Query: Lane Detection ( Part I ) : Mono-Vision Based Method

Relevant passages:
Real time detection of lane markers in urban streets <=SEP=> We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.

Robust Lane Detection and Tracking in Challenging Scenarios <=SEP=> A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.

Lane detection and tracking using B-Snake <=SEP=> In this paper, we proposed a B-Snake based lane detection and tracking algorithm without any cameras’ parameters. Compared with other lane models, the B-Snake based lane model is able to describe a wider range of lane structures since B-Spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been merged here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. Furthermore, a robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake. Also, a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B-Snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images. It is also applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads. q 2003 Elsevier B.V. All rights reserved.

Using vanishing points for camera calibration <=SEP=> In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.

Realtime lane tracking of curved local road <=SEP=> A lane detection system is an important component of many intelligent transportation systems. We present a robust realtime lane tracking algorithm for a curved local road. First, we present a comparative study to find a good realtime lane marking classifier. Once lane markings are detected, they are grouped into many lane boundary hypotheses represented by constrained cubic spline curves. We present a robust hypothesis generation algorithm using a particle filtering technique and a RANSAC (random sample concensus) algorithm. We introduce a probabilistic approach to group lane boundary hypotheses into left and right lane boundaries. The proposed grouping approach can be applied to general part-based object tracking problems. It incorporates a likelihood-based object recognition technique into a Markov-style process. An experimental result on local streets shows that the suggested algorithm is very reliable

The Ontological Foundation of REA Enterprise Information Systems <=SEP=> Philosophers have studied ontologies for centuries in their search for a systematic explanation of existence: “What kind of things exist?” Recently, ontologies have emerged as a major research topic in the fields of artificial intelligence and knowledge management where they address the content issue: “What kind of things should we represent?” The answer to that question differs with the scope of the ontology. Ontologies that are subject-independent are called upper-level ontologies, and they attempt to define concepts that are shared by all domains, such as time and space. Domain ontologies, on the other hand, attempt to define the things that are relevant to a specific application domain. Both types of ontologies are becoming increasingly important in the era of the Internet where consistent and machine-readable semantic definitions of economic phenomena become the language of e-commerce. In this paper, we propose the conceptual accounting framework of the Resource-Event-Agent (REA) model of McCarthy (1982) as an enterprise domain ontology, and we build upon the initial ontology work of Geerts and McCarthy (2000) which explored REA with respect to the ontological categorizations of John Sowa (1999). Because of its conceptual modeling heritage, REA already resembles an established ontology in many declarative (categories) and procedural (axioms) respects, and we also propose here to extend formally that framework both (1) vertically in terms of entrepreneurial logic (value chains) and workflow detail, and (2) horizontally in terms of type and commitment images of enterprise economic phenomena. A strong emphasis throughout the paper is given to the microeconomic foundations of the category definitions.

Dual- and Multiband U-Slot Patch Antennas <=SEP=> A wide band patch antenna fed by an L-probe can be designed for dual- and multi-band application by cutting U-slots on the patch. Simulation and measurement results are presented to illustrate this design.

Advances in Collaborative Filtering <=SEP=> The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.

VirtualTable: a projection augmented reality game <=SEP=> VirtualTable is a projection augmented reality installation where users are engaged in an interactive tower defense game. The installation runs continuously and is designed to attract people to a table, which the game is projected onto. Any number of players can join the game for an optional period of time. The goal is to prevent the virtual stylized soot balls, spawning on one side of the table, from reaching the cheese. To stop them, the players can place any kind of object on the table, that then will become part of the game. Depending on the object, it will become either a wall, an obstacle for the soot balls, or a tower, that eliminates them within a physical range. The number of enemies is dependent on the number of objects in the field, forcing the players to use strategy and collaboration and not the sheer number of objects to win the game.

