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OBJECTIVE To assess the pharmacokinetics and opioid effects of methadone after administration of multiple doses by means of 2 dosing regimens of methadone-fluconazole-naltrexone. ANIMALS 12 healthy Beagles. PROCEDURES Dogs were randomly allocated (6 dogs/group) to receive 1 of 2 oral dosing regimens of methadone-fluconazole-naltrexone. Treatment 1 doses were administered at 0 (methadone-to-fluconazole-to-naltrexone ratio of 1:5:0.25 mg/kg), 14 (1:5:0.25), 24 (0.5:2.5:0.125), and 38 (0.5:2.5:0.125) hours. Treatment 2 doses were administered at 0 (1:5:0.25), 4 (0.5:2.5:0.125), 10 (0.5:2.5:0.125), and 24 (0.5:2.5:0.125) hours. Blood samples, rectal temperatures, and von Frey antinociceptive measurements were obtained at designated times. RESULTS Compared with baseline, temperatures significantly decreased for treatment 1 group dogs at 2 to ≥ 4 hours and from 16 to ≥ 50 hours (12 hours after last dose) and for treatment 2 group dogs at 2 to ≥ 36 hours (12 hours after last dose), when trough methadone concentrations were ≥ 21.3 ng/mL. Antinociception occurred after the first dose but was not maintained throughout the study. Lesions were noted in some dogs at the application site of the von Frey device. Naltrexone and β-naltrexol were sporadically detected in plasma, and naltrexone glucuronide was consistently detected. CONCLUSIONS AND CLINICAL RELEVANCE Opioid effects were noted after oral administration of the first dose, and data suggested that administering a second dose 6 hours later and every 12 hours thereafter was necessary to maintain opioid effects. Antinociception may have been lost because dogs became averse or hyperalgesic to the von Frey device, such that the antinociception model used here may not be robust for repeated measurements in dogs.

In this demonstration, we present AB9 SoC system, a single-chip solution for AI application. It provides the reconfigurable and programmable architecture to support the general computations for a variety of neural networks. The AB9 SoC is implemented using TSMC 28-nm process technology with a chip size of 17×23 mm2 and 1GHz operating frequency.

OBJECTIVE To determine perioperative analgesia associated with oral administration of a novel methadone-fluconazole-naltrexone formulation in dogs undergoing routine ovariohysterectomy. ANIMALS 43 healthy female dogs. PROCEDURES Dogs were randomly assigned to receive the methadone-fluconazole-naltrexone formulation at 1 of 2 dosages (0.5 mg/kg, 2.5 mg/kg, and 0.125 mg/kg, respectively, or 1.0 mg/kg, 5.0 mg/kg, and 0.25 mg/kg, respectively, PO, q 12 h, starting the evening before surgery; n = 15 each) or methadone alone (0.5 mg/kg, SC, q 4 h starting the morning of surgery; 13). Dogs were sedated with acepromazine, and anesthesia was induced with propofol and maintained with isoflurane. A standard ovariohysterectomy was performed by experienced surgeons. Sedation and pain severity (determined with the Glasgow Composite Pain Scale—short form [GCPS-SF]) were scored for 48 hours after surgery. Rescue analgesia was to be provided if the GCPS-SF score was > 6. Dogs also received carprofen starting the day after surgery. RESULTS None of the dogs required rescue analgesia. The highest recorded GCPS-SF score was 4. A significant difference in GCPS-SF score among groups was identified at 6:30 am the day after surgery, but not at any other time. The most common adverse effect was perioperative vomiting, which occurred in 11 of the 43 dogs. CONCLUSIONS AND CLINICAL RELEVANCE Oral administration of a methadone-fluconazole-naltrexone formulation at either of 2 dosages every 12 hours (3 total doses) was as effective as SC administration of methadone alone every 4 hours (4 total doses) in dogs undergoing routine ovariohysterectomy. Incorporation of naltrexone in the novel formulation may provide a deterrent to human opioid abuse or misuse.

