Query: Parasitic Inductance and Capacitance-Assisted Active Gate Driving Technique to Minimize Switching Loss of SiC MOSFET

Relevant passages:
A di/dt Feedback-Based Active Gate Driver for Smart Switching and Fast Overcurrent Protection of IGBT Modules <=SEP=> This paper presents an active gate driver (AGD) for IGBT modules to improve their overall performance under normal condition as well as fault condition. Specifically, during normal switching transients, a di/dt feedback controlled current source and current sink is introduced together with a push-pull buffer for dynamic gate current control. Compared to a conventional gate drive strategy, the proposed one has the capability of reducing the switching loss, delay time, and Miller plateau duration during turn-on and turn-off transient without sacrificing current and voltage stress. Under overcurrent condition, it provides a fast protection function for IGBT modules based on the evaluation of fault current level through the di/dt feedback signal. Moreover, the AGD features flexible protection modes, which overcomes the interruption of converter operation in the event of momentary short circuits. A step-down converter is built to evaluate the performance of the proposed driving schemes under various conditions, considering variation of turn-on/off gate resistance, current levels, and short-circuit fault types. Experimental results and detailed analysis are presented to verify the feasibility of the proposed approach.

High Switching Performance of 1700-V, 50-A SiC Power MOSFET Over Si IGBT/BiMOSFET for Advanced Power Conversion Applications <=SEP=> Due to wider band gap of silicon carbide (SiC) compared to silicon (Si), MOSFET made in SiC has considerably lower drift region resistance, which is a significant resistive component in high-voltage power devices. With low on-state resistance and its inherently low switching loss, SiC MOSFETs can offer much improved efficiency and compact size for the converter compared to those using Si devices. In this paper, we report switching performance of a new 1700-V, 50-A SiC MOSFET designed and developed by Cree, Inc. Hard-switching losses of the SiC MOSFETs with different circuit parameters and operating conditions are measured and compared with the 1700-V Si BiMOSFET and 1700-V Si IGBT, using same test set-up. Based on switching and conduction losses, the operating boundary of output power and switching frequency of these devices are found out in a dc-dc boost converter and compared. The switching dv/dts and di/dts of SiC MOSFET are captured and discussed in the perspective of converter design. To validate the continuous operation, three dc-dc boost converters using these devices, are designed and tested at 10 kW of power with 1 kV of output voltage and 10 kHz of switching frequency. 1700V SiC Schottky diode is used as the blocking diode in each case. Corresponding converter efficiencies are evaluated and the junction temperature of each device is estimated. To demonstrate high switching frequency operation, the SiC MOSFET is switched upto 150 kHz within permissible junction temperature rise. A switch combination of the 1700-V SiC MOSFET and 1700-V SiC Schottky diode connected in series is also evaluated for zero voltage switching turn-ON behavior and compared with those of bipolar Si devices. Results show substantial power loss saving with the use of SiC MOSFET.

Turn-on Performance of Reverse Blocking IGBT (RB IGBT) and Optimization Using Advanced Gate Driver <=SEP=> Turn-on performance of a reverse blocking insulated gate bipolar transistor (RB IGBT) is discussed in this paper. The RB IGBT is a specially designed IGBT having ability to sustain blocking voltage of both the polarities. Such a switch shows superior conduction but much worst switching (turn- on) performances compared to a combination of an ordinary IGBT and blocking diode. Because of that, optimization of the switching performance is a key issue that makes the RB IGB not well accepted in the real applications. In this paper, the RB IGBT turn-on losses and reverse recovery current are analyzed for different gate driver techniques, and a new gate driver is proposed. Commonly used conventional gate drivers do not have capability for the switching dynamics optimization. In contrast to this, the new proposed gate driver provides robust and simple way to control and optimize the reverse recovery current and turn-on losses. The collector current slope and reverse recovery current are controlled by the means of the gate emitter voltage control in feedforward manner. In addition, the collector emitter voltage slope is controlled during the voltage falling phase by the means of inherent increase of the gate current. Therefore, the collector emitter voltage tail and the total turn- on losses are reduced, independently on the reverse recovery current. The proposed gate driver was experimentally verified and the results presented and discussed.

