IU Indianapolis ScholarWorks :: Browsing by Author "Schubert, Peter"

Browsing by Author "Schubert, Peter"

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A Theoretical Study on Porous-Silicon Based Synapse Design for Neural Hardware
(IEEE, 2021-12) Sikder, Orthi; Schubert, Peter; Electrical and Computer Engineering, School of Engineering and Technology
Porous silicon (po-Si) is a form of silicon (Si) with nanopores of tunable sizes and shapes distributed over the bulk structure. Although crystalline Si (c-Si) is already established as one of the most advantageous and promising elements for its technological significance, the additional key aspect of po-Si is its large surface area with respect to its small volume which is beneficial for surface chemistry. In this work, we explore the design of a po-Si based synaptic device and investigate its potential for neuromorphic hardware. First, we analyze several electrical properties of po-Si through density functional theory (Ab Initio/ first principle) calculation. We show that the presence of intra-pore dangling states appears within the bandgap region of po-Si. While the bandgap of the po-Si is well known to be higher than c-Si yielding low carrier density and higher resistance, the appearance of these dangling states can significantly participate in electronic transport through hopping mechanism. Then, we analyze the electric-field driven modulation in the dangling bond through controlled intra-pore Si-H bond dissociation. Such modulation of the dangling state density further allows the tenability of the po-Si conductance. Finally, we theoretically evaluate the current-voltage characteristics of our proposed po-Si based synaptic devices and determine the possible range of obtainable conductivity for different porosity. Our analysis signifies that the integration of such devices in the synaptic fabric can enable significantly denser and energy-efficient neuromorphic hardware.
Density Functional Theory (DFT) study of hydrogen storage in porous silicon
(2018) Boaks, Mawla; Schubert, Peter
Based on plane wave DFT calculation, we carried out micro level investigation of hydrogen storage in nanoporous silicon (npSi). One quarter of a hexagonal pore with Palladium catalyst placed at the surface has been studied for hydrogen dissociation, spillover, bond hopping, and diffusion for both single catalyst atom and small catalyst cluster consisting of multiple catalyst atoms. All the DFT computations were done in one of the biggest research supercomputer facilities of the world, Big Red II. We opted ABINIT, an open source DFT tool for our computations. Our calculation revealed low dissociation, spillover, and bond hoping energy barrier. The energy required to be provided from external sources to fully recharge the storage medium from a gaseous source at a completely empty state has also been evaluated. Hydrogen diffusion along the inner surface of the pore as a means of bond hopping and the possibility of quantum tunneling, a low temperature phenomena used to spontaneously go over an otherwise less likely high energy barrier have been studied as well. Using these micro level parameter values evaluated from the DFT study, the performance of any potential hydrogen storage material can be compared to a set of characteristics sought in an efficient storage media. Thus, the micro scale feasibility of this novel npSi material based hydrogen storage technology was studied as a part of a STTR Phase I project.
Developing a PV and Energy Storage Sizing Methodology for Off-Grid Communities
(2018-12) Vance, David M.; Razban, Ali; Weissbach, Robert; Schubert, Peter
Combining rooftop solar with energy storage for off-grid residential operation is restrictively expensive. Historically, operating off-grid requires an 'isolated self-consumption' operating strategy where any excess generation is wasted and to ensure reliability you must install costly, polluting generators or a large amount of energy storage. With the advent of Blockchain technology residents can come together and establish transactive microgrids which have two possible operating strategies: Centralized Energy Sharing (CES) and Interconnected Energy Sharing (IES). The CES strategy proposes that all systems combine their photovoltaic (PV) generation and energy storage systems (ESS) to meet their loads. IES strategy establishes an energy trading system between stand-alone systems which allows buying energy when battery capacity is empty and selling energy when battery capacity is full. Transactive microgrids have been investigated analytically by several sources, none of which consider year-round off-grid operation. A simulation tool was developed through MATLAB for comparing the three operating strategies: isolated self-consumption, CES, and IES. This simulation tool could easily be incorporated into existing software such as HOMER. The effect of several variables on total cost was tested including interconnection type, initial charge, load variability, starting month, number of stand-alone systems, geographic location, and required reliability. It was found that the CES strategy improves initial cost by 7\% to 10\% compared to the baseline (isolated self-consumption) and IES cases in every simulation. The IES case consistently saved money compared to the baseline, just by a very small amount (less than 1\%). Initial charge was investigated for March, July, and November and was only found to have an effect in November. More research should be done to show the effect of initial charge for every month of the year. Load variability had inconsistent results between the two geographic locations studied, Indianapolis and San Antonio. This result would be improved with an improved load simulation which includes peak shifting. The number of systems did not have a demonstrable effect, giving the same cost whether there were 2 systems or 50 involved in the trading strategies. It may be that only one other system is necessary to receive the benefits from a transactive microgrid. Geographic locations studied (Indianapolis, Indiana; Phoenix, Arizona; Little Rock, Arkansas; and Erie, Pennsylvania) showed a large effect on the total cost with Phoenix being considerably cheaper than any other location and Erie having the highest cost. This result was expected due to each geographic location's load and solar radiation profiles. Required reliability showed a consistent and predictable effect with cost going down as the requirement relaxed and more hours of outage were allowed. In order to accomplish off-grid operation with favorable economics it is likely that a system will need to reduce its reliability requirement, adopt energy saving consumption habits, choose a favorable geographic location, and either establish a transactive microgrid or include secondary energy generation and/or storage.
