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The hyperbolic Radon transform is a commonly used tool in seismic processing, for instance in seismic velocity analysis, data interpolation and for multiple removal. A direct implementation by summation of traces with different moveouts is computationally expensive for large data sets. In this paper we present a new method for fast computation of the hyperbolic Radon transforms. It is based on usi
A 27-47-GHz differential cascode power amplifier for millimeter-wave 5G new radio applications is presented. The PA is a three-stage design using inductively coupled impedance transformers with 31-dB nominal power gain. A device periphery ratio of 1:2:6 is adopted for predriver, driver, and final stage, respectively. A gain equalization technique was used in the interstage transformers to obtain t
Brain activation mapping using functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) contrast has been conventionally focused on probing gray matter, the BOLD contrast in white matter having been generally disregarded. Recent results have provided evidence of the functional significance of the white matter BOLD signal, showing at the same time that its correlation structure is hi
The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. Using graph
The ability to remember an episode from our past is often hindered by competition from similar events. For example, if we want to remember the article a colleague recommended during the last lab-meeting, we may need to resolve interference from other article recommendations from the same colleague. This study investigates if the contextual features specifying the encoding episodes are incidentally
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-performing policies, can produce dangerous behaviors while learning, and the optimized policies (usually modeled as neural networks) give almost zero expla
A convex optimization framework over contrast current density is developed to calculate fundamental bounds on the performance of linear passive cloaks. The formulation uses the method of moments applied to the electric field integral equation while using extincted power as the optimized metric. The presented results show that high cloaking efficiency requires cloaks made of low-loss and high-contr
The prospect of ex vivo functional evaluation of donor hearts is considered. Particularly, the dynamics of a synthetic cardiac afterload model are compared to those of normal physiology. A method for identification of continuous-time transfer functions from sampled data is developed and verified against results from the literature. The method relies on exact gradients and Hessians obtained through
Purpose—Ischemic myocardial contracture (IMC) or ‘‘stoneheart’’ is a condition with rapid onset following circulatory death. It inhibits transplantability of hearts donated uponcirculatory death (DCD). We investigate the effectiveness of hemodynamic normalization upon withdrawal of life-sustaining therapy (WLST) in a large-animal controlled DCD model, with the hypothesis that reduction in cardiac
This work analyzes the nonlinear effects in the track and hold circuit applied in high-speed ADCs or RF sampling receiver (RX) front-ends. Non-ideal effects inside the main sampling NMOS switch are studied. Parasitic varactor and sampling on-resistance modulation effects are analyzed through frequency domain Volterra series and the EKV MOS transistor model. Polynomial curve fitting is applied show
In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and c
The notion of Cloud RAN is taking a prominent role in narrative for the next generation wireless infrastructure. It is also seen as a mean to industrial communication systems. In order to provide reliable wireless connectivity for industrial deployments, by conventional means, the cloud infrastructure needs to be reliable and incur little latency, which however, is contradictory to the stochastic
We address the problem of insufficient quality of robot simulators to produce precise sensor readings for joint positions, velocities and torques. Realistic simulations of sensor readings are particularly important for real time robot control laws and for data intensive Reinforcement Learning of robot movements in simulation. We systematically construct two architectures based on Long Short-Term M
Software development companies spend considerable time resolving bug reports. However, bug reports might be invalid, i.e., not point to a valid flaw. Expensive resources and time might be expended on invalid bug reports before discovering that they are invalid. In this case study, we explore the impact of invalid bug reports and develop and assess the use of machine learning (ML) to indicate wheth
The IEEE 802.11 standard, often referred to as Wi-Fi, underpins wireless networking applications around the world that impact our daily lives, such as wireless access to the Internet from offices, homes, airports, hotels, restaurants, trains, and aircraft. Today's laptops, tablets, and smartphones are typically equipped with at least one IEEE 802.11 radio. IEEE 802.11 standards have enabled a whol
We show that spatially coupled low-density parity-check (LDPC) codes yield robust performance over changing intersymbol interfere (ISI) channels with optimal and suboptimal detectors. We compare the performance with classical LDPC code design which involves optimizing the degree distribution for a given (known) channel. We demonstrate that these classical schemes, despite working very good when de
Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework allows calculating minimum safe inter-vehicular distances for arbitrary ego vehicle control policies. We use this framework to enhance the Responsibility-Sensitive Safety (RSS) model and models based on it,
This issue marks the start of my term as department editor for the “Requirements” column. I very much look forward to exploring contemporary aspects of requirements and requirements engineering (RE) in the coming years! As an institute researcher with RISE, I primarily work in strictly regulated domains, in which requirements are cornerstones in the development activities. Please check my introduc
