Piya Pal - US grants

This project develops advanced cyber-physical sensing, modeling, control, and optimization methods to significantly improve the efficiency of algal biomass production using membrane bioreactor technologies for waste water processing and algal biofuel. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Large-scale algae production represents one of the most promising and attractive solutions for simultaneous wastewater treatment and biofuel production. The critical bottleneck is low algae productivity and high biofuel production cost.

The previous work of this research team has successfully developed an algae membrane bioreactor (A-MBR) technology for high-density algae production which doubles the productivity in an indoor bench-scale environment. The goal of this project is to explore advanced cyber-physical sensing, modeling, control, and optimization methods and co-design of the A-MBR system to bring the new algae production technology into the field. The specific goal is to increase the algal biomass productivity in current practice by three times in the field environment while minimizing land, capital, and operating costs. Specifically, the project will (1) adapt the A-MBR design to address unique new challenges for algae cultivation in field environments, (2) develop a multi-modality sensor network for real-time in-situ monitoring of key environmental variables for algae growth, (3) develop data-driven knowledge-based kinetic models for algae growth and automated methods for model calibration and verification using the real-time sensor network data, and (4) deploy the proposed CPS system and technologies in the field for performance evaluations and demonstrate its potentials.

This project will demonstrate a new pathway toward green and sustainable algae cultivation and biofuel production using wastewater, addressing two important challenging issues faced by our nation and the world: wastewater treatment and renewable energy. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving K-12 students, women and minority students. With web-based access and control, this project will convert the bench-scale and pilot scale algae cultivation systems into an exciting interactive online learning platform to educate undergraduate and high-school students about cyber-physical system design, process control, and renewable biofuel production.

Technological advances have driven modern sensing systems towards generating massive amounts of data, making it increasingly challenging to store, transmit and process such data in a cost effective and reliable manner. However, the ultimate goal in many information-processing tasks is to infer some parameters of interest, that govern the statistical and physical model of the data. This includes applications ranging from source localization in radar and imaging systems to inferring latent variables in machine learning. The number of parameters in such problems is much smaller than the acquired volume of data, which leads to the possibility of more intelligent ways of sensing high dimensional signals, that can exploit the statistical model of the signal (with or without invoking sparsity), and the physics of the problem. The objective of this project is to develop a systematic theory of smart sampling and information retrieval algorithms for modern sensing systems that exploit the correlation structure of high dimensional signals to significantly reduce the number of measurements needed for inference. The proposed research can lead to deployment of fewer sensors (than what is traditionally required), as well as more energy efficient ways to collect and process spatio-temporal data that will positively impact a number of applications across disciplines, such as, high resolution imaging, remote sensing, neural signal processing and wireless communication. The educational component of this project aims at integrating the research outcomes into innovative teaching platforms such as ''Sense Smarter'', and ''Signals Everywhere'' that will help train the next generation of electrical engineers, and encourage them to pursue careers in STEM fields.

The technical component of the project has three interconnected goals: (i) designing fundamentally new geometries for correlation-aware samplers that exploit the statistical as well as physical signal models, (ii) developing, and analyzing the performance of new correlation driven algorithms to understand fundamental capabilities of correlation-aware samplers, and (iii) exploiting the ideas behind correlation-aware samplers to develop more efficient algorithms for solving bi- and multi-linear problems. Design of these samplers will provide new theoretical insights into properties of quadratic samplers, and will help address fundamental mathematical questions that can be of independent interest. The samplers also facilitate the development of new inference strategies, and the proposed rigorous theoretical analysis of these algorithms is expected to fundamentally advance our current understanding of the limits of parameter estimation from compressed data. Finally, the ideas behind correlation-aware samplers have strong connections with problems in machine learning such as dictionary learning, and latent variable analysis, and they will foster future research advances in these areas.