Highlights • The differential diagnosis for neck masses is broad and includes neoplastic, infectious, and anatomic considerations. • Bronchopulmonary neuroendocrine tumors are known to metastasize to the head and neck. • This report is the first of ileal neuroendocrine tumor metastasis to the submandibular gland., Background Neuroendocrine tumors (NETs) of small intestinal origin are generally slow-growing tumors with a relatively high propensity for metastases to surrounding organs and lymphatic tissue. We present the first case of an ileal NET metastasizing to the submandibular gland in a woman with metastatic carcinoid syndrome. Case presentation A 55-year-old female presented with a four-month history of a palpable, left-sided neck mass. The patient had a history of metastatic neuroendocrine tumor of ileal origin, initially treated with primary resection 4.5 years previously, with known subdiaphragmatic metastases to the liver, mesenteric nodes, and peritoneum. Four years following primary resection she developed carcinoid syndrome leading to therapy with radiolabelled metaiodobenzylguanidine (MIBG), as well as telotristat etiprate in the context of a clinical trial due to progressive symptoms. A fine needle aspiration biopsy of the neck mass revealed an immunohistochemical staining pattern consistent with ileal NET. The patient underwent a left level 1b neck dissection and submandibular gland excision. Pathology was consistent with metastastic ileal NET. Conclusion We report the first case of ileal NET metastasis to the submandibular gland. Familiarity with the carcinoid syndrome and associated physiology should be maintained as it can affect the head and neck on rare occasions. Maintaining a broad differential is key in diagnosis of undifferentiated neck masses.

AI processors are extending the application area into mobile and edge devices. The requirement of low power consumption which has been an essential factor in designing processors is now becoming the most critical factor for mobile AI processors to be viable. The high performance requirement exacerbates the energy crisis caused by a large area due to a lot of processing engines required for implementing AI processors. We present the design of an AI processor targeting both CNN and MLP processing in autonomous vehicles. The proposed AI processor integrates Super-Thread-Core composed of 16384 nano cores in mesh-grid network for neural network acceleration. The performance of the processor reaches 32 Tera FLOPS enabling hyper real-time execution of CNN and MLP. Each nano core is programmable by a sequence of instructions compiled from the neural network description by the proprietary AI-Ware. The mesh-array of nano cores at the heart of neural computing accounts for most of the power consumption. The AI-ware compiler enables adaptive power gating by dynamically compiling the commands based on the temperature profile reducing 50% of total power consumption.

An implementation of Yolo-v2 image recognition and other testbenches for a deep learning accelerator is presented. This chip is the initial version of our on-going effort for a higher performance accelerator development. The accelerator is based on a systolic array and can handle convolution and max-pooling layer in a combined way or separately using 16 bit floating-point data. It also supports inner-product and LSTM layers. For demonstration and as one of the design verification testbenches, we implemented Yolo-v2 image recognition for 80 object classes. We converted the Yolo-v2 software to 16 bit floating point version and used it in the simulation and FPGA experiment during the chip development. Several other testbenches were designed and used to test various networks. In this paper, the accelerator’s architecture and the Yolo-v2 implementation on our FPGA board including camera and LCD is explained, including the software implementation. Some analyses on the measured and estimated performance is shown.

State-of-the-art neural network accelerators consist of arithmetic engines organized in a mesh structure datapath surrounded by memory blocks that provide neural data to the datapath. While server-based accelerators coupled with server-class processors are accommodated with large silicon area and consume large amounts of power, electronic control units in autonomous driving vehicles require power-optimized, ‘AI processors’ with a small footprint. An AI processor for mobile applications that integrates general-purpose processor cores with mesh-structured neural network accelerators and high speed memory while achieving high-performance with low-power and compact area constraints necessitates designing a novel AI processor architecture. We present the design of an AI processor for electronic systems in autonomous driving vehicles targeting not only CNN-based object recognition but also MLP-based in-vehicle voice recognition. The AI processor integrates Super-Thread-Cores (STC) for neural network acceleration with function-safe general purpose cores that satisfy vehicular electronics safety requirements. The STC is composed of 16384 programmable nano-cores organized in a mesh-grid structured datapath network. Designed based on thorough analysis of neural network computations, the nano-core-in-memory architecture enhances computation intensity of STC with efficient feeding of multi-dimensional activation and kernel data into the nano-cores. The quad function-safe general purpose cores ensure functional safety of Super-Thread-Core to comply with road vehicle safety standard ISO 26262. The AI processor exhibits 32 Tera FLOPS, enabling hyper real-time execution of CNN, RNN, and FCN.

We present a processing platform that implements DMR with separate clock and power sources to prevent dependent failures working with a reconfigurable cache that includes BIST with self-recovering function to detect transient faults and error prediction to prevent permanent faults. The fault tolerant processor is analyzed to be complying with the ISO26262 SOTIF for ADAS SoC which is fabricated with 28nm CMOS Technology. The single point faults metric is 99.64% with a safety mechanism.

