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Assessment of disease burden and drug efficacy is achieved preclinically using high resolution micro computed tomography (CT). However, micro-CT is not applicable to clinical human imaging due to operating at high dose. In addition, the technology differences between micro-CT and standard clinical CT prevent direct translation of preclinical applications. The current proof-of-concept study presents spectral photon-counting CT as a clinically translatable, molecular imaging tool by assessing contrast uptake in an ex-vivo mouse model of pulmonary tuberculosis (TB). Iodine, a common contrast used in clinical CT imaging, was introduced into a murine model of TB. The excised mouse lungs were imaged using a standard micro-CT subsystem (SuperArgus) and the contrast enhanced TB lesions quantified. The same lungs were imaged using a spectral photoncounting CT system (MARS small-bore scanner). Iodine and soft tissues (water and lipid) were materially separated, and iodine uptake quantified. The volume of the TB infection quantified by spectral CT and micro-CT was found to be 2.96 mm3 and 2.83 mm3, respectively. This proof-of-concept study showed that spectral photon-counting CT could be used as a predictive preclinical imaging tool for the purpose of facilitating drug discovery and development. Also, as this imaging modality is available for human trials, all applications are translatable to human imaging. In conclusion, spectral photon-counting CT could accelerate a deeper understanding of infectious lung diseases using targeted pharmaceuticals and intrinsic markers, and ultimately improve the efficacy of therapies by measuring drug delivery and response to treatment in animal models and later in humans.

This paper presents a technique for grounding system design in a small area distribution substation. Compression ratio adjustment in order to decrease touch voltage is hard to design with this situation due to the limitation of area. Furthermore, this condition can’t use the ground rod length increasing method since the length doesn’t reach bottom layer of the ground. So this paper will show a method for solve this problem by using apparent resistivity from field, then analyze and estimate the resistivity with steepest descent method, after that simulate grounding system with two layers of soil by using CDEGS software base on IEEE 80-2000. The results show that touch voltage decrease 47.48 percent when increase ground grid and ground rod in square type at the edge of substation although bottom layer has more soil resistivity than top layer.

This paper introduces the interconnection between the power generation controlled by frequency and by the angle. The conventional active power generation by synchronous generator is controlled normally by adjusting the frequency output. This operation causes the frequency deviation, severe deviation leads the system unstable. In this paper, the grid supporting inverter the active power controlled by angle is proposed to mitigate the frequency deviation in the power system that interconnected with the synchronous generator. The proposed inverter supplies the active power as specified while regulating the output voltage. The feasibility of the inverter is verified by the simulation of three case studies.

For a fixed-wing unmanned aerial vehicle flight in turbulent environments, distributing aerodynamic load of single aileron through multiple segments can provide rapid actuation, precise roll and heave control while also having the potential to improve yaw response for tail-less (flying wing) aircraft. This paper investigates identification of multi-segment unmanned aerial vehicle (SUAV) via aileron (two segments) instead of the conventional control. Only roll axis is considered in this paper given it represents the most sensitive axis to atmospheric disturbances. The multi-segment aileron is configured as a multi-input and single-output system and each segment is regarded a control input. Experiments are conducted in a wind tunnel to determine the frequency response of the system and the corresponding transfer functions. The experimental results and the mathematical models indicate that interaction between various aileron surfaces is nonlinear. An understanding of this non-linearity aids future development of precise maneuverability, energy efficient control and highly stable operation under severe air turbulence.

Data deduplication has become a crucial technique for reducing data in modern storage systems. We present SEP-D, a practical scale-out distributed storage system to incorporate data deduplication for primary storage. SEP-D introduces a novel metadata handling mechanism which combines contentbased hashing with built-in distributed data placement strategies such as CRUSH. This enables SEP-D to eliminate the needs for remote metadata lookups, thus incorporating deduplication without affecting scalability. SEP-D integrates smoothly with the existing storage system, allowing the re-use of storage policies across different pools of storage. We implemented SEP-D in Ceph, a popular distributed storage system widely adopted in the industry, and demonstrated that SEP-D has minimal impact on I/O performance in data while maintaining existing storage policies implemented in underlying distributed storage systems.

