Query: Image Processing on DSP Environment Using OpenCV

Relevant passages:
Digital Image Processing <=SEP=> Image defects which could be caused by the digitization process or by faults in the imaging set-up (for example, bad lighting) can be corrected using Image Enhancement techniques. Once the image is in good condition, the Measurement Extraction operations can be used to obtain useful information from the image. Course Objectives “Introduction to Digital Image Processing” is two-day hands-on course that provide fundamental concepts and techniques for digital image processing and the software principles used in their practical implementation.

Real-time computer vision with OpenCV <=SEP=> Mobile computer-vision technology will soon become as ubiquitous as touch interfaces.

Python for Prototyping Computer Vision Applications <=SEP=> Python is a popular language widely adopted by the scientific community due to its clear syntax and an extensive number of specialized packages. For image processing or computer vision development, two libraries are prominently used: NumPy/SciPy and OpenCV with a Python wrapper. In this paper, we present a comparative evaluation of both libraries, assessing their performance and their usability. We also investigate the performance of OpenCV when accessed through a python wrapper versus directly using the native C implementation.

Python for Scientific Computing <=SEP=> Python is an excellent "steering" language for scientific codes written in other languages. However, with additional basic tools, Python transforms into a high-level language suited for scientific and engineering code that's often fast enough to be immediately useful but also flexible enough to be sped up with additional extensions.

Image Alignment and Stitching : A Tutorial 1 <=SEP=> This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. This tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce seamless mosaics. It closes with a discussion of open research problems in the area.

Substrate-Independent Microwave Components in Substrate Integrated Waveguide Technology for High-Performance Smart Surfaces <=SEP=> Although all existing air-filled substrate integrated waveguide (AFSIW) topologies yield a substrate-independent electrical performance, they rely on dedicated, expensive, laminates to form air-filled regions that contain the electromagnetic fields. This paper proposes a novel substrate-independent AFSIW manufacturing technology, enabling straightforward integration of high-performance microwave components into a wide range of general-purpose commercially available surface materials by means of standard additive (3-D printing) or subtractive (computer numerically controlled milling/laser cutting) manufacturing processes. First, an analytical formula is derived for the effective permittivity and loss tangent of the AFSIW waveguide. This allows the designer to reduce substrate losses to levels typically encountered in high-frequency laminates. Then, several microwave components are designed and fabricated. Measurements of multiple AFSIW waveguides and a four-way power divider/combiner, both relying on a new coaxial-to-air-filled SIW transition, prove that this novel approach yields microwave components suitable for direct integration into everyday surfaces, with low insertion loss, and excellent matching and isolation over the entire [5.15–5.85] GHz band. Hence, this innovative approach paves the way for a new generation of cost-effective, high-performance, and invisibly integrated smart surface systems that efficiently exploit the area and the materials available in everyday objects.

VIPS: a Vision-based Page Segmentation Algorithm <=SEP=> A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. It simulates how a user understands web layout structure based on his visual perception. Comparing to other existing techniques, our approach is independent to underlying documentation representation such as HTML and works well even when the HTML structure is far different from layout structure. Experiments show satisfactory results.

Modality Effects in Deception Detection and Applications in Automatic-Deception-Detection <=SEP=> Modality is an important context factor in deception, which is context-dependent. In order to build a reliable and flexible tool for automatic-deception-detection (ADD), we investigated the characteristics of verbal cues to deceptive behavior in three modalities: text, audio and face-to-face communication. Seven categories of verbal cues (21 cues) were studied: quantity, complexity, diversity, verb nonimmediacy, uncertainty, specificity and affect. After testing the interaction effects between modality and condition (deception or truth), we found significance only with specificity and observed that differences between deception and truth were in general consistent across the three modalities. However, modality had strong effects on verbal cues. For example, messages delivered face-to-face were largest in quantity (number of words, verbs, and sentences), followed by the audio modality. Text had the sparsest examples. These modality effects are an important factor in building baselines in ADD tools, because they make it possible to use them to adjust the baseline for an unknown modality according to a known baseline, thereby simplifying the process of ADD. The paper discusses in detail the implications of these findings on modality effects in three modalities.

Model-based clustering of high-dimensional data: A review <=SEP=> Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays more and more frequent and, unfortunately, classical model-based clustering techniques show a disappointing behavior in high-dimensional spaces. This is mainly due to the fact that model-based clustering methods are dramatically over-parametrized in this case. However, high-dimensional spaces have specific characteristics which are useful for clustering and recent techniques exploit those characteristics. After having recalled the bases of model-based clustering, this article will review dimension reduction approaches, regularization-based techniques, parsimonious modeling, subspace clustering methods and clustering methods based on variable selection. Existing softwares for model-based clustering of high-dimensional data will be also reviewed and their practical use will be illustrated on real-world data sets.