Efficient Edge Intelligence in the Era of Big Data
(2021-08) Wong, Jun Hua; Zhang, Qingxue; King, Brian; Schubert, Peter
Smart wearables, known as emerging paradigms for vital big data capturing, have been attracting intensive attentions. However, one crucial problem is their power-hungriness, i.e., the continuous data streaming consumes energy dramatically and requires devices to be frequently charged. Targeting this obstacle, we propose to investigate the biodynamic patterns in the data and design a data-driven approach for intelligent data compression. We leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data. The minimized energy need, even taking into consideration the CAE-induced overhead, is tremendously lower than the original energy need. Further, compared with state-of-the-art wavelet compression-based method, our method can compress the data with a dramatically lower error for a similar energy budget. Our experiments and the validated approach are expected to boost the energy efficiency of wearables, and thus greatly advance ubiquitous big data applications in era of smart health. In recent years, there has also been a growing interest in edge intelligence for emerging instantaneous big data inference. However, the inference algorithms, especially deep learning, usually require heavy computation requirements, thereby greatly limiting their deployment on the edge. We take special interest in the smart health wearable big data mining and inference. Targeting the deep learning’s high computational complexity and large memory and energy requirements, new approaches are urged to make the deep learning algorithms ultra-efficient for wearable big data analysis. We propose to leverage knowledge distillation to achieve an ultra-efficient edge-deployable deep learning model. More specifically, through transferring the knowledge from a teacher model to the on-edge student model, the soft target distribution of the teacher model can be effectively learned by the student model. Besides, we propose to further introduce adversarial robustness to the student model, by stimulating the student model to correctly identify inputs that have adversarial perturbation. Experiments demonstrate that the knowledge distillation student model has comparable performance to the heavy teacher model but owns a substantially smaller model size. With adversarial learning, the student model has effectively preserved its robustness. In such a way, we have demonstrated the framework with knowledge distillation and adversarial learning can, not only advance ultra-efficient edge inference, but also preserve the robustness facing the perturbed input.
Efficient Wearable Big Data Harnessing and Mining with Deep Intelligence
(2022-08) Basile, Elijah James; Zhang, Qingxue; King, Brian; Schubert, Peter
Wearable devices and their ubiquitous use and deployment across multiple areas of health provide key insights in patient and individual status via big data through sensor capture at key parts of the individual’s body. While small and low cost, their limitations rest in their computational and battery capacity. One key use of wearables has been in individual activity capture. For accelerometer and gyroscope data, oscillatory patterns exist between daily activities that users may perform. By leveraging spatial and temporal learning via CNN and LSTM layers to capture both the intra and inter-oscillatory patterns that appear during these activities, we deployed data sparsification via autoencoders to extract the key topological properties from the data and transmit via BLE that compressed data to a central device for later decoding and analysis. Several autoencoder designs were developed to determine the principles of system design that compared encoding overhead on the sensor device with signal reconstruction accuracy. By leveraging asymmetric autoencoder design, we were able to offshore much of the computational and power cost of signal reconstruction from the wearable to the central devices, while still providing robust reconstruction accuracy at several compression efficiencies. Via our high-precision Bluetooth voltmeter, the integrated sparsified data transmission configuration was tested for all quantization and compression efficiencies, generating lower power consumption to the setup without data sparsification for all autoencoder configurations. Human activity recognition (HAR) is a key facet of lifestyle and health monitoring. Effective HAR classification mechanisms and tools can provide healthcare professionals, patients, and individuals key insights into activity levels and behaviors without the intrusive use of human or camera observation. We leverage both spatial and temporal learning mechanisms via CNN and LSTM integrated architectures to derive an optimal classification architecture that provides robust classification performance for raw activity inputs and determine that a LSTMCNN utilizing a stacked-bidirectional LSTM layer provides superior classification performance to the CNNLSTM (also utilizing a stacked-bidirectional LSTM) at all input widths. All inertial data classification frameworks are based off sensor data drawn from wearable devices placed at key sections of the body. With the limitation of wearable devices being a lack of computational and battery power, data compression techniques to limit the quantity of transmitted data and reduce the on-board power consumption have been employed. While this compression methodology has been shown to reduce overall device power consumption, this comes at a cost of more-or-less information loss in the reconstructed signals. By employing an asymmetric autoencoder design and training the LSTMCNN classifier with the reconstructed inputs, we minimized the classification performance degradation due to the wearable signal reconstruction error The classifier is further trained on the autoencoder for several input widths and with quantized and unquantized models. The performance for the classifier trained on reconstructed data ranged between 93.0\% and 86.5\% accuracy dependent on input width and autoencoder quantization, showing promising potential of deep learning with wearable sparsification.