The advent of IoT and edge computing will lead to massive amounts of data that need to be collected and transmitted to online storage systems. To address this problem, we push data deduplication to the network edge. Specifically, we propose a new technique for collaborative edge-facilitated deduplication (EF-dedup), wherein we partition the resource-constrained edge nodes into disjoint clusters, maintain a deduplication index structure for each cluster using a distributed key-value store and perform decentralized deduplication within those clusters. This is a challenging partitioning problem that addresses a novel tradeoff: edge nodes with highly correlated data may not always be within the same edge cloud, with non-trivial network cost among them. We address this challenge by first formulating an optimization problem to partition the edge nodes, considering both the data similarities across the nodes and the inter-node network cost. We prove that the problem is NP-Hard, provide bounded heuristics to solve it and build a prototype EF-dedup system. Our experiments on EF-dedup, performed on edge nodes in AT&T research lab and a central cloud at AWS, demonstrate that EF-dedup achieves 38.3-118.5% better deduplication throughput than sole cloud-based techniques and achieves 43.4-60.2% lesser aggregate cost in terms of the network-storage tradeoff as compared to approaches that solely favor one over the other.

The concept of smart cities is gaining interest in the field of urban development. Social inclusion and equality for all citizens are major challenges for smart city architecture. By improving access to the community so the quality of life of mobility impaired people can be improved. Several types of technologies have been proposed to gather necessary information about disability access facilities within a city and provide better accessibility solutions. This paper presents a systematic literature review of some of the technological components which are the building blocks of smart accessibility and a comparison of existing technologies and case studies. A smart city-based system architecture is proposed based on the modification of existing approaches to centralize the accessibility of information and improve the ease of mobility for people with movement disabilities. The proposed architecture gathers and stores disability-related geographical point of interests, provides customized routing services using open source solutions for wheelchair access, and makes the data available for open access.

Treatment failure in cancer is often due to variation in tumour characteristics within the same tumour, or across tumour sites, or over time. At present, most cancers are staged with imaging; treatment is selected, then the patient is re-imaged to see if the treatment is working. We intend to transform that approach by using a novel non-invasive spectral imaging technology together with targeted and non-targeted gold nanoparticles to measure tumour burden as well as drug delivery. In this study, we report spectral CT imaging of four different cancer cell types (ovarian, breast, Raji cancer cells and Lewis lung carcinoma) using gold nanoparticles. We also report that drug labelled targeted gold nanoparticles can specifically target HER2+ breast cancer cells and can be quantified by a spectral scanner. MARS CT incorporated with Medipix3RX detector was used. For image acquisition, four energy thresholds were set between 18 to 118keV to detect the K-edge of gold nanoparticles. Reconstructed images in narrow energy bins were used for material decomposition. In the first study, two ovarian cancer cell lines (OVCAR5 and SKOV3) were incubated in four sizes of gold nanoparticles (18, 40, 60 and 80nm). Results indicated a high uptake of 18 and 80nm of the gold nanoparticle by SKOV3; OVCAR5 show less uptake for all four nanoparticle sizes. In the second study, Lewis lung carcinoma was implanted in C57BL mice, and 15nm non-functionalized gold nanoparticles were injected via tail vein. Gold nanoparticles were visualized and quantified (0.497mg) in the peripheral region of a tumour whilst showing tumour necrosis in the middle. The third study showed the successful cross-over experiment of gold nanoparticles labelled to two drugs, Rituximab, and Herceptin to target Raji, and breast cancer cells respectively. The findings demonstrated spectral CT has the potential to enable the imaging and quantification of nanoparticles to monitor biological or disease processes and drug delivery to specific cell types.