High performance active gate drive for high power IGBTs <=SEP=> This paper deals with an active gate drive (AGD) technology for high power IGBTs. It is based on an optimal combination of several requirements necessary for good switching performance under hard switching conditions. The scheme specifically combines together the slow drive requirements for low noise and switching stress and the fast drive requirements for high speed switching and low switching energy loss. The gate drive can also effectively dampen oscillations during low current turn-on transients in the IGBT. This paper looks at the conflicting requirements of the conventional gate drive circuit design and demonstrates, using experimental results, that the proposed three-stage active gate drive technique can be an effective solution.

A Novel Driving and Protection Circuit for Reverse-Blocking IGBT Used in Matrix Converter <=SEP=> A novel drive and protection circuit for reverse-blocking insulated gate bipolar transistor (RB-IGBT) is proposed in this paper. For the drive circuit, a dynamic current source is introduced to reduce the turn-on and turn-off transients. Meanwhile, the di/dt of the collector current and the dv/dt of the collector-emitter voltage are strictly restricted, so do the respective stresses. The drive circuit consists of a conventional push-pull driver and two controllable current sources-a current generator and a current sink. These two current sources work in switching transitions. For the protection circuit, a novel collector current detecting circuit suitable for RB-IGBT is proposed. This method detects the collector current by sensing collector-emitter voltage of the device. Further study shows that this method can be used to acquire the current signs in commutation transitions of matrix converter. A series of experiments has been carried out concerning the proposed drive and protection circuit and the experimental setup; results as well as detailed analysis are presented

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection <=SEP=> Convolutional neural networks have been successfully applied to semantic segmentation problems. However, there are many problems that are inherently not pixel-wise classification problems but are nevertheless frequently formulated as semantic segmentation. This ill-posed formulation consequently necessitates hand-crafted scenario-specific and computationally expensive post-processing methods to convert the per pixel probability maps to final desired outputs. Generative adversarial networks (GANs) can be used to make the semantic segmentation network output to be more realistic or better structure-preserving, decreasing the dependency on potentially complex post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate the discussed problem using an embedding loss. With EL-GAN, we discriminate based on learned embeddings of both the labels and the prediction at the same time. This results in much more stable training due to having better discriminative information, benefiting from seeing both ‘fake’ and ‘real’ predictions at the same time. This substantially stabilizes the adversarial training process. We use the TuSimple lane marking challenge to demonstrate that with our proposed framework it is viable to overcome the inherent anomalies of posing it as a semantic segmentation problem. Not only is the output considerably more similar to the labels when compared to conventional methods, the subsequent post-processing is also simpler and crosses the competitive 96% accuracy threshold.

A single nutrient feed supports both chemically defined NS0 and CHO fed-batch processes: Improved productivity and lactate metabolism. <=SEP=> A chemically defined nutrient feed (CDF) coupled with basal medium preloading was developed to replace a hydrolysate-containing feed (HCF) for a fed-batch NS0 process. The CDF not only enabled a completely chemically defined process but also increased recombinant monoclonal antibody titer by 115%. Subsequent tests of CDF in a CHO process indicated that it could also replace the hydrolysate-containing nutrient feed in this expression system as well as providing an 80% increase in product titer. In both CDF NS0 and CHO processes, the peak lactate concentrations were lower and, more interestingly, lactate metabolism shifted markedly from net production to net consumption when cells transitioned from exponential to stationary growth phase. Subsequent investigations of the lactate metabolic shift in the CHO CDF process were carried out to identify the cause(s) of the metabolic shift. These investigations revealed several metabolic features of the CHO cell line that we studied. First, glucose consumption and lactate consumption are strictly complementary to each other. The combined cell specific glucose and lactate consumption rate was a constant across exponential and stationary growth phases. Second, Lactate dehydrogenase (LDH) activity fluctuated during the fed-batch process. LDH activity was at the lowest when lactate concentration started to decrease. Third, a steep cross plasma membrane glucose gradient exists. Intracellular glucose concentration was more than two orders of magnitude lower than that in the medium. Fourth, a large quantity of citrate was diverted out of mitochondria to the medium, suggesting a partially truncated tricarboxylic acid (TCA) cycle in CHO cells. Finally, other intermediates in or linked to the glycolytic pathway and the TCA cycle, which include alanine, citrate, isocitrate, and succinate, demonstrated a metabolic shift similar to that of lactate. Interestingly, all these metabolites are either in or linked to the pathway downstream of pyruvate, but upstream of fumarate in glucose metabolism. Although the specific mechanisms for the metabolic shift of lactate and other metabolites remain to be elucidated, the increased understanding of the metabolism of CHO cultures could lead to future improvements in medium and process development.

