Middleware and Architecture for Advanced Applications of Cyber-physical Systems

In this thesis, we address issues related to middleware, architecture and applications of cyber-physical systems. The first problem we address is the cross-layer design of cyber-physical systems to cope with interactions between the cyber layer and the physical layer in a dynamic environment. We propose a bi-directional middleware that allows the optimal utilization of the common resources for the benefit of either or both the layers in order to obtain overall system performance. The case study of network connectivity preservation in a vehicular formation illustrates how this approach can be applied to a particular situation where the network connectivity drives the application layer. Next we address another aspect of cross-layer impact: the problem that arises when network performance, in this case delay performance, affects control system performance. We propose a two-pronged approach involving a flexible adaptive model identification algorithm with outlier rejection, which in turn uses an adaptive system model to detect and reject outliers, thus shielding the estimation algorithm and thereby improving reliability. We experimentally demonstrate that the outlier rejection approach which intercepts and filters the data, combined with simultaneous model adaptation, can result in improved performance of Model Predictive Control in the vehicular testbed. Then we turn to two advanced applications of cyber-physical systems. First, we address the problem of security of cyber-physical systems. We consider the context of an intelligent transportation system in which a malicious sensor node manipulates the position data of one of the autonomous cars to deviate from a safe trajectory and collide with other cars. In order to secure the safety of such systems where sensor measurements are compromised, we employ the procedure of “dynamic watermarking”. This procedure enables an honest node in the control loop to detect the existence of a malicious node within the feedback loop. We demonstrate in the testbed that dynamic watermarking can indeed protect cars against collisions even in the presence of sensor attacks. The second application of cyber-physical systems that we consider is cyber-manufacturing which is an origami-type laser-based custom manufacturing machine employing folding and cutting of sheet material to manufacture 3D objects. We have developed such a system for use in a laser-based autonomous custom manufacturing machine equipped with real-time sensing and control. The basic elements in the architecture are a laser processing machine, a sensing system to estimate the state of the workpiece, a control system determining control inputs for a laser system based on the estimated data, a robotic arm manipulating the workpiece in the work space, and middleware supporting the communication among the systems. We demonstrate automated 3D laser cutting and bending to fabricate a 3D product as an experimental result. Lastly, we address the problem of traffic management of an unmanned aerial system. In an effort to improve the performance of the traffic management for unmanned aircrafts, we propose a probability-based collision resolution algorithm. The proposed algorithm analyzes the planned trajectories to calculate their collision probabilities, and modifies individual drone starting times to reduce the probability of collision, while attempting to preserve high performance. Our simulation results demonstrate that the proposed algorithm improves the performance of the drone traffic management by guaranteeing high safety with low modification of the starting times

Middleware and Architecture for Advanced Applications of Cyber-physical Systems

In this thesis, we address issues related to middleware, architecture and applications of cyber-physical systems. The first problem we address is the cross-layer design of cyber-physical systems to cope with interactions between the cyber layer and the physical layer in a dynamic environment. We propose a bi-directional middleware that allows the optimal utilization of the common resources for the benefit of either or both the layers in order to obtain overall system performance. The case study of network connectivity preservation in a vehicular formation illustrates how this approach can be applied to a particular situation where the network connectivity drives the application layer. Next we address another aspect of cross-layer impact: the problem that arises when network performance, in this case delay performance, affects control system performance. We propose a two-pronged approach involving a flexible adaptive model identification algorithm with outlier rejection, which in turn uses an adaptive system model to detect and reject outliers, thus shielding the estimation algorithm and thereby improving reliability. We experimentally demonstrate that the outlier rejection approach which intercepts and filters the data, combined with simultaneous model adaptation, can result in improved performance of Model Predictive Control in the vehicular testbed. Then we turn to two advanced applications of cyber-physical systems. First, we address the problem of security of cyber-physical systems. We consider the context of an intelligent transportation system in which a malicious sensor node manipulates the position data of one of the autonomous cars to deviate from a safe trajectory and collide with other cars. In order to secure the safety of such systems where sensor measurements are compromised, we employ the procedure of “dynamic watermarking”. This procedure enables an honest node in the control loop to detect the existence of a malicious node within the feedback loop. We demonstrate in the testbed that dynamic watermarking can indeed protect cars against collisions even in the presence of sensor attacks. The second application of cyber-physical systems that we consider is cyber-manufacturing which is an origami-type laser-based custom manufacturing machine employing folding and cutting of sheet material to manufacture 3D objects. We have developed such a system for use in a laser-based autonomous custom manufacturing machine equipped with real-time sensing and control. The basic elements in the architecture are a laser processing machine, a sensing system to estimate the state of the workpiece, a control system determining control inputs for a laser system based on the estimated data, a robotic arm manipulating the workpiece in the work space, and middleware supporting the communication among the systems. We demonstrate automated 3D laser cutting and bending to fabricate a 3D product as an experimental result. Lastly, we address the problem of traffic management of an unmanned aerial system. In an effort to improve the performance of the traffic management for unmanned aircrafts, we propose a probability-based collision resolution algorithm. The proposed algorithm analyzes the planned trajectories to calculate their collision probabilities, and modifies individual drone starting times to reduce the probability of collision, while attempting to preserve high performance. Our simulation results demonstrate that the proposed algorithm improves the performance of the drone traffic management by guaranteeing high safety with low modification of the starting times

EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks

Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (> 15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.Comment: 16 pages, 15 figure

Structural abnormalities in benign childhood epilepsy with centrotemporal spikes (BCECTS)

AbstractPurposeThe aim of this study was to investigate cortical thickness and gray matter volume abnormalities in benign childhood epilepsy with centrotemporal spikes (BCECTS). We additionally assessed the effects of comorbid attention-deficit/hyperactivity (ADHD) on these abnormalities.MethodsSurface and volumetric MR imaging data of children with newly diagnosed BCECTS (n=20, 14 males) and age-matched healthy controls (n=20) were analyzed using FreeSurfer (version 5.3.0, https://surfer.nmr.mgh.harvard.edu). An additional comparison was performed between BCECTS children with and without ADHD (each, n=8). A group comparison was carried out using an analysis of covariance with a value of significance set as p<0.01 or p<0.05.ResultsChildren with BCECTS had significantly thicker right superior frontal, superior temporal, middle temporal, and left pars triangularis cortices. Voxel-based morphometric analysis revealed significantly larger cortical gray matter volumes of the right precuneus, left orbitofrontal, pars orbitalis, precentral gyri, and bilateral putamen and the amygdala of children with BCECTS compared to healthy controls. BCECTS patients with ADHD had significantly thicker left caudal anterior and posterior cingulate gyri and a significantly larger left pars opercularis gyral volume compared to BCECTS patients without ADHD.ConclusionChildren with BCECTS have thicker or larger gray matters in the corticostriatal circuitry at the onset of epilepsy. Comorbid ADHD is also associated with structural aberrations. These findings suggest structural disruptions of the brain network are associated with specific developmental electro-clinical syndromes

Acute Necrotizing Encephalopathy: Diffusion MR Imaging and Localized Proton MR Spectroscopic Findings in Two Infants

In this report, we describe the findings of diffusion MR imaging and proton MR spectroscopy in two infants with acute necrotizing encephalopathy in which there was characteristic symmetrical involvement of the thalami. Diffusion MR images of the lesions showed that the observed apparent diffusion coefficient (ADC) decrease was more prominent in the first patient, who had more severe brain damage and a poorer clinical outcome, than in the second. Proton MR spectroscopy detected an increase in the glutamate/glutamine complex and mobile lipids in the first case but only a small increase of lactate in the second. Diffusion MR imaging and proton MR spectroscopy may provide useful information not only for diagnosis but also for estimating the severity and clinical outcome of acute necrotizing encephalopathy

Polyclonal gammopathy related to renal bleeding in a peritoneal dialysis patient

Polyclonal gammopathy represents the diffuse activation of B cells and is usually related to inflammation or immune-related diseases. However, the mechanisms leading to polyclonal gammopathy are essentially speculative. Generally, infectious, inflammatory, or various other reactive processes may be indicated by the presence of a broad-based peak or band in the gamma region on serum protein electrophoresis results. A 15-year-old girl, who had been receiving peritoneal dialysis, presented with polyclonal gammopathy and massive gross hematuria. Renal artery embolization was performed, after which the continuous bleeding subsided and albumin-globulin dissociation resolved. This is a rare case of polyclonal gammopathy related to renal bleeding