We describe a CMS-Medipix3RX neutron camera developed by adapting and modifying detector readout electronics developed at the University of Canterbury. The readout electronics are part of the MARS x-ray scanner used for imaging applications [1]. The neutron cameras will be used for the precise evaluation of complex radiation fields in and around the Compact Muon Solenoid (CMS) detector on the Large Hadron Collider (LHC) at CERN. This evaluation will help to ascertain the performance of various sub-systems installed in the cavern as well as to predict their useful lifetimes. Medipix3RX detector can deliver real-time images of the flux and spectral composition of different particles, including slow and fast neutrons. In this neutron camera, slow neutrons are detected using a lithium fluoride conversion layer and fast neutrons by a polypropylene layer. These produce charged particles, which are then detected by a semiconductor sensor material, silicon. We modelled the mixed-field radiation at seven Medipix detector locations in the cavern by scoring the particle travelling through the detector location using FOCUS, a Monte-Carlo simulation tool, analysing the energy as well as their angular distributions of neutrons from the result of simulations.A good agreement was observed between the average flux predicted by standard FLUKA methods and those obtained from FOCUS output data integrated over time. Also, the response function of the Medipix detectors was modelled and simulated for different thicknesses of the neutron conversion layer. An algorithm was developed for track reconstruction and recognition using cluster analysis techniques. This labels and determines the density of clusters formed by groups of particles. The CMS-Medipix detectors with their conversion layers were calibrated in the CERN neutron facility and installed in the CMS cavern at the beginning of 2018. This paper discusses the calibration of the detector installation and presents early results of radiation measurements from 2018 run.

This study aims to demonstrate that spectral CT imaging can identify and quantify inflammatory components of unstable plaque such as iron, calcium and lipid in phantoms and excised human atherosclerotic plaques. Spectral CT acquisition protocol was optimised using the MARS spectral scanner. A phantom with multiple concentrations of ferric nitrate (25, 50, 100, 200 and 400 mg/ml), hydroxyapatite (104.3, 402.3, and 603.3 mg/cm3), iodine (9 and 18 mg/ml), lipid and water was scanned followed by blood clots and excised human plaques using energy thresholds 20, 28, 36 and 44 keV at 80 kVp, 55 µA tube current and 100 ms exposure time. CT images were reconstructed in narrow energy bins. Differences in linear attenuation coefficients between different concentrations of ferric nitrate and hydroxyapatite were compared using the receiver operating characteristic (ROC) curve and considered successful if AUC≥0.8. Differentiation between iron and calcium was successful at 400mg/ml ferric nitrate and 100mg/ml hydroxyapatite (AUC≥0.9; 99% correct material identification). The optimised calibrations were implemented in blood clots and plaque scans, which successfully identified iron signal within the clots, and areas of intraplaque haemorrhage and calcification in the carotid plaque specimens.

The aim of the present study is to show that non-invasive MARS imaging can differentiate between infected and healthy pulmonary tissue using an iodine-based contrast agent at high resolution. One C57BL/6J mouse with chronic tuberculosis (TB) was euthanized with CO2 and the pulmonary tissue excised. The TB lungs were incubated in 3% iodine solution. Mouse pulmonary tissue free of TB was also excised and incubated in the iodine solution for control purposes. Calibration of the MARS scanner involved scanning a phantom containing four concentrations of iodine along with water (soft tissue) and lipid (fat). The calibration phantom, control, and TB infected tissue were imaged at four threshold energy levels (20, 27, 34, 45 keV) at a constant 60 kVp tube voltage and 90 µA tube current. Following analysis of the calibration phantom, material decomposition (MD) was applied to the pulmonary tissue samples and iodine to obtain material images. MARS Vision software was used to visualize the materials to produce 3D material images. TB granulomas are visible within the lung lobes due to the iodine uptake. The amount of iodine uptake can be measured in mg by analysis of the material images using MARS Vision. MARS imaging was able to better differentiate between infected and healthy tissue. The present study demonstrated non-invasive, photon-counting CT is capable of differentiating between infected and healthy tissue. Future studies will consider development of TB markers, or drug markers labelled with gold nanoparticles, to enhance the understanding of the basic biology and mechanisms underpinning TB, and its relevance to the phenomenon of persistence in the infected host during therapy.