Automated Assembly Using 3D and 2D Cameras <=SEP=> 2D and 3D computer vision systems are frequently being used in automated production to detect and determine the position of objects. Accuracy is important in the production industry, and computer vision systems require structured environments to function optimally. For 2D vision systems, a change in surfaces, lighting and viewpoint angles can reduce the accuracy of a method, maybe even to a degree that it will be erroneous, while for 3D vision systems, the accuracy mainly depends on the 3D laser sensors. Commercially available 3D cameras lack the precision found in high-grade 3D laser scanners, and are therefore not suited for accurate measurements in industrial use. In this paper, we show that it is possible to identify and locate objects using a combination of 2D and 3D cameras. A rough estimate of the object pose is first found using a commercially available 3D camera. Then, a robotic arm with an eye-in-hand 2D camera is used to determine the pose accurately. We show that this increases the accuracy to < 1 mm and < 1◦. This was demonstrated in a real industrial assembly task where high accuracy is required.

STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset <=SEP=> In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.

Learning to Pinpoint Singing Voice from Weakly Labeled Examples <=SEP=> Building an instrument detector usually requires temporally accurate ground truth that is expensive to create. However, song-wise information on the presence of instruments is often easily available. In this work, we investigate how well we can train a singing voice detection system merely from song-wise annotations of vocal presence. Using convolutional neural networks, multipleinstance learning and saliency maps, we can not only detect singing voice in a test signal with a temporal accuracy close to the state-of-the-art, but also localize the spectral bins with precision and recall close to a recent source separation method. Our recipe may provide a basis for other sequence labeling tasks, for improving source separation or for inspecting neural networks trained on auditory spectrograms.

High-Dimensional Single-Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease. <=SEP=> Individual reports suggest that the central nervous system (CNS) contains multiple immune cell types with diverse roles in tissue homeostasis, immune defense, and neurological diseases. It has been challenging to map leukocytes across the entire brain, and in particular in pathology, where phenotypic changes and influx of blood-derived cells prevent a clear distinction between reactive leukocyte populations. Here, we applied high-dimensional single-cell mass and fluorescence cytometry, in parallel with genetic fate mapping systems, to identify, locate, and characterize multiple distinct immune populations within the mammalian CNS. Using this approach, we revealed that microglia, several subsets of border-associated macrophages and dendritic cells coexist in the CNS at steady state and exhibit disease-specific transformations in the immune microenvironment during aging and in models of Alzheimer's disease and multiple sclerosis. Together, these data and the described framework provide a resource for the study of disease mechanisms, potential biomarkers, and therapeutic targets in CNS disease.

EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. <=SEP=> Two experiments were designed to examine the effects of attentional demands on the electroencephalogram during cognitive and emotional tasks. We found an interaction of task with hemisphere as well as more overall parietal alpha for tasks not requiring attention to the environment, such as mental arithmetic, than for those requiring such attention. Differential hemispheric activation for beta was found most strongly in the temporal areas for emotionally positive or negative tasks and in the parietal areas for cognitive tasks.

The possibility of predicting learning performance using features of note taking activities and instructions in a blended learning environment <=SEP=> A formative assessment was introduced to a blended learning course in order to predict participant’s learning performance using measurements of their note taking activity and metrics of their attitudes. The lexical metrics were created by analyzing the contents of student’s notes from every class, and by measuring their characteristics. In the results of two types of regression analysis of these measurements, features of note taking activities were a significant source of information for predicting the scores of final exams. In regards to temporal changes in prediction performance during the course, the progress of learning and the typical changes in features of note taking activity were discussed. During the analyses, the effectiveness of the lecturer’s note taking instructions was also confirmed.