A Context-Aware Usability Model for Mobile Health Applications <=SEP=> Mobile healthcare is a fast growing area of research that capitalizes on mobile technologies and wearables to provide realtime and continuous monitoring and analysis of vital signs of users. Yet, most of the current applications are developed for general population without taking into consideration the context and needs of different user groups. Designing and developing mobile health applications and diaries according to the user context can significantly improve the quality of user interaction and encourage the application use. In this paper, we propose a user context model and a set of usability attributes for developing mobile applications in healthcare. The proposed model and the selected attributes are integrated into a mobile application development framework to provide user-centered and context-aware guidelines. To validate our framework, a mobile diary was implemented for patients undergoing Peritoneal Dialysis (PD) and tested with real users.

New approach for clustering of big data: DisK-means <=SEP=> The exponential growth in the amount of data gathered from various sources has resulted in the need for more efficient algorithms to quickly analyze large datasets. Clustering techniques, like K-Means are useful in analyzing data in a parallel fashion. K-Means largely depends upon a proper initialization to produce optimal results. K-means++ initialization algorithm provides solution based on providing an initial set of centres to the K-Means algorithm. However, its inherent sequential nature makes it suffer from various limitations when applied to large datasets. For instance, it makes k iterations to find k centres. In this paper, we present an algorithm that attempts to overcome the drawbacks of previous algorithms. Our work provides a method to select a good initial seeding in less time, facilitating fast and accurate cluster analysis over large datasets.

A probabilistic constrained clustering for transfer learning and image category discovery <=SEP=> Neural network-based clustering has recently gained popularity, and in particular a constrained clustering formulation has been proposed to perform transfer learning and image category discovery using deep learning. The core idea is to formulate a clustering objective with pairwise constraints that can be used to train a deep clustering network; therefore the cluster assignments and their underlying feature representations are jointly optimized end-toend. In this work, we provide a novel clustering formulation to address scalability issues of previous work in terms of optimizing deeper networks and larger amounts of categories. The proposed objective directly minimizes the negative log-likelihood of cluster assignment with respect to the pairwise constraints, has no hyper-parameters, and demonstrates improved scalability and performance on both supervised learning and unsupervised transfer learning.

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach <=SEP=> The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains.

EEG Classification of Different Imaginary Movements within the Same Limb <=SEP=> The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.

Single-Stage Single-Switch Four-Output Resonant LED Driver With High Power Factor and Passive Current Balancing <=SEP=> A resonant single-stage single-switch four-output LED driver with high power factor and passive current balancing is proposed. By controlling one output current, the other output currents of four-output LED driver can be controlled via passive current balancing, which makes its control simple. When magnetizing inductor current operates in critical conduction mode, unity power factor is achieved. The proposed LED driver uses only one active switch and one magnetic component, thus it benefits from low cost, small volume, and light weight. Moreover, high-efficiency performance is achieved due to single-stage power conversion and soft-switching characteristics. The characteristics of the proposed LED driver are studied in this paper and experimental results of two 110-W four-output isolated LED drivers are provided to verify the studied results.

Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines <=SEP=> Natural language software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies. In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.

Correlating low-level image statistics with users - rapid aesthetic and affective judgments of web pages <=SEP=> In this paper, we report a study that examines the relationship between image-based computational analyses of web pages and users' aesthetic judgments about the same image material. Web pages were iteratively decomposed into quadrants of minimum entropy (quadtree decomposition) based on low-level image statistics, to permit a characterization of these pages in terms of their respective organizational symmetry, balance and equilibrium. These attributes were then evaluated for their correlation with human participants' subjective ratings of the same web pages on four aesthetic and affective dimensions. Several of these correlations were quite large and revealed interesting patterns in the relationship between low-level (i.e., pixel-level) image statistics and design-relevant dimensions.