The growing introduction of non-dispatchable intermittent energy sources to the electrical grid can cause some additional instability to arise. Energy storage systems can be used to close the gap between power generated and load demanded by either supplying power to the grid when other sources do not meet demand or consume power when demand is lower than supply. An interesting alternative to pumped hydro energy storage has been developed by Advanced Rail Energy Storage LLC which seems to be an ideal fit for the landscape of West Virginia. This study seeks to understand the technology behind the system and identify suitable sites in West Virginia where this technology could be successfully applied and operated. Simulation results are provided for both the general technology and the specific site identified as having great potential.

This paper presents the design of Distributed Generator (DG) for using in microgrid in which the sharing of power is controlled by the power-angle control. The responsibility of the proposed DG is to deliver the active power to the microgrid in according to the command from the DG’s operator. By using with the DG with the on-demand power feeding, the proposed DG is capable of feeding the power to the microgrid autonomously. Simulation results demonstrate the validity of the proposed control.

Erasure-coded storage systems have gained considerable adoption recently since they can provide the same level of reliability with significantly lower storage overhead compared to replicated systems. However, background traffic of such systems - e.g. repair, rebalance, backup and recovery traffic - often has large volume and consumes significant network resources. Independently scheduling such tasks and selecting their sources can easily create interference among data flows, causing severe deadline violation. We show that the well-known heuristic scheduling algorithms fail to consider important constraints, thus resulting in unsatisfactory performance. In this paper, we claim that an optimal scheduling algorithm that aims to maximizethe number of background tasks completed before deadlines must simultaneously consider deadline-aware scheduling, network topology, chunk placement, and time-varying resource availability. To solve this problem, we propose a novel algorithm, called Linear Programming for Selected Tasks (LPST) to maximize the number of successful tasks and improve overallutilization of the datacenter network. It jointly schedules tasks and selects their sources based on a notion of Remaining Time Flexibility, which measures the slackness of the starting time of a task. We evaluated the efficacy of our algorithm using extensive simulations. Our results show that, under certain scenarios, LPST can perform 7x∼70x better than the heuristics which blindly treat the infrastructure as a collection of homogeneous resources, and 46.6%∼65.9% better than the algorithms that take into accountthe network topology.

Multimedia streaming is a major mobile application, accounting for more than half of total mobile traffic. Streaming applications usually have a static buffering strategy. For example, buffer size is limited to x minutes of the stream, where x is optimized to provide the best trade-off between minimizing stalls and limiting waste of user's bandwidth and energy resulting from user abandonment. We show that such strategies based on information available on the mobile device alone do not work well when network conditions change dynamically, e.g., connectivity degrades due to congestion. We propose an alternative strategy using the framework called TANGO, based on a novel idea of cooperation between cellular network and mobile devices. By monitoring real-time network conditions and continuously predicting user location, our system is able to predict connectivity degradation in the near term. In such events, a notification is sent to the mobile device so that the streaming application can initiate a mitigation action, such as to pre-cache more content. In simulations based on real user traces, we found that TANGO reduces pause time by 13–72%, significantly outperforming DASH, which is the current state of the art.

Many storage systems are undergoing a significant shift from dedicated appliance-based model to software-defined storage (SDS) because the latter is flexible, scalable and cost-effective for modern workloads. However, it is challenging to provide a reliable guarantee of end-to-end performance in SDS due to complex software stack, time-varying workload and performance interference among tenants. Therefore, modeling and monitoring the performance of storage systems is critical for ensuring reliable QoS guarantees. Existing approaches such as performance benchmarking and analytical modeling are inadequate because they are not efficient in exploring large configuration space, and cannot support elastic operations and diverse storage services in SDS. This paper presents Inside-Out, an automatic model building tool that creates accurate performance models for distributed storage services. Inside-Out is a black-box approach. It builds high-level performance models by applying machine learning techniques to low-level system performance metrics collected from individual components of the distributed SDS system. Inside-Out uses a two-level learning method that combines two machine learning models to automatically filter irrelevant features, boost prediction accuracy and yield consistent prediction. Our in-depth evaluation shows that Inside-Out is a robust solution that enables SDS to predict end-to-end performance even in challenging conditions, e.g., changes in workload, storage configuration, available cloud resources, size of the distributed storage service, and amount of interference due to multi-tenants. Our experiments show that Inside-Out can predict end-to-end performance with 91.1% accuracy on average. Its prediction accuracy is consistent across diverse storage environments.