Correlation analysis of MQTT loss and delay according to QoS level <=SEP=> MQTT is an open protocol developed and released by IBM. To ensure the reliability of message transmission, MQTT supports three levels of QoS. In this paper, we analyze MQTT message transmission process which consists of real wired/wireless publish client, broker server and Subscribe client. By transmitting messages through 3 levels of QoS with various sizes of payloads, we have captured packets to analyze end-to-end delays and message loss.

Improved resolution from subpixel shifted pictures <=SEP=> In this paper we consider the problem of obtaining a picture with improved resolution from an ensemble of K spatially shifted low-resolution pictures. We show that it can be done and provide a procedure for its implementations. The framework and scheme utilized in the generalized multichannel sampling theorem of Papoulis and Brown was found to be an appropriate representation for our problem. The linear channels of this scheme correspond to the processes that produce the low-resolution pictures (by blur and shifts) and to the process of reconstruction. The total process is shown to consist of two distinct degradation-restoration pairs: blurring-deblurring and undersampling-merging. This paper discusses merging. Deblurring (deconvolution) can be done by any desired scheme independent of merging. It is shown that the procedure achieves the claims of super resolution schemes but, unlike many of them, is well posed and robust. This procedure holds for a selected ensemble so that K is not too large and no two of the pictures practially coincide. The number of multiplications per pixel of the improved picture can be reduced to O(K2). o ESJZ

Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports <=SEP=> OBJECTIVE
To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports.


METHODS



DESIGN
Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured.


OUTCOME MEASURES
Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia.


RESULTS
Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference.


CONCLUSION
In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.

Valuation of IT Investments Using Real Options Theory <=SEP=> Real Options Theory is often applied to the valuation of IT investments. The application of Real Options Theory is generally accompanied by a monetary valuation of real options through option pricing models which in turn are based on restrictive assumptions and thus subject to criticism. Therefore, this paper analyzes the application of option pricing models for the valuation of IT investments. A structured literature review reveals the types of IT investments which are valuated with Real Options Theory in scientific literature. These types of IT investments are further investigated and their main characteristics are compared to the restrictive assumptions of traditional option pricing models. This analysis serves as a basis for further discussion on how the identified papers address these assumptions. The results show that a lot of papers do not account for critical assumptions, although it is known that the assumptions are not fulfilled. Moreover, the type of IT investment determines the criticality of the assumptions. Additionally, several extensions or adaptions of traditional option pricing models can be found which provide the possibility of relaxing critical assumptions. Researchers can profit from the results derived in this paper in two ways: First, which assumptions can be critical for various types of IT investments are demonstrated. Second, extensions of option pricing models that relax critical assumptions are introduced.

Coupled hidden Markov models for complex action recognition <=SEP=> We present algorithms for coupling and training hidden Markov models ( HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However, the Markovian framework makes strong restrictive assumptions about the system generating the signal—that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.

THE UBISENSE SMART SPACE PLATFORM <=SEP=> Ubisense has developed a platform for building Smart Space applications. The platform addresses the key requirements for building Smart Spaces: accurate 3D positioning, scalable realtime performance, development and deployment tools. This paper deepens the key requirements and describes how the Ubisense platform components meets them. The demonstration exemplifies the smart space platform by tracking players in a game application. The Ubisense Smart Space Platform Ubisense has developed a platform for building Smart Space applications. Our platform addresses the key requirements for building Smart Spaces: Accurate 3D positioning supports applications that can perceive the physical relationships between people and objects in the environment Scalable real-time performance enables arbitrary numbers of applications, used by arbitrary numbers of people over an arbitrarily-large area Development and deployment tools make it easy to design, implement, and manage Smart Space applications. The demonstration shows a Smart Space containing applications that integrate with external software (a simple game that users control by moving around), and devices (a PTZ camera that keeps users in shot while they are playing the game) Fig. 1 Smart space demonstration setup 1 Ubisense, St Andrews House, 90 St Andrews Road, Chesterton, CB4 1DL, United Kingdom, http://www.ubisense.net

The source code control system <=SEP=> The Source Code Control System (SCCS) is a software tool designed to help programming projects control changes to source code. It provides facilities for storing, updating, and retrieving all versions of modules, for controlling updating privileges for identifying load modules by version number, and for recording who made each software change, when and where it was made, and why. This paper discusses the SCCS approach to source code control, shows how it is used and explains how it is implemented.