Network-based moving target defense systems are a valuable addition to the world of computer security and privacy. Ongoing research attempts to push the boundaries of network-based moving target defenses by adding client/server functionality to the Moving Target IPv6 Defense (MT6D) system. While this additional functionality provides a great many benefits, it is important to fully understand the impact to the network of implementing an MT6D server, which by definition greatly increases the amount of management traffic on a network segment. IPv6 relies on the Neighbor Discovery Protocol (NDP) for all network management functions, including the discovery of currently active addresses. Each address that is bound requires a number of NDP messages to be exchanged before a host can begin using said address. When an MT6D server requires the binding of a large number of IPv6 addresses to a single interface, there is likely to be some negative impact to the network due to the amount of NDP traffic that is generated. This work attempts to quantify the specific impact to the network from NDP traffic generation through the use of ns-3 simulations and live experiments.

This article provides a pre- and post-Katrina snapshot of issues in the human resource arena with emphasis on resilience, persistence, and the overwhelming need for effective planning.

Video applications are becoming ubiquitous in mobile and embedded systems. Wearable video systems such as Google Glasses require capabilities for real-time video analytics and prolonged battery lifetimes. Further, the increasing resolution of image sensors in these mobile systems places an increasing demand on both the memory storage as well as the computational power. In this work, we present the Refresh Enabled Video Analytics (REVA) system, an embedded architecture for multi- object scene understanding and tackle the unique opportunities provided by real-time embedded video analytics applications for reducing the DRAM memory refresh energy. We compare our design with the existing design space and show savings of 88% in refresh power and 15% in total power, as compared to a standard DRAM refresh scheme.

A mobile robot operating in a netcentric environment can utilize offboard resources on the network to improve its local perception. One such offboard resource is a world model built and maintained by other sensor systems. In this paper we present results from research into improving the performance of Deformable Parts Model object detection algorithms by using an offboard 3D world model. Experiments were run for detecting both people and cars in 2D photographs taken in an urban environment. After generating candidate object detections, a 3D world model built from airborne Light Detection and Ranging (LIDAR) and aerial photographs was used to filter out false alarm using several types of geometric reasoning. Comparison of the baseline detection performance to the performance after false alarm filtering showed a significant decrease in false alarms for a given probability of detection.

This paper presents the results of a feasibility study of a novel electric motor/pump system that combines an axial field, permanent magnet motor with a centrifugal pump. This system, unique because the motor permanent magnet (PM) rotor and pump impeller vanes are a single unit, provides a compact, reliable, low-noise, and high-power-density electrically driven centrifugal pump suitable for underwater applications in which minimizing noise, vibration, and volume are major design objectives. Performance tradeoffs for the electromagnetic analysis were made by two- and three-dimensional finite element analysis (FEA) models in conjunction with a lumped parameter magnetic circuit model. The study results reveal that theoretically the new integrated electric motor/pump concept (1) provides up to seventy-three percent electromagnetic efficiency with a significantly reduced volume and weight and (2) substantially increases the power density and reliability with the potential for quiet and high performance. This study is also evidence that new concepts can be explored with expanded computer modeling and simulation techniques.

We present a prototype system which enables users to explore the global structure for digital imagery archives as well as drill-down into individual pictures. Our search engine builds upon computer vision advances made over the past decade in low-level feature matching, large data handling and object recognition. We demonstrate hierarchical clustering among images semi-cooperatively shot around MIT, automatic linking of flickr photos and aerial frames from the Grand Canyon, and video segment identification for a TV broadcast. Moreover, our software tools incorporate visible vs infrared band selection, color content quantization and human face detection. Ongoing and future extensions of this image search system are discussed.

In this paper we present Vortex: a reconfigurable Network-on-Chip platform suitable for implementing domain-specific hardware accelerators in a design efficient manner. Our Vortex platform provides a flexible means to compose domain-specific accelerators for streaming applications such as performance critical machine vision systems. By substituting a traditional shared-bus architecture with low latency packet-switched routers and high utility network adaptors, maximum performance is exploited with minimal regard to communication infrastructure design and validation. To highlight the utility of the Vortex platform we present a case study in which a video analytics pipeline is mapped onto a multi-FPGA system. The system meets real-time throughput requirements on 3 Megapixel 48-bit image sequences with minimal resource overhead attributed to the Vortex communication infrastructure.