Background traffic, such as repair, rebalance, backup and recovery traffic, often has large volume and consumes significant network resources in cloud storage systems. While having each application independently schedule its own background traffic can easily generate interference among data flows, causing violation of desired QoS requirements (e.g., latency and deadline), heuristic scheduling algorithms like Earliest-Deadline-First and First-In-First-Out are not able to take into account data center constraints such network topology or data chunk placement, thus resulting in unsatisfactory performance. In this paper, we propose a new algorithm, Linear Programming for Selected Tasks (LPST), which coordinate background traffic of different jobs to meet traffic deadline and optimize system throughput. In particular, our goal is to maximize the number of background traffic flows that meet their target deadlines under bandwidth constraints in data center storage systems. Using realistic traffic trace, our simulation results show that the proposed algorithm significantly improves task processing time and the probability of meeting deadlines.

A complex cloud application consists of virtual machines (VMs) running software such as web servers and load balancers, storage in the form of disk volumes, and network connections that enable communication between VMs and between VMs and disk volumes. The application is also associated with various requirements, including not only quantities such as the sizes of the VMs and disk volumes, but also quality of service (QoS) attributes such as throughput, latency, and reliability. This paper presents Ostro, an Open Stack-based scheduler that optimizes the utilization of data center resources, while satisfying the requirements of the cloud applications. The novelty of the approach realized by Ostro is that it makes holistic placement decisions, in which all the requirements of an application -- described using an application topology abstraction -- are considered jointly. Specific placement algorithms for application topologies are described including an estimate-based greedy algorithm and a time-bounded A algorithm. These algorithms can deal with complex topologies that have heterogeneous resource requirements, while still being scalable enough to handle the placement of hundreds of VMs and volumes across several thousands of host servers. The approach is evaluated using both extensive simulations and realistic experiments. These results show that Ostro significantly improves resource utilization when compared with naive approaches.

We propose partial pre-equalization for indoor optical wireless transmissions based on asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) with intensity modulation and direct detection (IM/DD). Bit loading is applied to minimize the transmit optical power for a fixed target BER. Similar to pre-equalization, for diffuse indoor optical wireless channels, partial pre-equalization can reduce the transmit optical power over post-equalization for point-to-point transmissions. In addition, we consider broadcast transmissions to multiple users with possibly different channel qualities, where pre-equalization is not applicable. Finally, we specify an appropriate channel estimate at the transmitter for such broadcast transmissions.

This paper quantifies the optimal utilized bandwidth (BW) of indoor optical wireless transmission using orthogonal frequency division multiplexing (OFDM) with intensity modulation and direct detection (IM/DD) by varying the number of transmitted bits per OFDM symbol. Due to its power efficiency, asymmetrically clipped optical OFDM (ACO-OFDM) is selected as a unipolar OFDM scheme. To further improve its power efficiency, ACO-OFDM is optimized through the bit loading technique where some target BER is fixed. For the transmission bit rates of 20 and 30 Mbps, the optimal operating BW is identified based on the required transmit optical power, and is neither the smallest nor the largest in the considered range of BW in which the cyclic prefix (CP) length is sufficient to avoid intersymbol interference (ISI) and inter-carrier interference (ICI).

This paper investigates the performance of indoor optical wireless transmissions using orthogonal frequency division multiplexing (OFDM) with intensity modulation and direct detection (IM/DD). To obtain unipolar OFDM signals compatible with IM/DD, DC biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are considered with DCO-OFDM as the baseline. Known to be more power efficient, ACO-OFDM is optimized over the size of standard QAM constellations subject to the constraints on a fixed transmission bit rate and on a fixed target bit error rate (BER). However, there is no constraint on the utilized bandwidth; this assumption holds for any room with a single transmitter, e.g., for broadcasting. For the transmission bit rates of 10 and 20 Mbps, using system parameters according to typical operating environments, it is observed that there is an optimal QAM constellation size that is neither the smallest nor the largest.