American Sign Language Recognition Using Leap Motion Sensor <=SEP=> In this paper, we present an American Sign Language recognition system using a compact and affordable 3D motion sensor. The palm-sized Leap Motion sensor provides a much more portable and economical solution than Cyblerglove or Microsoft kinect used in existing studies. We apply k-nearest neighbor and support vector machine to classify the 26 letters of the English alphabet in American Sign Language using the derived features from the sensory data. The experiment result shows that the highest average classification rate of 72.78% and 79.83% was achieved by k-nearest neighbor and support vector machine respectively. We also provide detailed discussions on the parameter setting in machine learning methods and accuracy of specific alphabet letters in this paper.

Cognitive, emotional, and behavioral considerations for chronic pain management in the Ehlers-Danlos syndrome hypermobility-type: a narrative review. <=SEP=> BACKGROUND
Ehlers-Danlos syndrome (EDS) hypermobility-type is the most common hereditary disorder of the connective tissue. The tissue fragility characteristic of this condition leads to multi-systemic symptoms in which pain, often severe, chronic, and disabling, is the most experienced. Clinical observations suggest that the complex patient with EDS hypermobility-type is refractory toward several biomedical and physical approaches. In this context and in accordance with the contemporary conceptualization of pain (biopsychosocial perspective), the identification of psychological aspects involved in the pain experience can be useful to improve interventions for this under-recognized pathology.


PURPOSE
Review of the literature on joint hypermobility and EDS hypermobility-type concerning psychological factors linked to pain chronicity and disability.


METHODS
A comprehensive search was performed using scientific online databases and references lists, encompassing publications reporting quantitative and qualitative research as well as unpublished literature.


RESULTS
Despite scarce research, psychological factors associated with EDS hypermobility-type that potentially affect pain chronicity and disability were identified. These are cognitive problems and attention to body sensations, negative emotions, and unhealthy patterns of activity (hypo/hyperactivity).


CONCLUSIONS
As in other chronic pain conditions, these aspects should be more explored in EDS hypermobility-type, and integrated into chronic pain prevention and management programs. Implications for Rehabilitation Clinicians should be aware that joint hypermobility may be associated with other health problems, and in its presence suspect a heritable disorder of connective tissue such as the Ehlers-Danlos syndrome (EDS) hypermobility-type, in which chronic pain is one of the most frequent and invalidating symptoms. It is necessary to explore the psychosocial functioning of patients as part of the overall chronic pain management in the EDS hypermobility-type, especially when they do not respond to biomedical approaches as psychological factors may be operating against rehabilitation. Further research on the psychological factors linked to pain chronicity and disability in the EDS hypermobility-type is needed.

A brief review: factors affecting the length of the rest interval between resistance exercise sets. <=SEP=> Research has indicated that multiple sets are superior to single sets for maximal strength development. However, whether maximal strength gains are achieved may depend on the ability to sustain a consistent number of repetitions over consecutive sets. A key factor that determines the ability to sustain repetitions is the length of rest interval between sets. The length of the rest interval is commonly prescribed based on the training goal, but may vary based on several other factors. The purpose of this review was to discuss these factors in the context of different training goals. When training for muscular strength, the magnitude of the load lifted is a key determinant of the rest interval prescribed between sets. For loads less than 90% of 1 repetition maximum, 3-5 minutes rest between sets allows for greater strength increases through the maintenance of training intensity. However, when testing for maximal strength, 1-2 minutes rest between sets might be sufficient between repeated attempts. When training for muscular power, a minimum of 3 minutes rest should be prescribed between sets of repeated maximal effort movements (e.g., plyometric jumps). When training for muscular hypertrophy, consecutive sets should be performed prior to when full recovery has taken place. Shorter rest intervals of 30-60 seconds between sets have been associated with higher acute increases in growth hormone, which may contribute to the hypertrophic effect. When training for muscular endurance, an ideal strategy might be to perform resistance exercises in a circuit, with shorter rest intervals (e.g., 30 seconds) between exercises that involve dissimilar muscle groups, and longer rest intervals (e.g., 3 minutes) between exercises that involve similar muscle groups. In summary, the length of the rest interval between sets is only 1 component of a resistance exercise program directed toward different training goals. Prescribing the appropriate rest interval does not ensure a desired outcome if other components such as intensity and volume are not prescribed appropriately.

