In this paper, we propose EdgeHD, a hierarchy-aware learning solution that performs online training and inference in a highly distributed, cost-effective way. We use brain-inspired hyperdimensional (HD) computing as the key enabler. HD computing performs the computation tasks on a high-dimensional space to emulate functionalities of the human memory, such as inter-data relationship reasoning and information aggregation. EdgeHD exploits HD computing to effectively learn the classification models on individual devices and combine the models through the hierarchical IoT nodes without high communication costs. We also propose a hardware design that accelerates EdgeHD on low-power FPGA platforms. We evaluated EdgeHD for a wide range of real-world classification applications. The evaluation shows that EdgeHD provides highly efficient computation with reduced communication. For example, EdgeHD achieves on average$3.4\times$and$11.7\times (1.9\times$and$7.8\times$) speedup and energy efficiency improvement during the training (inference) as compared to the centralized learning approach. It reduces the communication costs by 85% for the training and 78% for the inference.

Face detection is an essential component of many tasks in computer vision with several applications. However, existing deep learning solutions are significantly slow and inefficient to enable face detection on embedded platforms. In this paper, we propose HDFace, a novel framework for highly efficient and robust face detection. HDFace exploits HyperDimensional Computing (HDC) as a neurally-inspired computational paradigm that mimics important brain functionalities towards high-efficiency and noise-tolerant computation. We first develop a novel technique that enables HDC to perform stochastic arithmetic computations over binary hypervectors. Next, we expand these arithmetic for efficient and robust processing of feature extraction algorithms in hyperspace. Finally, we develop an adaptive hyperdimensional classification algorithm for effective and robust face detection. We evaluate the effectiveness of HDFace on large-scale emotion detection and face detection applications. Our results indicate that HDFace provides, on average, 6.1X (4.6X) speedup and 3.0X (12.1X) energy efficiency as compared to neural networks running on CPU (FPGA), respectively.

In the Internet of Things (IoT) domain, many applications are running machine learning algorithms to assimilate the data collected in the swarm of devices. Sending all data to the powerful computing environment, e.g., cloud, poses significant efficiency and scalability issues. A promising way is to distribute the learning tasks onto the IoT hierarchy, often referred to edge computing; however, the existing sophisticated algorithms such as deep learning are often overcomplex to run on less-powerful and unreliable embedded IoT devices. Hyperdimensional Computing (HDC) is a brain-inspired learning approach for efficient and robust learning on today's embedded devices. Encoding, or transforming the input data into high-dimensional representation, is the key first step of HDC before performing a learning task. All existing HDC approaches use a static encoder; thus, they still require very high dimensionality, resulting in significant efficiency loss for the edge devices with limited resources. In this paper, we have developed NeuralHD, a new HDC approach with a dynamic encoder for adaptive learning. Inspired by human neural regeneration study in neuroscience, NeuralHD identifies insignificant dimensions and regenerates those dimensions to enhance the learning capability and robustness. We also present a scalable learning framework to distribute NeuralHD computation over edge devices in IoT systems. Our solution enables edge devices capable of real-time learning from both labeled and unlabeled data. Our evaluation on a wide range of practical classification tasks shows that NeuralHD provides 5.7X and 6.1X (12.3X and 14.1X) faster and more energy-efficient training compared to the HD-based algorithms (DNNs) running on the same platform. NeuralHD also provides 4.2X and 11.6X higher robustness to noise in the unreliable network and hardware of IoT environments as compared to DNNs.

Heart diseases alter the rhythmic behaviors of cardiac electrical activity. Recent advances in sensing technology bring the ease to acquire space-time electrical activity of the heart such as vectorcardiogram (VCG) signals. Recurrence analysis of successive heartbeats is conducive to detect the disease-altered cardiac activities. However, conventional recurrence analysis is more concerned about homogeneous recurrences, and overlook heterogeneous types of recurrence variations in VCG signals (i.e., in terms of state properties and transition dynamics). This paper presents a new framework of heterogeneous recurrence analysis for the characterization and modeling of disease-altered spatiotemporal patterns in multi-channel cardiac signals. Experimental results show that the proposed approach yields an accuracy of 96.9%, a sensitivity of 95.0%, and a specificity of 98.7% for the identification of myocardial infarctions. The proposed method of heterogeneous recurrence analysis shows strong potential to be further extended for the analysis of other physiological signals such as electroencephalogram (EEG) and electromyography (EMG) signals towards medical decision making.

Recent studies highlight temporal ultrasound data as highly promising in differentiating between malignant and benign tissues in prostate cancer patients. Since Hidden Markov Models can be used for capturing order and patterns in time varying signals, we employ them to model temporal aspects of ultrasound data that are typically not incorporated in existing models. By comparing order-preserving and order-altering models, we demonstrate that the order encoded in the series is necessary to model the variability in ultrasound data of prostate tissues. In future studies, we will investigate the influence of order on the differentiation between malignant and benign tissues.

UNLABELLED: This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo. METHODS: We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient. RESULTS: In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework. CONCLUSION: Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.