Working with 2.3K+ digital images shot around MIT, we form a SIFT graph which imposes an initial topological ordering upon the quasi-random set of input urban photos. We next employ iterative bundle adjustment algorithms developed by Snavely et al to recover the MIT photos' geometrical structure. After georegistering the 2.3K+ images to an aerial ladar map, we plot recovered camera geolocations on a Google Map and conduct virtual tours through the 3D field of 2D urban photos. Similar computer vision techniques are applied to video footage collected over a rural scene by an aerial glider. 1.5K video frames are reconstructed and georegistered via the UAV's GPS track. We demonstrate how several difficult aerial video exploitation problems become tractable when a geometry-based analysis approach is adopted.

In this paper, we propose a flush optimization algorithm for fast protection switching in an Ethernet ring protection (ERP) network. To evaluate performance of our algorithm, we implement our algorithm on ERP in Ethernet switches and experiment on protection switching in an Ethernet ring network. We measure protection switching time under a link failure as increasing the number of traffic flows and compare performance with the general ERP. Experiment results show our algorithm always provides protection switching time less than 30ms even though the number of traffic flows increases.

Gabor filters are widely used to model the behavior of the V1 stage of the primary visual cortex of mammalian vision systems. Acceleration of this filter is critical for various real-time vision applications. In this work, we explore a variety of design options for the Gabor filter to tradeoff resource utilization, performance and accuracy. Our results indicate that our optimizations enable a variety of design options to meet a wide range of application constraints.

Biological vision systems use saliency-based visual attention mechanisms to limit higher-level vision processing on the most visually-salient subsets of an input image. Among several computational models that capture the visual-saliency in biological system, an information theoretic AIM(Attention based on Information Maximization) algorithm has been demonstrated to predict human gaze patterns better than other existing models. We present an FPGA based implementation of this computationally intensive AIM algorithm to support embedded vision applications. Our implementation provides performance of processing about 4M pixels/sec for 25 basis functions with a convolution kernel size of 21 by 21 for each of the R, G, and B color-channels, when implemented on a Virtex-6 LX240T. We also provide an optimization aimed at controlling the trade-off between power consumption and latency, and performance comparisons with a GPU implementation.

Recent work in computer vision has demonstrated the potential to automatically recover camera and scene geometry from large collections of uncooperatively-collected photos. At the same time, aerial ladar and Geographic Information System (GIS) data are becoming more readily accessible. In this paper, we present a system for fusing these data sources in order to transfer 3D and GIS information into outdoor urban imagery. Applying this system to 1000+ pictures shot of the lower Manhattan skyline and the Statue of Liberty, we present two proof-of-concept examples of geometry-based photo enhancement which are difficult to perform via conventional image processing: feature annotation and image-based querying. In these examples, high-level knowledge projects from 3D world-space into georegistered 2D image planes and/or propagates between different photos. Such automatic capabilities lay the groundwork for future real-time labeling of imagery shot in complex city environments by mobile smart phones.

Ethernet ring protection (ERP) introduced by ITU-T G.8032 Recommendation is a new class of packet switched network protection technology that can provides ring automatic protection switch for nested multiple Ethernet rings. An ERP system is required to provide network protection against link and node failures within 50ms. Whenever the protection scheme switches the ring to have a new topology, the standard requires all nodes to flush their filtering data base (FDB) resulting in congestion in the access networks. In this paper we evaluate the protection performance of the current G.8032 scheme and introduce additional novel schemes for performance enhancements in nested multi ring networks.

We extend recent automated computer vision algorithms to reconstruct the global three-dimensional structures for photos and videos shot at fixed points in outdoor city environments. Mosaics of digital stills and embedded videos are georegistered by matching a few of their 2D features with 3D counterparts in aerial ladar imagery. Once image planes are aligned with world maps, abstract urban knowledge can propagate from the latter into the former. We project geotagged annotations from a 3D map into a 2D video stream and demonstrate their tracking buildings and streets in a clip with significant panning motion. We also present an interactive tool which enables users to select city features of interest in video frames and retrieve their geocoordinates and ranges. Implications of this work for future augmented reality systems based upon mobile smart phones are discussed.

We extend recent automated computer vision algorithms to reconstruct the global three-dimensional structures for photos and videos shot at fixed points in outdoor city environments. Mosaics of digital stills and embedded videos are georegistered by matching a few of their 2D features with 3D counterparts in aerial ladar imagery. Once image planes are aligned with world maps, abstract urban knowledge can propagate from the latter into the former. We project geotagged annotations from a 3D map into a 2D video stream and demonstrate their tracking buildings and streets in a clip with significant panning motion. We also present an interactive tool which enables users to select city features of interest in video frames and retrieve their geocoordinates and ranges. Implications of this work for future augmented reality systems based upon mobile smart phones are discussed.