Fabrication and Characterization of Lithium-ion Battery Electrode Filaments Used for Fused Deposition Modeling 3D Printing
(2022-08) Kindomba, Eli; Zhang, Jing; Zhu, Likun; Schubert, Peter
Lithium-Ion Batteries (Li-ion batteries or LIBs) have been extensively used in a wide variety of industrial applications and consumer electronics. Additive Manufacturing (AM) or 3D printing (3DP) techniques have evolved to allow the fabrication of complex structures of various compositions in a wide range of applications. The objective of the thesis is to investigate the application of 3DP to fabricate a LIB, using a modified process from the literature [1]. The ultimate goal is to improve the electrochemical performances of LIBs while maintaining design flexibility with a 3D printed 3D architecture. In this research, both the cathode and anode in the form of specifically formulated slurry were extruded into filaments using a high-temperature pellet-based extruder. Specifically, filament composites made of graphite and Polylactic Acid (PLA) were fabricated and tested to produce anodes. Investigations on two other types of PLA-based filament composites respectively made of Lithium Manganese Oxide (LMO) and Lithium Nickel Manganese Cobalt Oxide (NMC) were also conducted to produce cathodes. Several filaments with various materials ratios were formulated in order to optimize printability and battery capacities. Finally, flat battery electrode disks similar to conventional electrodes were fabricated using the fused deposition modeling (FDM) process and assembled in half-cells and full cells. Finally, the electrochemical properties of half cells and full cells were characterized. Additionally, in parallel to the experiment, a 1-D finite element (FE) model was developed to understand the electrochemical performance of the anode half-cells made of graphite. Moreover, a simplified machine learning (ML) model through the Gaussian Process Regression was used to predict the voltage of a certain half-cell based on input parameters such as charge and discharge capacity. The results of this research showed that 3D printing technology is capable to fabricate LIBs. For the 3D printed LIB, cells have improved electrochemical properties by increasing the material content of active materials (i.e., graphite, LMO, and NMC) within the PLA matrix, along with incorporating a plasticizer material. The FE model of graphite anode showed a similar trend of discharge curve as the experiment. Finally, the ML model demonstrated a reasonably good prediction of charge and discharge voltages.
Introduction to Peter Schubert & His Work
(Center for Translating Research Into Practice, IU Indianapolis, 2023-12) Schubert, Peter
In this video, Dr. Peter Schubert describes his translational research. Three complementary solutions are needed so that everyone can enjoy a comfortable standard of living without despoiling our shared home. First, is baseload power for cities and factories, provided by Space Solar Power, in which orbiting powersats convert sunlight to radiowaves and deliver it wirelessly to terrestrial utilities that delivery electricity and produce hydrogen. Second is rural energy from non-food crop waste that can be converted, on-site, to electricity, heat, biochar, and also hydrogen transportation fuel. Third is safe, convenient hydrogen storage for mobile and portable applications that emit only water vapor as their effluent. These can be built from earth-abundant materials that are eminently recyclable, so that a clean and growing economy can benefit all humankind.