Categorical Data Clustering using Frequency and Tf-Idf based Cosine Similarity <=SEP=> Clustering is the process of grouping a set of physical objects into classes of similar object. Objects in real world consist of both numerical and categorical data. Categorical data are not analyzed as numerical data due to the lack of inherit ordering. This paper describes Frequency and Tf-Idf Based Categorical Data Clustering (FTCDC) technique based on cosine similarity measure. The FTCDC system consists of four modules, such as data pre-processing, similarity matrix generation, cluster formation and validation. The System architecture of FTCDC is explained and its performance is examined using a simple example scenario. The performance on real world data is measured using accuracy and error rate. The performance of the system much relay on the similarity threshold selected for the clustering process.

Transcription Methods for Consistency, Volume and Efficiency <=SEP=> This paper describes recent efforts at Linguistic Data Consortium at the University of Pennsylvania to create manual transcripts as a shared resource for human language technology research and evaluation. Speech recognition and related technologies in particular call for substantial volumes of transcribed speech for use in system development, and for human gold standard references for evaluating performance over time. Over the past several years LDC has developed a number of transcription approaches to support the varied goals of speech technology evaluation programs in multiple languages and genres. We describe each transcription method in detail, and report on the results of a comparative analysis of transcriber consistency and efficiency, for two transcription methods in three languages and five genres. Our findings suggest that transcripts for planned speech are generally more consistent than those for spontaneous speech, and that careful transcription methods result in higher rates of agreement when compared to quick transcription methods. We conclude with a general discussion of factors contributing to transcription quality, efficiency and consistency.

GraphP: Reducing Communication for PIM-Based Graph Processing with Efficient Data Partition <=SEP=> Processing-In-Memory (PIM) is an effective technique that reduces data movements by integrating processing units within memory. The recent advance of “big data” and 3D stacking technology make PIM a practical and viable solution for the modern data processing workloads. It is exemplified by the recent research interests on PIM-based acceleration. Among them, TESSERACT is a PIM-enabled parallel graph processing architecture based on Micron’s Hybrid Memory Cube (HMC), one of the most prominent 3D-stacked memory technologies. It implements a Pregel-like vertex-centric programming model, so that users could develop programs in the familiar interface while taking advantage of PIM. Despite the orders of magnitude speedup compared to DRAM-based systems, TESSERACT generates excessive crosscube communications through SerDes links, whose bandwidth is much less than the aggregated local bandwidth of HMCs. Our investigation indicates that this is because of the restricted data organization required by the vertex programming model. In this paper, we argue that a PIM-based graph processing system should take data organization as a first-order design consideration. Following this principle, we propose GraphP, a novel HMC-based software/hardware co-designed graph processing system that drastically reduces communication and energy consumption compared to TESSERACT. GraphP features three key techniques. 1) “Source-cut” partitioning, which fundamentally changes the cross-cube communication from one remote put per cross-cube edge to one update per replica. 2) “Two-phase Vertex Program”, a programming model designed for the “source-cut” partitioning with two operations: GenUpdate and ApplyUpdate. 3) Hierarchical communication and overlapping, which further improves performance with unique opportunities offered by the proposed partitioning and programming model. We evaluate GraphP using a cycle accurate simulator with 5 real-world graphs and 4 algorithms. The results show that it provides on average 1.7 speedup and 89% energy saving compared to TESSERACT.

An Efficient Privacy-Preserving Ranked Keyword Search Method <=SEP=> Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.

Architectural optimizations for high performance and energy efficient Smith-Waterman implementation on FPGAs using OpenCL <=SEP=> Smith-Waterman is a dynamic programming algorithm that plays a key role in the modern genomics pipeline as it is guaranteed to find the optimal local alignment between two strings of data. The state of the art presents many hardware acceleration solutions that have been implemented in order to exploit the high degree of parallelism available in this algorithm. The majority of these implementations use heuristics to increase the performance of the system at the expense of the accuracy of the result. In this work, we present an implementation of the pure version of the algorithm. We include the key architectural optimizations to achieve highest possible performance for a given platform and leverage the Berkeley roofline model to track the performance and guide the optimizations. To achieve scalability, our custom design comprises of systolic arrays, data compression features and shift registers, while a custom port mapping strategy aims to maximize performance. Our designs are built leveraging an OpenCL-based design entry, namely Xilinx SDAccel, in conjunction with a Xilinx Virtex 7 and Kintex Ultrascale platform. Our final design achieves a performance of 42.47 GCUPS (giga cell updates per second) with an energy efficiency of 1.6988 GCUPS/W. This represents an improvement of 1.72x in performance and energy efficiency over previously published FPGA implementations and 8.49x better in energy efficiency over comparable GPU implementations.