Motivated by the problem of uncovering networks of illicit actors in complex urban environments, we present a prototype system for intuitive navigation of vehicle track data via interacting map and network views. Our system combines 3D geospatial visualization, social network display and interactive track search software, and it provides a multi-touch interface for operators to navigate urban scenes and investigate potentially suspicious vehicle activity. We describe a case study to highlight the system's capabilities using ground truth vehicle data collected during a 2007 urban exercise. This data is most naturally viewed as tracks in space and time. But as cluttered track displays obscure potentially important actor relationships, our system provides a social network picture whose condensed format is easier to interpret. Through coordinated space-time/vehicle network searches, we demonstrate how analysts can uncover "Red" activities of tactical significance.

We assess the impact of supplementing two-dimensional video with three-dimensional geometry for persistent vehicle tracking in complex urban environments. Using recent video data collected over a city with minimal terrain content, we first quantify erroneous sources of automated tracking termination and identify those which could be ameliorated by detailed height maps. They include imagery misregistration, roadway occlusion and vehicle deceleration. We next develop mathematical models to analyze the tracking value of spatial geometry knowledge in general and high resolution ladar imagery in particular. Simulation results demonstrate how 3D information could eliminate large numbers of false tracks passing through impenetrable structures. Spurious track rejection would permit Kalman filter coasting times to be significantly increased. Track lifetimes for vehicles occluded by trees and buildings as well as for cars slowing down at corners and intersections could consequently be prolonged. We find high resolution 3D imagery can ideally yield an 83% reduction in the rate of automated tracking failure.

A prototype image processing system has recently been developed which generates, displays and analyzes threedimensional ladar data in real time. It is based upon a suite of novel algorithms that transform raw ladar data into cleaned 3D images. These algorithms perform noise reduction, ground plane identification, detector response deconvolution and illumination pattern renormalization. The system also discriminates static from dynamic objects in a scene. In order to achieve real-time throughput, we have parallelized these algorithms on a Linux cluster. We demonstrate that multiprocessor software plus Blade hardware result in a compact, real-time imagery generation adjunct to an operating ladar. Finally, we discuss several directions for future work, including automatic recognition of moving people, real-time reconnaissance from mobile platforms, and fusion of ladar plus video imagery. Such enhancements of our prototype imaging system can lead to multiple military and civilian applications of national importance.

The objective of this study was to explore the hypothesis that osteoporotic-like (OP) conditions have a negative effect on osseointegration (OI) of dental implants. Using an ovariectomized (OVX) rat model, the extent of OI using histologic and histomorphometric analysis (HMA) under a variety of OVX conditions was assessed. Five experimental groups (n = 7 rats per group) were used: 1) OP control, 2) OI control, () OI followed by OVX treatment to induce OP (OI-->OP), 4) OP induction followed by OI (OP-->OI), and 5) OP induction simultaneously with OI (OI = OP). Using undecalcified plastic-embedded cross-sections of the implant site, HMA was performed to determine the percent of bone contact (BC) at the implant-tissue interface and percent of bone area (BA) immediately (1.5-mm diameter) surrounding the implant site. The presence of Bone Sialoprotein (BSP), an important extracellular matrix component of bone, was evaluated using immunohistochemical staining procedures. The implant control resulted in the highest level of OI (BC = 79%; BA = 87%), whereas all groups in which OVX was performed resulted in a significant reduction in BA (70-75%). High levels of BC were observed in established OP conditions (OP-->OI; BC = 79%); however, following OI, induction of OP conditions (OI-->OP) led to a significant reduction in BC (50%). In each of the OP treatment groups, a diminution of cortical bone, increased trabecularization of the host bone site, and loss of staining of BSP was observed. The results of this work indicate that although OI is possible under a variety of OP-like conditions simulating implant placement, the long-term biomechanical stability of implants under these conditions could be compromised and remains unclear. Further research to understand implant use in the complex bone environment under OP-like conditions is encouraged.

Over the past three decades, the pulp and paper industry has been one of the fastest growing industries of Korea. The country's paper and paperboard consumption has grown at a rate of 14% per year from 0.05 million M/T in 1960s to 4.52 million M/T in 1990s, which is greater than the growth. rate of the general economy of the country.With the limited potential of its own resources, the pulp and paper industry is heavily dependent upon the fiber resources of foreign, countries. The lack of fiber has led to an imbalance in the development of pulp and paper industry-few pulp mills and many paper mills. As a result, most paper mills rely heavily on imported pulp and wastepaper. This poor resource endowment has led the paper industry to adopt a technology which uses a high ratio of wastepaper upto 55%.