Scheduling Real-Time Transactions: A Performance Evaluation <=SEP=> Managing transactions with real-time requirements presents many new problems. In this paper we address several: How can we schedule transactions with deadlines? How do the real-time constraints affect concurrency control? How should overloads be handled? How does the scheduling of 1/0 requests affect the timeliness of transactions? How should exclusive and shared locking be handled? We describe a new group of algorithms for scheduling real-time transactions that produce serializable schedules. We present a model for scheduling transactions with deadlines on a single processor disk resident database system, and evaluate the scheduling algorithms through detailed simulation experiments.

Magnetic field effects on plant growth, development, and evolution <=SEP=> The geomagnetic field (GMF) is a natural component of our environment. Plants, which are known to sense different wavelengths of light, respond to gravity, react to touch and electrical signaling, cannot escape the effect of GMF. While phototropism, gravitropism, and tigmotropism have been thoroughly studied, the impact of GMF on plant growth and development is not well-understood. This review describes the effects of altering magnetic field (MF) conditions on plants by considering plant responses to MF values either lower or higher than those of the GMF. The possible role of GMF on plant evolution and the nature of the magnetoreceptor is also discussed.

Attentive Tensor Product Learning for Language Generation and Grammar Parsing <=SEP=> This paper proposes a new architecture — Attentive Tensor Product Learning (ATPL) — to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via TPR-based deep neural network; 2) employing attention modules to compute TPR; and 3) integration of TPR with typical deep learning architectures including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN). The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. This ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a sentence. Experimental results demonstrate the effectiveness of the proposed approach.

Interrater reliability of a modified Ashworth scale of muscle spasticity. <=SEP=> We undertook this investigation to determine the interrater reliability of manual tests of elbow flexor muscle spasticity graded on a modified Ashworth scale. We each independently graded the elbow flexor muscle spasticity of 30 patients with intracranial lesions. We agreed on 86.7% of our ratings. The Kendall's tau correlation between our grades was .847 (p less than .001). Thus, the relationship between the raters' judgments was significant and the reliability was good. Although the results were limited to the elbow flexor muscle group, we believe them to be positive enough to encourage further trials of the modified Ashworth scale for grading spasticity.

Improved Plagiarism Detection Algorithm Based on Abstract Syntax Tree <=SEP=> Statements with conditionals are widely used in C, C++ and java, such as if and while statements and they are easy to plagiarize by adjusting the logical structure of the corresponding statements. However, the existing relative algorithms and tools cannot effectively detect code plagiarism of these statements. This paper puts forward an improved code plagiarism detection algorithm based on abstract syntax tree. The algorithm calculates the hash value for each node of the abstract syntax tree, and compares the hash values node by node. Based on this, it analyzes the if-statement plagiarism, as if-statements are representative in the statements with conditionals, and puts forward the corresponding detection schemes in order to detect plagiarism effectively. After that, with the results of many experiments, the algorithm is proved effective on detecting if-statement plagiarisms.

Penile Implants among Prisoners—A Cause for Concern? <=SEP=> BACKGROUND
We report the prevalence of penile implants among prisoners and determine the independent predictors for having penile implants. Questions on penile implants were included in the Sexual Health and Attitudes of Australian Prisoners (SHAAP) survey following concerns raised by prison health staff that increasing numbers of prisoners reported having penile implants while in prison.


METHODS
Computer-Assisted Telephone Interviewing (CATI) of a random sample of prisoners was carried out in 41 prisons in New South Wales and Queensland (Australia). Men were asked, "Have you ever inserted or implanted an object under the skin of your penis?" If they responded Yes: "Have you ever done so while you were in prison?" Univariate logistic regression and logistic regression were used to determine the factors associated with penile implants.