An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics <=SEP=> MOTIVATION
The field of phylodynamics focuses on the problem of reconstructing population size dynamics over time using current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g. influenza virus. Phylodynamic inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. Although this approach is quite powerful, large datasets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand inferential methods with relatively low computational cost.


RESULTS
To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function, we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm.


AVAILABILITY AND IMPLEMENTATION
The R code for all simulation studies and real data analysis conducted in this article are publicly available at http://www.ics.uci.edu/∼slan/lanzi/CODES.html and in the R package phylodyn available at https://github.com/mdkarcher/phylodyn.


CONTACT
S.Lan@warwick.ac.uk or babaks@uci.edu


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.

Multiuser Millimeter Wave Communications With Nonorthogonal Beams <=SEP=> Recently, millimeter-wave (mmWave) and even terahertz wireless (with higher frequency) networks have attracted significant research interests as alternatives to support the formidable growth of wireless communication services. Normally, directional beamforming (BF) is shown as a promising technique to compensate its high path loss. We focus on mmWave communications and assume that both mmWave base stations (MBSs) and user equipments (UEs) can support directional BF. As mmWave spectrum has short wavelength, massive antenna arrays can be deployed at MBSs to form multiple directional beams through BF training. Then, an MBS can transmit simultaneously to multiple UEs (SUEs) with different beams in the networks. However, the beams that serve different SUEs may transmit (almost) in the same path, especially when SUEs are distributed densely. Thus, they are not in perfect orthogonal beams. Due to the leakage of transmission power, the interference among these beams may be severe. To address this problem, typically the MBS could serve these SUEs in time division multiplex. This will degrade the spectral efficiency. In this context, we investigate the effect of nonorthogonal beam interference and then propose two novel solutions (i.e., dynamic beam switching and static beam selection) to coordinate the transmitting beams effectively. Then, an improved downlink multiuser simultaneous transmission scheme is introduced. In the scheme, an MBS can serve multiple SUEs simultaneously with multiple orthogonal and/or nonorthogonal beams to guarantee SUEs’ Quality of Service. The theoretical and numerical results have shown that our scheme can largely improve the performance of the achievable rate and, meanwhile, can serve lots of SUEs simultaneously.

An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning <=SEP=> In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL), to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

65GHz Doppler Sensor with On-Chip Antenna in 0.18μm SiGe BiCMOS <=SEP=> A single-chip 65GHz Doppler radar transceiver with on-chip patch antenna is reported. Implemented in a production 0.18mum SiGe BiCMOS process, it features a differential output transmit power of 4.3dBm, 16.5dB single-ended down-conversion gain and a double-sideband noise figure of 12.8dB. The radar includes a 65GHz 2-stage cascode LNA with S 11<-15dB at 50-94GHz and 14dB gain at 65GHz, a double-balanced down-convert mixer, a SiGe HBT IF amplifier with low 1/f noise, a VCO and a 65GHz output buffer. The LO is provided by an integrated varactor-tuned 61-67GHz VCO optimized for low phase noise. The patch antenna is designed to be impedance-matched for dual-band operation at 62.8 and 64.6GHz. The use of lumped inductors in all blocks and a vertically-stacked transformer for single-ended to differential conversion in the receive path help reduce the transceiver area to 1mm times 1mm

In-class distractions: The role of Facebook and the primary learning task <=SEP=> While laptops and other Internet accessible technologies facilitate student learning in the classroom, they also increase opportunities for interruptions from off-task social networking sites such as Facebook (FB). A small number of correlational studies have suggested that FB has a detrimental effect on learning performance, however; these studies had neglected to investigate student-engagement in the primary learning task and how this affects task-switching to goal-irrelevant FB intrusions (distractions); and how purposeful deployment of attention to FB (goal-relevant interruptions) affect lecture comprehension on such tasks. This experiment fills a gap in the literature by manipulating lecture interest-value and controls for duration of FB exposure, time of interruption, FB material and the order of FB posts. One hundred and fifty participants were randomly allocated to one of six conditions: (A) no FB intrusions, high-interest (HI) lecture; (B) no FB intrusions, low-interest (LI) lecture (C) goal-relevant FB intrusions, HI lecture (D) goal-relevant FB intrusions, LI lecture (E) goal-irrelevant FB intrusions, HI lecture (F) goal-irrelevant FB intrusions, LI lecture. As predicted, participants were more susceptible to FB distractions when the primary learning task was of low-interest. The study also found that goal-relevant FB intrusions significantly reduced HI lecture comprehension compared to the control condition (A). The results highlight the need for recourses that will help educators increase student engagement with their learning task. Implications for future research are discussed. 2014 Elsevier Ltd. All rights reserved.