Silicon carbide power MOSFETs: Breakthrough performance from 900 V up to 15 kV <=SEP=> Since Cree, Inc.'s 2<sup>nd</sup> generation 4H-SiC MOSFETs were commercially released with a specific on-resistance (R<sub>ON, SP</sub>) of 5 mΩ·cm<sup>2</sup> for a 1200 V-rating in early 2013, we have further optimized the device design and fabrication processes as well as greatly expanded the voltage ratings from 900 V up to 15 kV for a much wider range of high-power, high-frequency, and high-voltage energy-conversion and transmission applications. Using these next-generation SiC MOSFETs, we have now achieved new breakthrough performance for voltage ratings from 900 V up to 15 kV with a R<sub>ON, SP</sub> as low as 2.3 mΩ·cm<sup>2</sup> for a breakdown voltage (BV) of 1230 V and 900 V-rating, 2.7 mΩ·cm<sup>2</sup> for a BV of 1620 V and 1200 V-rating, 3.38 mΩ·cm<sup>2</sup> for a BV of 1830 V and 1700 V-rating, 10.6 mΩ·cm<sup>2</sup> for a BV of 4160 V and 3300 V-rating, 123 mΩ·cm<sup>2</sup> for a BV of 12 kV and 10 kV-rating, and 208 mΩ·cm<sup>2</sup> for a BV of 15.5 kV and 15 kV-rating. In addition, due to the lack of current tailing during the bipolar device switching turn-off, the SiC MOSFETs reported in this work exhibit incredibly high frequency switching performance over their silicon counter parts.

The evolution of human skin coloration. <=SEP=> Skin color is one of the most conspicuous ways in which humans vary and has been widely used to define human races. Here we present new evidence indicating that variations in skin color are adaptive, and are related to the regulation of ultraviolet (UV) radiation penetration in the integument and its direct and indirect effects on fitness. Using remotely sensed data on UV radiation levels, hypotheses concerning the distribution of the skin colors of indigenous peoples relative to UV levels were tested quantitatively in this study for the first time. The major results of this study are: (1) skin reflectance is strongly correlated with absolute latitude and UV radiation levels. The highest correlation between skin reflectance and UV levels was observed at 545 nm, near the absorption maximum for oxyhemoglobin, suggesting that the main role of melanin pigmentation in humans is regulation of the effects of UV radiation on the contents of cutaneous blood vessels located in the dermis. (2) Predicted skin reflectances deviated little from observed values. (3) In all populations for which skin reflectance data were available for males and females, females were found to be lighter skinned than males. (4) The clinal gradation of skin coloration observed among indigenous peoples is correlated with UV radiation levels and represents a compromise solution to the conflicting physiological requirements of photoprotection and vitamin D synthesis. The earliest members of the hominid lineage probably had a mostly unpigmented or lightly pigmented integument covered with dark black hair, similar to that of the modern chimpanzee. The evolution of a naked, darkly pigmented integument occurred early in the evolution of the genus Homo. A dark epidermis protected sweat glands from UV-induced injury, thus insuring the integrity of somatic thermoregulation. Of greater significance to individual reproductive success was that highly melanized skin protected against UV-induced photolysis of folate (Branda & Eaton, 1978, Science201, 625-626; Jablonski, 1992, Proc. Australas. Soc. Hum. Biol.5, 455-462, 1999, Med. Hypotheses52, 581-582), a metabolite essential for normal development of the embryonic neural tube (Bower & Stanley, 1989, The Medical Journal of Australia150, 613-619; Medical Research Council Vitamin Research Group, 1991, The Lancet338, 31-37) and spermatogenesis (Cosentino et al., 1990, Proc. Natn. Acad. Sci. U.S.A.87, 1431-1435; Mathur et al., 1977, Fertility Sterility28, 1356-1360).As hominids migrated outside of the tropics, varying degrees of depigmentation evolved in order to permit UVB-induced synthesis of previtamin D(3). The lighter color of female skin may be required to permit synthesis of the relatively higher amounts of vitamin D(3)necessary during pregnancy and lactation. Skin coloration in humans is adaptive and labile. Skin pigmentation levels have changed more than once in human evolution. Because of this, skin coloration is of no value in determining phylogenetic relationships among modern human groups.

