Abstract-In this paper, we investigate optimal load control in industrial sector, which involves several new and distinct research problems. For example, while most residential appliances operate independently, industrial units are highly interdependent and must follow certain operational sequences. Also, unlike residential appliances, the operation of industrial units may span across multiple days and involve multiple batch cycles. Furthermore, in industries with process control, energy management is often coupled with material flow management. The design in this paper is comprehensive and addresses industrial load control under various smart electricity pricing scenarios, including day-ahead pricing, time-of-use pricing, peak pricing (PP), inclining block rates, and critical PP. The use of behind-the-meter renewable generator and energy storage is also considered. The formulated optimization problem is a tractable mixed-integer linear program. Different case studies are presented based on a practical energy-extensive steel mill industry model. Index Terms-Batch processes, demand side management, industrial load control (ILC), optimal energy scheduling, smart pricing.

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Radio self-interference cancellation has been a technological challenge for more than a century while it is the most critical enabler for full-duplex radios. The eventual success of radio self-interference cancellation may well depend on not only improved hardware technology but also innovative signal processing schemes. In this paper, we present a few latest discoveries on such schemes. The first is an improvement of time-domain transmit beamforming with robustness against the IQ imbalances in radio circuits, which is supported by both simulation and hardware experimental results. A key innovation here is due to the use of real-valued linear model instead of complex-valued linear (or widely linear) model. The second is a numerical investigation of the performance limits of an allanalog cancellation channel based on clustered-taps of attenuators when the interference channel has a large number of random multipaths. The third is a blind digital tuning method which uses only the baseband waveforms to determine the values of the variable attenuators embedded in the all-analog cancellation channel. This method is robust against imperfections in the knowledge of the transfer function of any component in the system provided that a real-valued linearity property holds (except for the transmit chain).

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Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Soil Moisture and Ocean Salinity (SMOS), Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.