth generation (6G) mobile communications.Comment: arXiv admin note: text overlap with arXiv:2205.0356

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review

Single-pixel, single-photon three-dimensional imaging

The 3D recovery of a scene is a crucial task with many real-life applications such as self-driving vehicles, X-ray tomography and virtual reality. The recent development of time-resolving detectors sensible to single photons allowed the recovery of the 3D information at high frame rate with unprecedented capabilities. Combined with a timing system, single-photon sensitive detectors allow the 3D image recovery by measuring the Time-of-Flight (ToF) of the photons scattered back by the scene with a millimetre depth resolution. Current ToF 3D imaging techniques rely on scanning detection systems or multi-pixel sensor. Here, we discuss an approach to simplify the hardware complexity of the current 3D imaging ToF techniques using a single-pixel, single-photon sensitive detector and computational imaging algorithms. The 3D imaging approaches discussed in this thesis do not require mechanical moving parts as in standard Lidar systems. The single-pixel detector allows to reduce the pixel complexity to a single unit and offers several advantages in terms of size, flexibility, wavelength range and cost. The experimental results demonstrate the 3D image recovery of hidden scenes with a subsecond acquisition time, allowing also non-line-of-sight scenes 3D recovery in real-time. We also introduce the concept of intelligent Lidar, a 3D imaging paradigm based uniquely on the temporal trace of the return photons and a data-driven 3D retrieval algorithm

MgCeAl11O19:Tb3+ and Mg8Ge2O11F2:Mn4+ in enhancing the color quality of remote phosphor LED

As the name infers, the triple-layer remote phosphor (TRP) has 3 phosphor layers includes the red Mg8Ge2O11F2:Mn4+ phosphor layer on the top, the green MgCeAl11O19:Tb3+ phosphor layer in the middle, and the yellow YAG:Ce3+ layer at the bottom and is mentioned as a solution to increase the chromaticity and luminescence adequacy of the white LEDs (WLEDs) in this article. As to control the red light for higher value achieve in the color rendering index (CRI), using red Mg8Ge2O11F2:Mn4+ phosphor in the TRP structure is recommended. All the outcomes indicate that when red phosphor Mg8Ge2O11F2:Mn4+ concentration grows the CRI gets higher values, and drastically declines when the concentration of green phosphor MgCeAl11O19:Tb3+ increases. As the same time, applying the green MgCeAl11O19:Tb3+ phosphor layer to manage the green light as it can make the luminous efficacy (LE) of WLEDs increase. In particular, the index of LE can also be improved over 40% by limiting the scatter of light and putting in green light. Moreover, to preserve the average correlated color temperature (ACCT) stable at 8500K, the yellow YAG:Ce3+ concentration must be cut down as the concentration of red and green phosphor rise

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc

6G—Enabling the New Smart City: A Survey

Smart cities and 6G are technological areas that have the potential to transform the way we live and work in the years to come. Until this transformation comes into place, there is the need, underlined by research and market studies, for a critical reassessment of the entire wireless communication sector for smart cities, which should include the IoT infrastructure, economic factors that could improve their adoption rate, and strategies that enable smart city operations. Therefore, from a technical point of view, a series of stringent issues, such as interoperability, data privacy, security, the digital divide, and implementation issues have to be addressed. Notably, to concentrate the scrutiny on smart cities and the forthcoming influence of 6G, the groundwork laid by the current 5G, with its multifaceted role and inherent limitations within the domain of smart cities, is embraced as a foundational standpoint. This examination culminates in a panoramic exposition, extending beyond the mere delineation of the 6G standard toward the unveiling of the extensive gamut of potential applications that this emergent standard promises to introduce to the smart cities arena. This paper provides an update on the SC ecosystem around the novel paradigm of 6G, aggregating a series of enabling technologies accompanied by the descriptions of their roles and specific employment schemes

Low-cost portable microscopy systems for biomedical imaging and healthcare applications

In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas.In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas