Search

Transcranial magnetic stimulation (TMS) is often applied to the motor cortex to stimulate a collection of motor evoked potentials (MEPs) in groups of peripheral muscles. The causal interface between TMS and MEP is the selective activation of neurons in the motor cortex; moving around the TMS ‘spot’ over the motor cortex causes different MEP responses. A question of interest is whether a collection of MEP responses can be used to identify the stimulated locations on the cortex, which could potentially be used to then place the TMS coil to produce chosen sets of MEPs. In this work we leverage our previous report on a 3D convolutional neural network (CNN) architecture that predicted MEPs from the induced electric field, to tackle an inverse imaging task in which we start with the MEPs and estimate the stimulated regions on the motor cortex. We present and evaluate five different inverse imaging CNN architectures, both conventional and generative, in terms of several measures of reconstruction accuracy. We found that one architecture, which we propose as M2M-InvNet, consistently achieved the best performance.

As resource-constrained Internet-of-Things (IoT) devices become popular targets of various malicious attacks, frequent updates to keep their software up to date are essential to their security. However, state-of-the-art software delivery and payment systems incorporate multiple services in a client-server structure requiring multiple transits of information between client and server, while also creating a wide attack surface. We propose a blockchain-based end-to-end secure software update delivery framework for Internet of Things (IoT) devices, which aims to ensure confidentiality, integrity, availability, efficiency, and audit-ability for verified software delivery, while offloading the cryptographic computation from resource-constrained IoT devices to a decentralized blockchain system. In particular, we leverage Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and design a customized authorization policy to not only ensure that software updates can only be decrypted and installed on authorized IoT devices but also significantly reduce the computational overhead for key generation and key delivery on the manufacturer side. Furthermore, secure and atomic software delivery and payments between IoT devices and the manufacturer are assured through smart contracts. The authenticity of the delivered software is guaranteed by offloading the computation-based signature validation to smart contracts. Compliance audits are satisfied through immutable records on the blockchain’s public ledger, and the smart contracts efficiently guarantee the delivery of software updates in exchange for payment. Security analysis and experiments are performed to compare the proposed framework with state-of-the-art studies and validate its effectiveness.

This paper presents an efficient physics-based electro-thermal model that solves some advanced problems of modeling Silicon Carbide (SiC) power MOSFETs. It is the first electro-thermal model that simulates the temperature dependency of the first and the third quadrant characteristics, including the reverse recovery of the body diode accurately and efficiently. It extends from a previous work that demonstrated the isothermal physics-based model of the gate-dependent body diode. Physics-based temperature scaling of the first and third quadrant allows simulation of the self-heating effect in a wide range of temperatures (27 °C–200 °C), even for the synchronous operation. Moreover, a physics-based modeling approach is taken to include gate-voltage dependent non-linearity of the gate to source capacitance (Cgs). Also, a physic-based segmented cascaded method is taken to accurately model the Miller (Crss), and the output (Coss) capacitances at the low and very high drain to source voltage regions. Further, the temperature-dependent breakdown mechanism is included for reliable system design. Double Pulse Tests (DPTs) at various temperatures up to 200 °C validate the model's accuracy. Lastly, a synchronous buck converter test demonstrates the model's ability to predict junction temperatures, validating the model's accuracy and efficiency in a continuous operation with self-heating.

Hypofractionated stereotactic body radiotherapy treatments (SBRT) have demonstrated impressive results for the treatment of a variety of solid tumors. The role of tumor supporting vasculature damage in treatment outcome for SBRT has been intensely debated and studied. Fast, non-invasive, longitudinal assessments of tumor vasculature would allow for thorough investigations of vascular changes correlated with SBRT treatment response. In this paper, we present a novel theranostic system which incorporates a fluorescence molecular imager into a commercial, preclinical, microCT-guided, irradiator and was designed to quantify tumor vascular response (TVR) to targeted radiotherapy. This system overcomes the limitations of single-timepoint imaging modalities by longitudinally assessing spatiotemporal differences in intravenously-injected ICG kinetics in tumors before and after high-dose radiation. Changes in ICG kinetics were rapidly quantified by principle component (PC) analysis before and two days after 10 Gy targeted tumor irradiation. A classifier algorithm based on PC data clustering identified pixels with TVR. Results show that two days after treatment, a significant delay in ICG clearance as measured by exponential decay (40.5± 16.1% P = 0.0405 Paired t-test n = 4) was observed. Changes in the mean normalized first and second PC feature pixel values (PC1 & PC2) were found (P = 0.0559, 0.0432 paired t-test), suggesting PC based analysis accurately detects changes in ICG kinetics. The PC based classification algorithm yielded spatially-resolved TVR maps. Our first-of-its-kind theranostic system, allowing automated assessment of TVR to SBRT, will be used to better understand the role of tumor perfusion in metastasis and local control.

Today, cloud computing services are undeniably becoming parts of modern information and communication systems in our daily lives. Cloud computing has proven to be an incredible technology for provisioning quickly deployed and scalable information technology (IT) solutions at reduced infrastructure costs. Cloud computing is unable to meet the requirements of low latency, location awareness, and mobility support. To solve this problem, researchers have introduced a trusted and dependable solution through fog and mobile edge computing (FMEC) to put the services and resources of the cloud closer to users, which facilitates the leveraging of available services and resources in the edge networks. Organizations are beginning to look at FMEC as the answer to the cloud computing problem. FMEC consists of putting micro data centers or even small, purpose-built high-performance data analytics machines in remote offices and locations in order to gain real-time insights from the data collected, or to promote data thinning at the edge, by dramatically reducing the amount of data that needs to be transmitted to a central data center. Without having to move unnecessary data to a central data center, analytics at the edge can simplify and drastically speed analysis while also cutting costs. FMEC pushes applications, data, and computing power (services) away from centralized points to the logical extremes of a network. FMEC replicates fragments of information across distributed networks of web servers, which may be vast. As a topological paradigm, edge computing is also referred to as mesh computing, peer-to-peer computing, autonomic (self-healing) computing, grid computing, and other names implying non-centralized, node-less availability.

Molecular imaging is becoming essential for precision targeted radiation therapy, yet progress is hindered from a lack of integrated imaging and treatment systems. We report the development of a prototype positron emission tomography (PET) scanner integrated into a commercial cone beam computed tomography (CBCT) based small animal irradiation system for molecular-image-guided, targeted external beam radiation therapy. The PET component consists of two rotating Hamamatsu time-of-flight PET modules positioned with a bore diameter of 101.6 mm and a radial field-of-view of 53.1 mm. The measured energy resolution after linearity correction at 511 KeV was 12.9% and the timing resolution was 283.6 ps. The measured spatial resolutions at the field-of-view center and 5 mm off the radial center were 2.6 mm x 2.6 mm x 1.6 mm and 2.6 mm x 2.6 mm x 2.7 mm respectively. 18F-Fluorodeoxyglucose-based PET imaging of a NEMA NU 4-2008 phantom resolved cylindrical volumes with diameters as small as 3 mm. To validate the system in-vivo, we performed 64Cu-DOTA-M5A PET and computed tomography (CT) imaging of carcinoembryonic antigen (CEA)-positive colorectal cancer in athymic nude mice and compared the results with a commercially available Siemens Inveon PET/CT system. The prototype PET system performed comparably to the Siemens system for identifying the location, size, and shape of tumors. Regions of heterogeneous 64Cu-DOTA-M5A uptake were observed. Using 64Cu-DOTA-M5A PET and CT images, a Monte Carlo-based radiation treatment plan was created to escalate the dose to the 64Cu-DOTA-M5A-based, highly active, biological target volume while largely sparing the normal tissue. Results demonstrate the feasibility of molecular-image-guided treatment plans using the prototype theranostic system.