Guidelines for human electromyographic research. <=SEP=> 

Design and experimental validation of HyTAQ, a Hybrid Terrestrial and Aerial Quadrotor <=SEP=> This paper details the design, modeling, and experimental validation of a novel mobile robot capable of both aerial and terrestrial locomotion. Flight is achieved through a quadrotor configuration; four actuators provide the required thrust. Adding a rolling cage to the quadrotor makes terrestrial locomotion possible using the same actuator set and control system. Thus, neither the mass nor the system complexity is increased by inclusion of separate actuators for terrestrial and aerial locomotion. An analysis of the system's energy consumption demonstrates that during terrestrial locomotion, the robot only needs to overcome rolling resistance and consumes much less energy compared to the aerial mode. This solves one of the most vexing problems of quadrotors and rotorcraft in general - their short operation time. Experimental results show that the hybrid robot can travel a distance four times greater and operate almost six times longer than an aerial only system. It also solves one of the most challenging problems in terrestrial robot design - obstacle avoidance. When an obstacle is encountered, the system simply flies over it.

Inference and Analysis of Population Structure Using Genetic Data and Network Theory. <=SEP=> Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of prior conditions. The method is implemented in the software NetStruct (available at https://giligreenbaum.wordpress.com/software/).

A comprehensive review of firefly algorithms <=SEP=> The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations. & 2013 Elsevier Ltd. All rights reserved.

Third-wave livestreaming: teens' long form selfie <=SEP=> Mobile livestreaming is now well into its third wave. From early systems such as Bambuser and Qik, to more popular apps Meerkat and Periscope, to today's integrated social streaming features in Facebook and Instagram, both technology and usage have changed dramatically. In this latest phase of livestreaming, cameras turn inward to focus on the streamer, instead of outwards on the surroundings. Teens are increasingly using these platforms to entertain friends, meet new people, and connect with others on shared interests. We studied teens' livestreaming behaviors and motivations on these new platforms through a survey completed by 2,247 American livestreamers and interviews with 20 teens, highlighting changing practices, teens' differences from the broader population, and implications for designing new livestreaming services.

State-space solutions to standard H2 and H∞ control problems <=SEP=> Simple state-space formulas are presented for a controller solving a standard H∞-problem. The controller has the same state-dimension as the plant, its computation involves only two Riccati equations, and it has a separation structure reminiscent of classical LQG (i.e., H2) theory. This paper is also intended to be of tutorial value, so a standard H2-solution is developed in parallel.

What Makes a Great Software Engineer? <=SEP=> Good software engineers are essential to the creation of good software. However, most of what we know about software-engineering expertise are vague stereotypes, such as 'excellent communicators' and 'great teammates'. The lack of specificity in our understanding hinders researchers from reasoning about them, employers from identifying them, and young engineers from becoming them. Our understanding also lacks breadth: what are all the distinguishing attributes of great engineers (technical expertise and beyond)? We took a first step in addressing these gaps by interviewing 59 experienced engineers across 13 divisions at Microsoft, uncovering 53 attributes of great engineers. We explain the attributes and examine how the most salient of these impact projects and teams. We discuss implications of this knowledge on research and the hiring and training of engineers.

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning <=SEP=> TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow (Abadi et al., 2015). It provides an easy-to-use Scikit-learn (Pedregosa et al., 2011) style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-ofart machine learning algorithms built on top of TensorFlow’s low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.

Drunk person identification using thermal infrared images <=SEP=> Drunk person identification is carried out using thermal infrared images. The features used for this purpose are simply the pixel values on specific points on the face of the person. It is proved that for a drunk person, the corresponding cluster in the feature space moves far away from its original position for the sober person. The concept behind the proposed approach is based on the physiology-based face identification. For demonstration purposes, Fisher Linear Discriminant approach is used for space dimensionality reduction. The feature space is found to be of very low dimensionality.

Big Data , Digital Media , and Computational Social Science : Possibilities and Perils <=SEP=> We live life in the network. We check our e-mails regularly, make mobile phone calls from almost any location ... make purchases with credit cards ... [and] maintain friendships through online social networks. ... These transactions leave digital traces that can be compiled into comprehensive pictures of both individual and group behavior, with the potential to transform our understanding of our lives, organizations, and societies.

A combinatorial strongly polynomial algorithm for minimizing submodular functions <=SEP=> This paper presents a combinatorial polynomial-time algorithm for minimizing submodular functions, answering an open question posed in 1981 by Grötschel, Lovász, and Schrijver. The algorithm employs a scaling scheme that uses a flow in the complete directed graph on the underlying set with each arc capacity equal to the scaled parameter. The resulting algorithm runs in time bounded by a polynomial in the size of the underlying set and the length of the largest absolute function value. The paper also presents a strongly polynomial version in which the number of steps is bounded by a polynomial in the size of the underlying set, independent of the function values.