Query: IoT based control and automation of smart irrigation system: An automated irrigation system using sensors, GSM, Bluetooth and cloud technology

Relevant passages:
IOT based crop-field monitoring and irrigation automation <=SEP=> Internet Of Things (IoT)is a shared network of objects or things which can interact with each other provided the Internet connection. IoT plays an important role in agriculture industry which can feed 9.6 billion people on the Earth by 2050. Smart Agriculture helps to reduce wastage, effective usage of fertilizer and thereby increase the crop yield. In this work, a system is developed to monitor crop-field using sensors (soil moisture, temperature, humidity, Light) and automate the irrigation system. The data from sensors are sent to Web server database using wireless transmission. In server database the data are encoded in JSON format. The irrigation is automated if the moisture and temperature of the field falls below the brink. In greenhouses light intensity control can also be automated in addition to irrigation. The notifications are sent to farmers' mobile periodically. The farmers' can able to monitor the field conditions from anywhere. This system will be more useful in areas where water is in scarce. This system is 92% more efficient than the conventional approach.

Automated Irrigation System-IoT Based Approach <=SEP=> Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.

Smart irrigation using low-cost moisture sensors and XBee-based communication <=SEP=> Deficiency in fresh water resources globally has raised serious alarms in the last decade. Efficient management of water resources play an important role in the agriculture sector. Unfortunately, this is not given prime importance in the third world countries because of adhering to traditional practices. This paper presents a smart system that uses a bespoke, low cost soil moisture sensor to control water supply in water deficient areas. The sensor, which works on the principle of moisture dependent resistance change between two points in the soil, is fabricated using affordable materials and methods. Moisture data acquired from a sensor node is sent through XBEE wireless communication modules to a centralized server that controls water supply. A user-friendly interface is developed to visualize the daily moisture data. The low-cost and wireless nature of the sensing hardware presents the possibility to monitor the moisture levels of large agricultural fields. Moreover, the proposed moisture sensing method has the ability to be incorporated into an automated drip-irrigation scheme and perform automated, precision agriculture in conjunction with de-centralized water control.

Self-Autonomous Wireless Sensor Nodes With Wind Energy Harvesting for Remote Sensing of Wind-Driven Wildfire Spread <=SEP=> The satellite-based remote sensing technique has been widely used in monitoring wildfire spread. There are two prominent drawbacks with this approach of using satellites located in space: (1) very low sampling rate (temporal resolution problem) and (2) lack of accuracy (spatial and spectral resolution problem). To address these challenges, a wireless sensor network deployed at ground level with high-fidelity and low-altitude atmospheric sensing for wind speed of local wildfire spread has been used. An indirect approach in sensing wind speed has been proposed in this paper as an alternative to the bulky conventional wind anemometer to save cost and space. The wind speed is sensed by measuring the equivalent electrical output voltage of the wind turbine generator (WTG). The percentage error in the wind speed measurement using the proposed indirect method is measured to be well within the ±4% limit with respect to wind anemometer accuracy. The same WTG also functions as a wind energy harvesting (WEH) system to convert the available wind energy into electrical energy to sustain the operation of the wireless sensor node. The experimental results show that the designed WEH system is able to harvest an average electrical power of 7.7 mW at an average wind speed of 3.62 m/s for powering the operation of the wireless sensor node that consumes 3.5 mW for predicting the wildfire spread. Based on the sensed wind speed information, the fire control management system determines the spreading condition of the wildfire, and an adequate fire suppression action can be performed by the fire-fighting experts.

Wireless Sensor Network for Precise Agriculture Monitoring <=SEP=> Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments.

A CORDIC processor for FFT computation and its implementation using gallium arsenide technology <=SEP=> In this paper, the architecture and the implementation of a complex fast Fourier transform (CFFT) processor using 0.6 m gallium arsenide (GaAs) technology are presented. This processor computes a 1024-point FFT of 16 bit complex data in less than 8 s, working at a frequency beyond 700 MHz, with a power consumption of 12.5 W. The architecture of the processor is based on the COordinate Rotation DIgital Computer (CORDIC) algorithm, which avoids the use of conventional multiplicationand-accumulation (MAC) units, but evaluates the trigonometric functions using only add and shift operations. Improvements to the basic CORDIC architecture are introduced in order to reduce the area and power of the processor. This together with the use of pipelining and carry save adders produces a very regular and fast processor. The CORDIC units were fabricated and tested in order to anticipate the final performance of the processor. This work also demonstrates the maturity of GaAs technology for implementing ultrahigh-performance signal processors.