Silicon Based Nano-electronic Synaptic Device for Neuromorphic Hardware
(2024-08) Sikder, Orthi; Schubert, Peter; King, Brian; Rizkalla, Maher; Agarwal, Mangilal
Porous silicon (po-Si) is a unique form of silicon (Si) that features tunable nanopores distributed throughout its bulk structure. While crystalline Si (c-Si) already boasts technological advantages, po-Si offers an additional key aspect with its large surface area relative to its small volume, making it highly conducive to surface chemistry. In this research, our focus centers on the design of a synaptic device based on po-Si, exploring its potential for neuromorphic hardware applications. To begin, we delve into the analysis of several electrical properties of po-Si using density functional theory (ab initio/first principles) calculations. Notably, we discover the presence of intra-pore dangling states within the bandgap region of po-Si. Although po-Si is known for its higher bandgap compared to c-Si, resulting in low carrier density and increased resistance, the existence of these dangling states significantly impacts its electronic transport. Additionally, we investigate the electric field driven modulation of dangling bonds through controlled intra-pore Si-H bond dissociation. This modulation enables precise control over the density of dangling states, facilitating the tunability of po-Si conductance. Theoretically evaluating the current-voltage characteristics of our proposed po-Si based synaptic devices, we determine the potential range of obtainable conductivity. Finally, we evaluate the performance by integrating porous silicon nanoelectronics devices into neural networks. These devices exhibit superior synaptic plasticity, faster response times, and reduced power consumption compared to other synapses. The research indicates that porous-silicon devices are highly effective in neuromorphic systems, paving the way for more efficient and scalable neural networks. These advancements have significant practical and cost-effective implications for a wide range of applications, including pattern recognition, machine learning, and artificial intelligence. Overall, our analyses reveal that the integration of po-Si based synaptic devices into the neural fabric offers a path towards achieving significantly denser and more energy-efficient neuromorphic hardware. With its tunable properties, large surface area, and potential for controlled conductance, po-Si emerges as a promising candidate for the development of advanced silicon-based nano-electronic devices tailored for neuromorphic computing. As we delve deeper into the potentials of po-Si, the era of cognitive computing, inspired by the elegance of bio-mimetic neural networks, edges closer to becoming a reality.
Solving All the World's Energy Problems Once and For All
(Center for Translating Research Into Practice, IU Indianapolis, 2023-12-15) Schubert, Peter
Dr. Schubert discusses three solutions needed so that everyone can enjoy a comfortable standard of living without damaging our planet. First, is baseload power for cities and factories, provided by Space Solar Power. Second is rural energy from non-food crop waste that can be converted, on-site, to electricity, heat, biochar, and also hydrogen transportation fuel. Third is safe, convenient hydrogen storage for mobile and portable applications that emit only water vapor as their liquid waste. Dr. Schubert shares how these solutions can be built from earth-abundant materials that are recyclable, so that a clean and growing economy can benefit all humankind.
Wireless power transfer: a reconfigurable phased array with novel feeding architecture
(2018-04-13) Szazynski, Mitchel H.; Schubert, Peter
This thesis proposes a reconfigurable phased array of antennas for wireless power transfer. The array finds use in many applications, from drone destruction (for defense) to wireless charging of robots and mobile devices. It utilizes a novel feeding architecture to greatly reduce the number of high cost elements (such as amplifiers and phase shifters) as well as the quantity of unused resources in the system. Upon the instruction of the CPU, the array can separate into any number of subarrays, each of which transmits power to a single receiver, steering its beam as the receiver changes location. Currently dormant elements in the array can be used to provide position information about the receivers, either via Radar, or by listening for beacons pulses from the receiver. All of this is made possible, with only 4 amplifiers and 3 phase shifters, by the proposed 4-Bus Method. The source signal is divided into four buses, which are respectively phase shifted by 270 degrees, 180 degrees, 90 degrees, and 0 degrees (no shifter required) and then amplified. The CPU calculates, based on the number and positions of the receivers / targets, what the amplitude and phase excitation must be at each element. Any phase and amplitude which could be required can be achieved by simply adding together appropriate quantities of the correct two buses. In order to achieve this, the key piece is the variable power divider. These differ from Wilkinson dividers in that the dividing ratio can be changed via an applied DC voltage. Therefore, at each junction, by properly diverting the power levels on each phase bus to their proper location, complete delocalization of both amplifiers and phase shifters can be achieved. A method has also been developed which helps overcome the limitations of each variable power divider. That is, in certain instances, it may be desirable to pass all the power to a single output port or the other, which is not a possibility inherently possible with the device. With the use of a unique combination of RF switches, the nodes achieve much enhanced flexibility. Finally, an intensive study is carried out, in an attempt to yield greater understanding, as well as quick, useful approximations, of the behaviors of both rectangular and hexagonal arrays of various sizes and beam steering angles for wireless power.

Browsing by Author "Schubert, Peter"

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