RESULTS
A total of 2,018 male prisoners were surveyed, aged between 18 and 65 years, and 118 (5.8%) reported that they had inserted or implanted an object under the skin of their penis. Of these men, 87 (73%) had this done while they were in prison. In the multivariate analysis, a younger age, birth in an Asian country, and prior incarceration were all significantly associated with penile implants (p<0.001). Men with penile implants were also more likely to report being paid for sex (p<0.001), to have had body piercings (p<0.001) or tattoos in prison (p<0.001), and to have taken non-prescription drugs while in prison (p<0.05).


CONCLUSIONS
Penile implants appear to be fairly common among prisoners and are associated with risky sexual and drug use practices. As most of these penile implants are inserted in prison, these men are at risk of blood borne viruses and wound infection. Harm reduction and infection control strategies need to be developed to address this potential risk.

Air-filled substrate integrated waveguide — A flexible and low loss technological platform <=SEP=> In this paper, the air-filled substrate integrated waveguide (AFSIW), offering a technological platform for the design of high performance millimeterwave self-packaged substrate integrated components and systems, is presented. AFSIW is based on low-cost printed circuit board (PCB). Several components, including antennas, filters and couplers, have been theoretically studied and experimentally demonstrated up to 60 GHz. This technology, surpassing its dielectric-filled SIW (DFSIW) counterpart, is expected by the authors to replace the rectangular waveguide technology for future low-cost extremely high data rate communication systems and highly sensitive sensors operating at millimeterwave and beyond.

Attention Deficit Hyperactivity Disorder: Is There an App for That? Suitability Assessment of Apps for Children and Young People With ADHD <=SEP=> BACKGROUND
Attention-deficit/hyperactivity disorder (ADHD) is a complex highly comorbid disorder, which can have a huge impact on those with ADHD, their family, and the community around them. ADHD is currently managed using pharmacological and nonpharmacological interventions. However, with advances in technology and an increase in the use of mobile apps, managing ADHD can be augmented using apps specifically designed for this population. However, little is known regarding the suitability and usability of currently available apps.


OBJECTIVE
The aim of this study was to explore the suitability of the top 10 listed apps for children and young people with ADHD and clinicians who work with them. It is hypothesized that mobile apps designed for this population could be more suitably designed for this population.


METHODS
The top 10 listed apps that are specifically targeted toward children and young people with ADHD in the United Kingdom were identified via the Google Play (n=5) and iTunes store (n=5). Interviews were then undertaken with 5 clinicians who specialize in treating this population and 5 children and young people with ADHD themselves, to explore their opinions of the 10 apps identified and what they believe the key components are for apps to be suitable for this population.


RESULTS
Five themes emerged from clinician and young people interviews: the accessibility of the technology, the importance of relating to apps, addressing ADHD symptoms and related difficulties, age appropriateness, and app interaction. Three additional themes emerged from the clinician interviews alone: monitoring symptoms, side effects and app effect on relationships, and the impact of common comorbid conditions. The characteristics of the apps did not appear to match well with the views of our sample.


CONCLUSIONS
These findings suggest that the apps may not be suitable in meeting the complex needs associated with this condition. Further research is required to explore the value of apps for children and young people with ADHD and their families and, in particular, any positive role for apps in the management of ADHD in this age group. A systematic review on how technology can be used to engage this population and how it can be used to help them would be a useful way forward. This could be the platform to begin exploring the use of apps further.

Scale Up vs. Scale Out in Cloud Storage and Graph Processing Systems <=SEP=> Deployers of cloud storage and iterative processing systems typically have to deal with either dollar budget constraints or throughput requirements. This paper examines the question of whether such cloud storage and iterative processing systems are more cost-efficient when scheduled on a COTS (scale out) cluster or a single beefy (scale up) machine. We experimentally evaluate two systems: 1) a distributed key-value store (Cassandra), and 2) a distributed graph processing system (Graph Lab). Our studies reveal scenarios where each option is preferable over the other. We provide recommendations for deployers of such systems to decide between scale up vs. Scale out, as a function of their dollar or throughput constraints. Our results indicate that there is a need or adaptive scheduling in heterogeneous clusters containing scale up and scale out nodes.

Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory <=SEP=> Very high resolution satellite images provide valuable information to researchers. Among these, urban-area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose the use of scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationships between these vertices (such as spatial distance and intensity values) lead to the edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic 1-m-resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings.