Browsing by Author "Electrical and Computer Engineering, Purdue School of Engineering and Technology"
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Item A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models(MDPI, 2024-11-20) Najafi, Hamidreza; Savoji, Kimia; Mirzaeibonehkhater, Marzieh; Moravvej, Seyed Vahid; Alizadehsani, Roohallah; Pedrammehr, Siamak; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyBackground: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues. Methods: To address these hurdles, this paper presents an innovative three-step approach that leverages Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and VGG16 algorithms for the accurate reconstruction of three-dimensional (3D) lung tumor images. The first challenge we address is the accurate segmentation of lung tissues from CT images, a task complicated by the overwhelming presence of non-lung pixels, which can lead to classifier imbalance. Our solution employs a GAN model trained with a reinforcement learning (RL)-based algorithm to mitigate this imbalance and enhance segmentation accuracy. The second challenge involves precisely detecting tumors within the segmented lung regions. We introduce a second GAN model with a novel loss function that significantly improves tumor detection accuracy. Following successful segmentation and tumor detection, the VGG16 algorithm is utilized for feature extraction, preparing the data for the final 3D reconstruction. These features are then processed through an LSTM network and converted into a format suitable for the reconstructive GAN. This GAN, equipped with dilated convolution layers in its discriminator, captures extensive contextual information, enabling the accurate reconstruction of the tumor's 3D structure. Results: The effectiveness of our method is demonstrated through rigorous evaluation against established techniques using the LIDC-IDRI dataset and standard performance metrics, showcasing its superior performance and potential for enhancing early lung cancer detection. Conclusions: This study highlights the benefits of combining GANs, LSTM, and VGG16 into a unified framework. This approach significantly improves the accuracy of detecting and reconstructing lung tumors, promising to enhance diagnostic methods and patient results in lung cancer treatment.Item Actinide concentration from lunar regolith via hydrocyclone density separation(Longdom Publishing, 2021) Schubert, Peter J.; Kindomba, Eli; Hantzis, Connor; Conaway, Adam; Yeong, Haoyee; Littell, Steven; Palani, Sashindran; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyBeneficiation of regolith to concentrate the high-density ore fraction from the gangue can be accomplished through momentum transfer methods, such as ballistic deflection or cyclonic separation. This study explores the extraction of actinide-bearing minerals from lunar regolith based on the difference in apparent density between thorium-bearing minerals (e.g. ThO2 ρ=10) from silicates (e.g. SiO2 ρ=2.65). Thorium content in lunar regolith ranges from single-digit parts per million (ppm) to as high as 60 ppm. Concentrating thorium-bearing minerals is a required first step in the preparation of fission fuels for a nuclear reactor in which all of the radioactive operations are performed 380,000 km from the Earth’s biosphere. After comparison with ballistic deflection, cyclone separation with a non-volatile fluid carrier was chosen for further study. With sieving to separate particles by size, such a hydrocyclone can be used to efficiently separate the dense fraction from the lighter minerals. Design equations were used to fabricate an at-scale apparatus using water, iron particles, and glass beads as simulants. Results show the ability to effect a 2 to 5.4 % increase in dense fraction concentration each pass, such that 95% concentration requires between 50 and 100 passes, or a cascade of this many apparatuses. The selection of a suitable fluid for safe and low-mass transport to the Moon is part of a techno-economic analysis of the cost and infrastructure needed to produce highly-purified thorium minerals on the lunar surface.Item Advancing Profiling Sensors with a Wireless Approach(MDPI, 2012-11-22) Galvis, Alex; Russomanno, David J.; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyThe notion of a profiling sensor was first realized by a Near-Infrared (N-IR) retro-reflective prototype consisting of a vertical column of wired sparse detectors. This paper extends that prior work and presents a wireless version of a profiling sensor as a collection of sensor nodes. The sensor incorporates wireless sensing elements, a distributed data collection and aggregation scheme, and an enhanced classification technique. In this novel approach, a base station pre-processes the data collected from the sensor nodes and performs data re-alignment. A back-propagation neural network was also developed for the wireless version of the N-IR profiling sensor that classifies objects into the broad categories of human, animal or vehicle with an accuracy of approximately 94%. These enhancements improve deployment options as compared with the first generation of wired profiling sensors, possibly increasing the application scenarios for such sensors, including intelligent fence applications.Item AutoForecast: Automatic Time-Series Forecasting Model S(National Science Foundation, 2022) Abdallah, Mustafa; Rossi, Ryan; Mahadik, Kanak; Kim, Sungchul; Zhao, Handong; Bagchi, Saurabh; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyIn this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. In particular, we develop a forecasting meta-learning approach called AutoForecast that allows for the quick inference of the best time-series forecasting model for an unseen dataset. Our approach learns both forecasting models performances over time horizon of same dataset and task similarity across different datasets. The experiments demonstrate the effectiveness of the approach over state-of-the-art (SOTA) single and ensemble methods and several SOTA meta-learners (adapted to our problem) in terms of selecting better forecasting models (i.e., 2X gain) for unseen tasks for univariate and multivariate testbeds.Item Context-Aware Collaborative Intelligence With Spatio-Temporal In-Sensor-Analytics for Efficient Communication in a Large-Area IoT Testbed(IEEE, 2021) Chatterjee, Baibhab; Seo, Dong-Hyun; Chakraborty, Shramana; Avlani, Shitij; Jiang, Xiaofan; Zhang, Heng; Abdallah, Mustafa; Raghunathan, Nithin; Mousoulis, Charilaos; Shakouri, Ali; Bagchi, Saurabh; Peroulis, Dimitrios; Sen, Shreyas; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyDecades of continuous scaling has reduced the energy of unit computing to virtually zero, while energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous Internet-of-Things (IoT) nodes. This article presents and analyzes the tradeoffs between the energies required for communication and computation in a wireless sensor network, deployed in a mesh architecture over a 2400-acre university campus, and is targeted toward multisensor measurement of temperature, humidity and water nitrate concentration for smart agriculture. Several scenarios involving in-sensor analytics (ISA), collaborative intelligence (CI), and context-aware switching (CAS) of the cluster head during CI has been considered. A real-time co-optimization algorithm has been developed for minimizing the energy consumption in the network, hence maximizing the overall battery lifetime. Measurement results show that the proposed ISA consumes ≈ 467× lower energy as compared to traditional Bluetooth low energy (BLE) communication, and ≈ 69500× lower energy as compared with long-range (LoRa) communication. When the ISA is implemented in conjunction with LoRa, the lifetime of the node increases from a mere 4.3 h to 66.6 days with a 230-mAh coin cell battery, while preserving >99% of the total information. The CI and CAS algorithms help in extending the worst case node lifetime by an additional 50%, thereby exhibiting an overall network lifetime of ≈ 104 days, which is >90% of the theoretical limits as posed by the leakage current present in the system, while effectively transferring information sampled every second. A Web-based monitoring system was developed to continuously archive the measured data, and for reporting real-time anomalies.Item Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer’s Disease(Sage, 2024) Xie, Linhui; Raj, Yash; Varathan, Pradeep; He, Bing; Yu, Meichen; Nho, Kwangsik; Salama, Paul; Saykin, Andrew J.; Yan, Jingwen; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyBackground: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.Item EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox(Science Publishing Group, 2021) Kalgaonkar, Priyank; El-Sharkawy, Mohamed; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyIntelligent edge devices with built-in processors vary widely in terms of capability and physical form to perform advanced Computer Vision (CV) tasks such as image classification and object detection, for example. With constant advances in the field of autonomous cars and UAVs, embedded systems and mobile devices, there has been an ever-growing demand for extremely efficient Artificial Neural Networks (ANN) for real-time inference on these smart edge devices with constrained computational resources. With unreliable network connections in remote regions and an added complexity of data transmission, it is of an utmost importance to capture and process data locally instead of sending the data to cloud servers for remote processing. Edge devices on the other hand, offer limited processing power due to their inexpensive hardware, and limited cooling and computational resources. In this paper, we propose a novel deep convolutional neural network architecture called EffCNet which is an improved and an efficient version of CondenseNet Convolutional Neural Network (CNN) for edge devices utilizing self-querying data augmentation and depthwise separable convolutional strategies to improve real-time inference performance as well as reduce the final trained model size, trainable parameters, and Floating-Point Operations (FLOPs) of EffCNet CNN. Furthermore, extensive supervised image classification analyses are conducted on two benchmarking datasets: CIFAR-10 and CIFAR-100, to verify real-time inference performance of our proposed CNN. Finally, we deploy these trained weights on NXP BlueBox which is an intelligent edge development platform designed for self-driving vehicles and UAVs, and conclusions will be extrapolated accordingly.Item Flexible and Scalable Annotation Tool to Develop Scene Understanding Datasets(National Science Foundation, 2022) Elahi, Md Fazle; Tian, Renran; Luo, Xiao; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyRecent progress in data-driven vision and language-based tasks demands developing training datasets enriched with multiple modalities representing human intelligence. The link between text and image data is one of the crucial modalities for developing AI models. The development process of such datasets in the video domain requires much effort from researchers and annotators (experts and non-experts). Researchers re-design annotation tools to extract knowledge from annotators to answer new research questions. The whole process repeats for each new question which is time consuming. However, since the last decade, there has been little change in how the researchers and annotators interact with the annotation process. We revisit the annotation workflow and propose a concept of an adaptable and scalable annotation tool. The concept emphasizes its users’ interactivity to make annotation process design seamless and efficient. Researchers can conveniently add newer modalities to or augment the extant datasets using the tool. The annotators can efficiently link free-form text to image objects. For conducting human-subject experiments on any scale, the tool supports the data collection for attaining group ground truth. We have conducted a case study using a prototype tool between two groups with the participation of 74 non-expert people. We find that the interactive linking of free-form text to image objects feels intuitive and evokes a thought process resulting in a high-quality annotation. The new design shows ≈ 35% improvement in the data annotation quality. On UX evaluation, we receive above-average positive feedback from 25 people regarding convenience, UI assistance, usability, and satisfaction.Item mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave(IEEE, 2022) Xie, Yucheng; Jiang, Ruizhe; Guo, Xiaonan; Wang, Yan; Cheng, Jerry; Chen, Yingying; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyThere is a growing trend for people to perform work-outs at home due to the global pandemic of COVID-19 and the stay-at-home policy of many countries. Since a self-designed fitness plan often lacks professional guidance to achieve ideal outcomes, it is important to have an in-home fitness monitoring system that can track the exercise process of users. Traditional camera-based fitness monitoring may raise serious privacy concerns, while sensor-based methods require users to wear dedicated devices. Recently, researchers propose to utilize RF signals to enable non-intrusive fitness monitoring, but these approaches all require huge training efforts from users to achieve a satisfactory performance, especially when the system is used by multiple users (e.g., family members). In this work, we design and implement a fitness monitoring system using a single COTS mm Wave device. The proposed system integrates workout recognition, user identification, multi-user monitoring, and training effort reduction modules and makes them work together in a single system. In particular, we develop a domain adaptation framework to reduce the amount of training data collected from different domains via mitigating impacts caused by domain characteristics embedded in mm Wave signals. We also develop a GAN-assisted method to achieve better user identification and workout recognition when only limited training data from the same domain is available. We propose a unique spatialtemporal heatmap feature to achieve personalized workout recognition and develop a clustering-based method for concurrent workout monitoring. Extensive experiments with 14 typical workouts involving 11 participants demonstrate that our system can achieve 97% average workout recognition accuracy and 91% user identification accuracy.Item Natural Gamma Transmutation Studies(American Astronomical Society, 2021) Schubert, Peter J.; Electrical and Computer Engineering, Purdue School of Engineering and TechnologyBeyond Earth’s magnetosphere is a spectrum of extra-gallactic energetic photons of mysterious origin. Called the “gamma fog” this source of gamma rays provides a unique opportunity to study neutron generation (from beryllium) and the use of such neutrons to transmute elements found in the lunar crust. There are also many commercial applications.