Robust and false positive free watermarking in IWT domain using SVD and ABC <=SEP=> Watermarking is used to protect the copyrighted materials from being misused and help us to know the lawful ownership. The security of any watermarking scheme is always a prime concern for the developer. In this work, the robustness and security issue of IWT (integer wavelet transform) and SVD (singular value decomposition) based watermarking is explored. Generally, SVD based watermarking techniques suffer with an issue of false positive problem. This leads to even authenticating the wrong owner. We are proposing a novel solution to this false positive problem; that arises in SVD based approach. Firstly, IWT is employed on the host image and then SVD is performed on this transformed host. The properties of IWT and SVD help in achieving high value of robustness. Singular values are used for the watermark embedding. In order to further improve the quality of watermarking, the optimization of scaling factor (mixing ratio) is performed with the help of artificial bee colony (ABC) algorithm. A comparison with other schemes is performed to show the superiority of proposed scheme. & 2015 Elsevier Ltd. All rights reserved.

A Literature Survey of Benchmark Functions For Global Optimization Problems <=SEP=> Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.

Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems <=SEP=> In this article, three approaches are proposed for measuring difficulty that can be useful in developing Dynamic Difficulty Adjustment (DDA) systems in different game genres. Our analysis of the existing DDA systems shows that there are three ways to measure the difficulty of the game: using the formal model of gameplay, using the features of the game, and direct examination of the player. These approaches are described in this article and supplemented by appropriate examples of DDA implementations. In addition, the article describes the distinction between task complexity and task difficulty in DDA systems. Separating task complexity (especially the structural one) is suggested, which is an objective characteristic of the task, and task difficulty, which is related to the interaction between the task and the task performer.

A low-voltage, low quiescent current, low drop-out regulator <=SEP=> The demand for low voltage, low drop-out (LDO) regulators is increasing because of the growing demand for portable electronics, i.e., cellular phones, pagers, laptops, etc. LDOs are used coherently with dc-dc converters as well as standalone parts. In power supply systems, they are typically cascaded onto switching regulators to suppress noise and provide a low noise output. The need for low voltage is innate to portable low power devices and corroborated by lower breakdown voltages resulting from reductions in feature size. Low quiescent current in a battery operated system is an intrinsic performance parameter because it partially determines battery life. This paper discusses some techniques that enable the practical realizations of low quiescent current LDOs at low voltages and in existing technologies. The proposed circuit exploits the frequency response dependence on load-current to minimize quiescent current flow. Moreover, the output current capabilities of MOS power transistors are enhanced and drop-out voltages are decreased for a given device size. Other applications, like dc-dc converters, can also reap the benefits of these enhanced MOS devices. An LDO prototype incorporating the aforementioned techniques was fabricated. The circuit was operable down to input voltages of 1 V with a zero-load quiescent current flow of 23 μA. Moreover, the regulator provided 18 and 50 mA of output current at input voltages of 1 and 1.2 V respectively.

Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation <=SEP=> Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely consider the spatio-temporal intervals between neighbor checkins, which are essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose a new variant of LSTM, named STLSTM, which implements time gates and distance gates into LSTM to capture the spatio-temporal relation between successive check-ins. Specifically, one time gate and one distance gate are designed to control short-term interest update, and another time gate and distance gate are designed to control long-term interest update. Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. Our experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.

