Department of Electrical and Computer Engineering Works

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    Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images
    (Springer Nature, 2023-01-27) Huang, Zhi; Shao, Wei; Han, Zhi; Alkashash, Ahmad Mahmoud; De la Sancha, Carlo; Parwani, Anil V.; Nitta, Hiroaki; Hou, Yanjun; Wang, Tongxin; Salama, Paul; Rizkalla, Maher; Zhang, Jie; Huang, Kun; Li, Zaibo; Electrical and Computer Engineering, School of Engineering and Technology
    Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.
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    CNN-based network has Network Anisotropy -work harder to learn rotated feature than non-rotated feature
    (IEEE, 2022-10) Dale, Ashley S.; Qui, Mei; Christopher, Lauren; Krogg, Wen; William, Albert; Electrical and Computer Engineering, School of Engineering and Technology
    Successful object identification and classification in a generic Convolutional Neural Network (CNN) depends on object orientation. We expect CNN-based architectures to work harder to learn a rotated version of a feature than when learning the same feature in its default orientation. We name this phenomenon “Network Anisotropy”. A data set of 6000 RGB and grayscale images was created with rotated orientations of a feature predetermined and evenly distributed across four classes: 0°, 30°, 60°, 90°. Four ResNet (18, 34, 50, 101) classifier architectures were trained and the confidence scores were used to represent prediction accuracy. The results show that in all networks, training performance lags several epochs for the 30° and 60° rotation predictions compared to the 0° and 90° rotations, indicating a quantifiable network anisotropy. Because 0° and 90° both lie along a single rectilinear axis that coincides with the convolutional kernel of the CNN, we expect the classifier to do better on these two classes than on 30° and 60° classes. This work confirms that CNN architectures may have weaker performance based on feature orientation alone, independent of the feature distribution within the data set or the correlation of features within an image.
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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.
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    Attention Mechanism Improves YOLOv5x for Detecting Vehicles on Surveillance Videos
    (IEEE, 2022-10) Qui, Mei; Christopher, Lauren Ann; Chein, Stanley; Chen, Yaobin; Electrical and Computer Engineering, School of Engineering and Technology
    Vehicle detection accuracy on surveillance videos is heavily restricted by camera angles, low lighting conditions, low visibility caused by harsh weather, and serious occlusions. For the full 24/7 operation, the Intelligent Transportation Services (ITS) are expected to perform well on all the categories of the target detections in the environment. Unfortunately, most existing datasets do not cover all these difficult conditions. Moreover, the state-of-the-art Deep Learning detector performance decreases for these difficult conditions. This paper reports on the training of an object detection system using a range of traffic scenarios: sunny, rainy, snowy, one-side road, two-side road, complex road structures with occlusions, heavy traffic with congestion, light traffic, and reduced traffic at night. The state-of-the-art object detector of YOLOv5x is used for vehicle detection and is fine-tuned on this new diverse dataset through transfer learning. Transfer learning freezes the backbone network while training the remaining fully connected network. To further improve the detection performance, we added two convolutional block attention modules (CBAM) to the neck as our proposed system: 2xCBAM-YOLOv5. Several experiments refined the number of CBAMs and the placement of these modules to optimize performance. Doing transfer learning alone, the mean Average Precision(mAP) on the test data improves from 75.9% to 78.9%. After transfer learning, ablations were done on YOLOv5x combined with the new CBAMs. The resulting mAP reaches 85.0%, while precision improves from 82.3% to 88.2%, recall improves from 72.3% to 80.4% and F1-score improves from 0.77 to 0.841 compared with transfer learning alone. This new architecture provides an overall improvement for ITS traffic surveillance applications.
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    Trusted Data Anomaly Detection (TaDA) in Ground Truth Image Data
    (IEEE, 2022-10) Boler, William; Dale, Ashley; Christopher, Lauren; Electrical and Computer Engineering, School of Engineering and Technology
    Current state-of-the-art Artificial Intelligence (AI) anomaly detection from images is primarily used for defect detection and relies on relatively homogeneous datasets of images with similar foregrounds and backgrounds. This type of anomaly detection uses human labelled ground-truth data. In our research, we have extremely heterogeneous datasets and want to identify outliers. We use self-supervised Variational Autoencoders (VAEs) to identify anomalies in the latent vector feature space. Understanding the outliers in a large training data set is important for establishing trustworthiness of the AI models learned from these data, a strong requirement for military AI applications. Our study uses 8984 examples from Kaggle military planes and 4300 examples from Kaggle landscape data. We present the results of the combined heterogeneous dataset on the localized methods, with one such result exhibiting inliers as landscapes/backgrounds and outliers as all aircraft, detecting aircraft as anomalies with a 0.87 AUC. Results also include the inter-class AUC across the different aircraft classes. Our contribution to the state-of-the-art is to apply isolation forests to the latent space data after UMAP embeddings in a strongly heterogeneous image dataset for military applications to identify anomalies.