Understanding the structural and functional components of the brain that underlie perception, cognition and action, is crucial for developing next generation neural prostheses, brain machine interfaces, and discovering preventive measures against neurological disorders. Optical technologies have enabled us to record and infer neural activity with single-cell resolution. However, they are limited by low temporal resolution, and often fail to accurately capture the neural dynamics at the milli-second time scales. Electrophysiology, on the other hand, provides higher temporal resolution, but single-cell electrophysiology usually suffers from low throughput, and recordings that cover larger spatial scales suffer from poor spatial resolution, making it difficult to decipher neural activity at cellular scale from large areas. Realizing that micro-scale optical imaging and macro-scale electrophysiological recording possess complementary strengths in terms of spatial and temporal resolution, this multidisciplinary project will combine the two recording modalities using innovations in neural engineering, multi-modal imaging and signal processing, to understand neural activity at previously unattained temporal and spatial resolution. Such a capability will lead to new discoveries on information processing in the brain and circuit dysfunctions for neurological disorders (epilepsy, depression, memory disorders, etc.), affecting one billion people worldwide. Recording and resolving neural activity with enhanced resolution can drive the development of next-generation of brain computer interfaces for restoring vision, hearing, and movement. The outcomes of this project will also be integrated into developing interdisciplinary educational materials for training the next generation of neuroengineers, neuroscientists and signal processing experts. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

The project has three main technical components that consist of development of novel electrode arrays, careful design of multi-modal imaging experiments, and advanced signal processing techniques for solving ill-posed inverse problems. Simultaneous multiphoton imaging and electrophysiology experiments enabled by novel electrode arrays will generate brand new datasets which will be processed by new data-driven super-resolution algorithms that judiciously exploit the complementary strengths of the two imaging modalities. The key idea is to cast the fusion problem within the mathematical framework of bilinear problems, and exploit sparsity of the underlying neural activity as a key ingredient in solving the inverse problem by fusing the datasets obtained from optical and electrophysiological recordings. The mathematical principles and algorithms used for creating super-resolution images by fusing signals with complementary attributes have broader applicability beyond neural imaging, and can be used for developing more efficient solutions for ill-posed inverse problems that arise in diverse imaging applications.

The project addresses the challenges of next-generation wireless communication systems. A key enabling technology for reliable and high-data-rate communication is the deployment of Multiple Input Multiple Output (MIMO) systems, which consist of multiple antennas for transmission and reception. With the use of the millimeter-wave (mmWave) frequencies in next-generation systems, the shorter wavelength enables deployment of many antennas in a small physical area, leading to massive MIMO systems. Massive MIMO systems, however, tend to have high complexity, high power consumption and high cost. This project seeks to do more with less: “Less” refers to limited hardware (fewer radio-frequency chains, one-bit analog to digital converters, etc.) and “more” to being able to extract the benefits (with minimal degradation) of massive MIMO systems by working around these hardware limitations. To do more with less, the project adopts a synergistic approach where innovations in system architecture and algorithms (model-based and data-driven) complement each other via judicious exploitation of structure (antenna array geometry and modeling) aided by powerful inference frameworks (sparse Bayesian learning and machine-learning techniques). The project will lead to state-of-the-art wireless communication systems that should help with maintaining US leadership in this important technology as well to train the next generation of researchers in this area of strategic importance.

To develop low-complexity, low-cost, next-generation mmWave massive MIMO systems, this project has two major components. One is to harness antenna array geometry, both for one-dimensional and two-dimensional arrays, for rich channel sensing with fewer sensors complemented by robust inference. A key aspect of this work is embedding a nested array into a massive MIMO architecture employing fewer radio-frequency chains. The rich sensing capability of the nested array is being maximally exploited using the sparse Bayesian learning method. The channel sensing is also complemented with enhanced channel models incorporating variable and unknown angular spreads. A further component is the use of learning through deep neural networks to compensate for nonlinearities introduced to reduce power and cost. Models complemented by learning as well as fully data-driven techniques are being developed that address the specific and unique needs of wireless systems, such as variable numbers of users and channel coherence.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.