Absorptive Frequency-Selective Transmission Structure With Square-Loop Hybrid Resonator <=SEP=> A novel design of an absorptive frequency-selective transmission structure (AFST) is proposed. This structure is based on the design of a frequency-dependent lossy layer with square-loop hybrid resonator (SLHR). The parallel resonance provided by the hybrid resonator is utilized to bypass the lossy path and improve the insertion loss. Meanwhile, the series resonance of the hybrid resonator is used for expanding the upper absorption bandwidth. Furthermore, the absorption for out-of-band frequencies is achieved by using four metallic strips with lumped resistors, which are connected with the SLHR. The quantity of lumped elements required in a unit cell can be reduced by at least 50% compared to previous structures. The design guidelines are explained with the aid of an equivalent circuit model. Both simulation and experiment results are presented to demonstrate the performance of our AFST. It is shown that an insertion loss of 0.29 dB at 6.1 GHz and a 112.4% 10 dB reflection reduction bandwidth are obtained under the normal incidence.

Implementation of AES-GCM encryption algorithm for high performance and low power architecture Using FPGA <=SEP=> Evaluation of the Advanced Encryption Standard (AES) algorithm in FPGA is proposed here. This Evaluation is compared with other works to show the efficiency. Here we are concerned about two major purposes. The first is to define some of the terms and concepts behind basic cryptographic methods, and to offer a way to compare the myriad cryptographic schemes in use today. The second is to provide some real examples of cryptography in use today. The design uses an iterative looping approach with block and key size of 128 bits, lookup table implementation of S-box. This gives low complexity architecture and easily achieves low latency as well as high throughput. Simulation results, performance results are presented and compared with previous reported designs. Since its acceptance as the adopted symmetric-key algorithm, the Advanced Encryption Standard (AES) and its recently standardized authentication Galois/Counter Mode (GCM) have been utilized in various security-constrained applications. Many of the AES-GCM applications are power and resource constrained and requires efficient hardware implementations. In this project, AES-GCM algorithms are evaluated and optimized to identify the high-performance and low-power architectures. The Advanced Encryption Standard (AES) is a specification for the encryption of electronic data. The Cipher Block Chaining (CBC) mode is a confidentiality mode whose encryption process features the combining (“chaining”) of the plaintext blocks with the previous Cipher text blocks. The CBC mode requires an IV to combine with the first plaintext block. The IV need not be secret, but it must be unpredictable. Also, the integrity of the IV should be protected. Galois/Counter Mode (GCM) is a block cipher mode of operation that uses universal hashing over a binary Galois field to provide authenticated encryption. Galois Hash is used for authentication, and the Advanced Encryption Standard (AES) block cipher is used for encryption in counter mode of operation. To obtain the least-complexity S-box, the formulations for the Galois Field (GF) sub-field inversions in GF (24) are optimized By conducting exhaustive simulations for the input transitions, we analyze the synthesis of the AES S-boxes considering the switching activities, gate-level net lists, and parasitic information. Finally, by implementation of AES-GCM the high-performance GF (2128) multiplier architectures, gives the detailed information of its performance. An optimized coding for the implementation of Advanced Encryption Standard-Galois Counter Mode has been developed. The speed factor of the algorithm implementation has been targeted and a software code in Verilog HDL has been developed. This implementation is useful in wireless security like military communication and mobile telephony where there is a grayer emphasis on the speed of communication.

Multi-Document Summarization using Sentence-based Topic Models <=SEP=> Most of the existing multi-document summarization methods decompose the documents into sentences and work directly in the sentence space using a term-sentence matrix. However, the knowledge on the document side, i.e. the topics embedded in the documents, can help the context understanding and guide the sentence selection in the summarization procedure. In this paper, we propose a new Bayesian sentence-based topic model for summarization by making use of both the term-document and term-sentence associations. An efficient variational Bayesian algorithm is derived for model parameter estimation. Experimental results on benchmark data sets show the effectiveness of the proposed model for the multi-document summarization task.