Graduate students are involved in all of our research activities and have access to state-of-the-art facilities, such as the electromagnetic anechoic chamber, the wireless communications lab and the integrated circuit fabrication cleanroom. Some programs offer students theoretical approaches for the design of communication systems, while others include the implementation of wireless communication devices, such as the circuits in cell phones.
Analog communication systems by p chakrabarti pdf
These research efforts align with national and international efforts to address worldwide challenges in energy production and distribution, communications technology and information management, health care technology and delivery, sustainable development and economic growth and global security.
The School of Electrical, Computer and Energy Engineering offers instruction in the related areas of signal processing and communications systems at the graduate level. Courses are also offered for beginning graduate students in the field to bridge any gaps that might exist between undergraduate course work and the 500-level offerings at ASU.
The text book on Optical Fiber Communication describes the optical fiber with its low-loss and highbandwidth characteristics which has the potential to provide enormous capacity of transmitted data as compared to electronic means. This book will describe the fundamental operation and recent advances in the exciting area of optical fiber communication systems.
The text book on Optical Fiber Communication describes the optical fiber with its low-loss and high bandwidth characteristics which has the potential to provide enormous capacity of transmitted data as compared to electronic means. This book will describe the fundamental operation and recent advances in the exciting area of optical fiber communication systems.
Several potential advantages of the proposed smart WPT system with intelligent metasurface are remarked here. Firstly, the smart WPT system can be guaranteed to human exposure under the level of EM safety. For instance, the smart WPT system can be designed to be able to instantly shut off the power delivering when detecting a person moving close or falling into the charging region, and resume as the human leaves. Secondly, the smart WPT system can be optimized as simultaneous wireless information and power transfer system, when it is integrated with wireless sensor networks, communication modules and advanced algorithms. Thirdly, the WPT can be further extended to meet various needs such as automatic charging, monitoring, and microwave hyperthermia. In a word, the proposed WPT strategy could open a new avenue for the WPT with the high efficiency, safety, and intelligence.
NMMBCs work similar to the conventional wireless communication systems, but with the use of intelligent metasurfaces for recycling the energy dissipated in space that was conventionally thought to be useless and further improving SNR. Similar to conventional wireless communication systems, in NMMBC, an intended RF carrier carrying the information to be transferred is required for information transfer, where the signal modulation or demodulation is made by using nonlinear RF mixers. However, as opposed to the conventional wireless system, in NMMBC, the intelligent metasurface is deployed to shape the ambient environment such that the effective number of information channels can be increased (see Fig. 3a, b). In NMMBC, the intelligent metasurface is utilized to extend the aperture of the antenna in the conventional wireless communication systems in a distributed manner. In other words, the intelligent metasurface can be regarded as an extension part of antenna arrays of the conventional wireless communication systems, which is connected with the intended RF source using air rather than transmission lines [177].
Besides, the intelligent metasurface has several ubiquitous properties. First, the intelligent metasurface can be optimized to match any RF source and associated modules, since it improves the communication performance by tailoring the surrounding environment for all nearby devices instead of modifying the transmitting and receiving devices. Second, unlike the transmission lines in the conventional communication systems, the intelligent metasurface does not involve high-speed signals [177], and thus it can be easily incorporated into the ambient environment and remarkably improve SNR and thus the information capacity of the conventional systems. For instance, Tang et al. demonstrated theoretically that the intelligent metasurfaces were helpful in improving the energy efficiency of power allocation of the base station [177]. Hougne et al. demonstrated that the one-bit reconfigurable metasurface can be optimized to improve remarkably the equivalent number of channels of MIMO wireless communication systems [167]. More recently, in the community of wireless communication, the RIS has been numerically demonstrated to be helpful in enhancing the secure transfer [173, 174] (see Fig. 3c), reducing the mobile edge computing [175, 176] (see Fig. 3d), and so on. Overall, there are rapidly growing interests in this topic, and we would like to refer the readers of interest to Refs. [170,171,172] for more comprehensive reviews about recent progress.
We consider the utilization of linear embedding techniques in intelligent metasurface sensors [122]. As explored in Sect. 2.4, the intelligent metasurface is capable of generating nearly arbitrarily radiation patterns or the measurement modes desired by the machine learning techniques. Inspired by this, we proposed the concept of a machine-learning reprogrammable imager (see Fig. 4b), in which the intelligent metasurface is trained with a vast number of training data using the PCA such that the machine-learning-desired radiation patterns can be achieved on the physical level. Then, the intelligent metasurface serves as a physical computing device: which outputs the low-dimensional PCA features from the input of the high-dimensional raw data in an analog computing way. As such, the resultant sensing strategy is almost free of digital computation.
The linear-machine-learning-driven metasurface imager relies on the assumption of linear mapping from the data to results, which to some extent limits itself to handle relatively simple sensing tasks. It is believed that the deep networks have much more powerful representation capability than shallow networks do, let alone linear networks [96]. Recently, we have witnessed rapid progress in all-wave (specifically, all-optical) physical deep networks that are optimized to match the modern deep acritical networks in optics [194]. However, one of the remaining challenges is the difficulty of the physical implementation of the nonlinear activation functions, although nonlinear materials (e.g. crystals, polymers, semiconductor materials) are available. Thus, we considered the powerful capability of deep learning in the digital world, and proposed the intelligent sensing scheme by exploring the hybrid computing scheme [129, 220]: the analog high-dimensional data preprocessing (e.g., data compression) with the intelligent metasurface on the physical level, and the digital postprocessing with the modern deep acritical neural networks on the digital level. Note that the compressive-sensing-inspired computational metasurface sensors [207,208,209,210,211, 221] can be treated as hybrid-computing-based intelligent sensing, in the sense that the data compression is accomplished on the metasurface level, and the sparsity-aware data processing is implemented on the digital level.
Entropy in information theory is directly analogous to the entropy in statistical thermodynamics. The analogy results when the values of the random variable designate energies of microstates, so Gibbs formula for the entropy is formally identical to Shannon's formula. Entropy has relevance to other areas of mathematics such as combinatorics and machine learning. The definition can be derived from a set of axioms establishing that entropy should be a measure of how "surprising" the average outcome of a variable is. For a continuous random variable, differential entropy is analogous to entropy. 2ff7e9595c
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