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    INDF: Efficient Transaction Publishing in Blockchain
    (IEEE, 2021-06) Kumar, Valli Sanghami Shankar; Lee, John J.; Hu, Qin; Electrical and Computer Engineering, School of Engineering and Technology
    Blockchain is a distributed ledger technology based on the underlying peer-to-peer network. In this paper, we focus on improving the chances of a transaction being packaged into a valid block so as to be recorded on the main chain. Blockchain nodes typically broadcast transactions they receive to the whole network. Hence, for recording transactions on the blockchain more efficiently, it becomes essential to determine influential nodes to publish transactions, where influential nodes are more actively involved in mining, recording, or broadcasting transactions in the network. To that aim, we propose an Influential Node Determination Framework (INDF) using a series of significant factors, such as hash rate, latency, active time, and degree of a node. Specifically, INDF consists of two parallel schemes: the first scheme figures out influential pools according to their hash rates where a truth-telling mechanism design is employed to encourage the pool nodes to report their true hash rate values; the second one determines influential individual nodes based on an improved L-H index algorithm. Remarkably, the proposed truth-telling mechanism is proved to be incentive-compatible. Our improved L-H index algorithm is comparatively studied to reflect the impacts of different node parameters on the node’s ranking. Extensive experiments are conducted to demonstrate the effectiveness of our proposed framework.
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    TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining
    (Elsevier, 2021) Huang, Zhi; Han, Zhi; Wang, Tongxin; Shao, Wei; Xiang, Shunian; Salama, Paul; Rizkalla, Maher; Huang, Kun; Zhang, Jie; Electrical and Computer Engineering, School of Engineering and Technology
    Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.
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    A social and news media benchmark dataset for topic modeling
    (Elsevier, 2022-07-04) Miles, Samuel; Yao, Lixia; Meng, Weilin; Black, Christopher M.; Ben-Miled, Zina; Electrical and Computer Engineering, School of Engineering and Technology
    Topic modeling is an active research area with several unanswered questions. The focus of recent research in this area is on the use of a vector embedding representation of the input text with both generative and evolutionary topic modeling techniques. Unfortunately, it is hard to compare different techniques when the underlying data and preprocessing steps that were used to develop the models are not available. This paper presents two secondary datasets that can help address this gap. These datasets are derived from two primary datasets. The first consists of 8145 posts from the r/Cancer health forum and the second consists of 18,294 messages submitted to 20 different news groups. The same preprocessing procedure is applied to both datasets by removing punctuation, stop words and high frequency words. Each dataset is then clustered using three different topic modeling techniques: pPSO, ETM and NVDM and three topic numbers: 10, 20, 30. In addition, for pPSO two text embeddings representation are considered: sBERT and Skipgram. The secondary datasets were originally developed in support of a comparative analysis of the aforementioned topic modeling techniques in a study titled “Comparing PSO-based Clustering over Contextual Vector Embeddings to Modern Topic Modeling” submitted to the Journal of Information Processing and Management. The present paper provides a detailed description of the two secondary datasets including the unique identifier that can be used to retrieve the original documents, the pre-processing scripts, the topic keywords generated by the three topic modeling techniques with varying topic numbers and embedding representations. As such, the datasets allow direct comparison with other topic modeling techniques. To further facilitate this process, the algorithm underlying the evolutionary topic modeling technique, pPSO, proposed by the authors is also provided.
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    Solving all the world’s energy problems for once and forever
    (Springer, 2023-07-07) Schubert, Peter J.; Electrical and Computing Engineering, School of Engineering and Technology
    The ultimate baseload power is that which can be delivered from orbit, especially if constructed from in situ materials. Power satellites can deliver GW-class power to municipal statistical areas and industrial parks using wireless power transfer from phased array antennae. Two recent innovations allow for a low specific cost (USD/kWh) at maturity, along with a small carbon footprint (gCO2(eq)/kWh). Remote from cities, local power and heat can be produced from non-food biomass. For villages and settlements in rural areas, agricultural residues can be converted to a tar-free hydrogen-rich syngas suitable for hydrogen extraction or as a fuel for an electrical generator (fuel cell or internal-combustion engine). This proven technology provides always-on power to off-grid locations, as well as heat for cooking or sterilization. Furthermore, with dry feedstock, the process generates biochar that can augment soil productivity, and be carbon-negative as well. Mineral ash from biomass conversion includes silica that can be reduced, with biochar, to produce metallurgical grade silicon. That silicon can be made porous with a chemical etch, and treated with a transition metal to produce a hydrogen storage medium. The parasitic energy loss of charging and discharging catalytically-modified porous silicon is very low, and it has negligible leakage. These qualities make for an ideal choice in fuel cell vehicles and portable electronics. Hydrogen can come from biomass in the countryside, or from powersat electrolysis during periods of low demand in the city. Taken together, these complementary technologies can power all of human needs for all time to come.
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    Implementation and Performance Evaluation of In-vehicle Highway Back-of-Queue Alerting System Using the Driving Simulator
    (IEEE Xplore, 2021-09) Zhang, Zhengming; Shen, Dan; Tian, Renran; Li, Lingxi; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
    This paper proposes a prototype in-vehicle highway back-of-queue alerting system that is based on an Android-based smartphone app, which is capable of delivering warning information to on-road drivers approaching traffic queues. To evaluate the effectiveness of this alerting system, subjects were recruited to participate in the designed test scenarios on a driving simulator. The test scenarios include three warning types (no alerts, roadside alerts, and in-vehicle auditory alerts), three driver states (normal, distracted, and drowsy), and two weather conditions (sunny and foggy). Driver responses related to vehicle dynamics data were collected and analyzed. The results indicate that on average, the drowsy state decreases the minimum time-to-collision by 1.6 seconds compared to the normal state. In-vehicle auditory alerts can effectively increase the driving safety across different combinations of situations (driver states and weather conditions), while roadside alerts perform better than no alerts.