Real-parameter optimization with differential evolution <=SEP=> This study reports how the differential evolution (DE) algorithm performed on the test bed developed for the CEC05 contest for real parameter optimization. The test bed includes 25 scalable functions, many of which are both non-separable and highly multi-modal. Results include DE's performance on the 10 and 30-dimensional versions of each function

Menu-Match: Restaurant-Specific Food Logging from Images <=SEP=> Logging food and calorie intake has been shown to facilitate weight management. Unfortunately, current food logging methods are time-consuming and cumbersome, which limits their effectiveness. To address this limitation, we present an automated computer vision system for logging food and calorie intake using images. We focus on the "restaurant" scenario, which is often a challenging aspect of diet management. We introduce a key insight that addresses this problem specifically: restaurant plates are often both nutritionally and visually consistent across many servings. This insight provides a path to robust calorie estimation from a single RGB photograph: using a database of known food items together with restaurant-specific classifiers, calorie estimation can be achieved through identification followed by calorie lookup. As demonstrated on a challenging Menu-Match dataset and an existing third party dataset, our approach outperforms previous computer vision methods and a commercial calorie estimation app. Our Menu-Match dataset of realistic restaurant meals is made publicly available.

Towards a Trust Management System for Cloud Computing <=SEP=> Cloud computing provides cost-efficient opportunities for enterprises by offering a variety of dynamic, scalable, and shared services. Usually, cloud providers provide assurances by specifying technical and functional descriptions in Service Level Agreements (SLAs) for the services they offer. The descriptions in SLAs are not consistent among the cloud providers even though they offer services with similar functionality. Therefore, customers are not sure whether they can identify a trustworthy cloud provider only based on its SLA. To support the customers in reliably identifying trustworthy cloud providers, we propose a multi-faceted Trust Management (TM) system architecture for a cloud computing marketplace. This system provides means to identify the trustworthy cloud providers in terms of different attributes (e.g., security, performance, compliance) assessed by multiple sources and roots of trust information.

Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy <=SEP=> Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm’s past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimization problems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-theart optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms.

Lightweight IPS for port scan in OpenFlow SDN networks <=SEP=> Security has been one of the major concerns for the computer network community due to resource abuse and malicious flows intrusion. Before a network or a system is attacked, a port scan is typically performed to discover vulnerabilities, like open ports, which may be used to access and control them. Several studies have addressed Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) methods for detecting malicious activities, based on received flows or packet data analysis. However, those methods lead to an increase in switching latency, due to the need to analyze flows or packets before routing them. This may also increase network overhead when flows or packets are duplicated to be parsed by an external IDS. On the one hand, an IDS/IPS may be a bottleneck on the network and may not be useful. On the other hand, the new paradigm called Software Defined Networking (SDN) and the OpenFlow protocol provide some statistical information about the network that may be used for detecting malicious activities. Hence, this work presents a new port scan IPS for SDN based on the OpenFlow switch counters data. A non-intrusive and lightweight method was developed and implemented, with low network overhead, and low memory and processing power consumption. The results showed that our method is effective on detecting and preventing port scan attacks.

A new ultra-wideband, ultra-short monocycle pulse generator with reduced ringing <=SEP=> We introduce a new ultra-wideband (UWB), ultra-short, step recovery diode monocycle pulse generator. This pulse generator uses a simple RC high-pass filter as a differentiator to generate the monocycle pulse directly. The pulse-shaping network employs a resistive circuit to achieve UWB matching and substantial removal of the pulse ringing, and rectifying and switching diodes to further suppress the ringing. An ultra-short monocycle pulse of 300-ps pulse duration, -17-dB ringing level, and good symmetry has been demonstrated. Good agreement between the measured and calculated results was achieved.

Improved Option Pricing Using Artificial Neural Networks and Bootstrap Methods <=SEP=> A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.

High quality & low thermal resistance eutectic flip chip LED bonding <=SEP=> Die attach process plays a key role in the thermal management of high power LED packages by governing the thermal resistance of the solder interfaces between the LED chips and the substrate materials. In this paper, comparison was made between the GaN based flip chip LED packages fabricated by (1) heated collet and (2) flux reflow eutectic die attach process. Thermal transient characteristics of the samples had been investigated based on the evaluation of the differential structure function and chip temperature measurement by infrared microscopy. It was shown that LEDs with higher void content would result in higher thermal resistance and chip temperature. Under high power operation at 6W, it was estimated that the junction temperature would exceed 80°C with void content 40%, which was at least 10°C higher comparing to that with void content 20%. Based on the result, LEDs samples bonded by heated collet die attach process had an average void content of 8.8% with 0.9% standard deviation, which were smaller comparing to the samples bonded by flux reflow die attach process (40% void content with standard deviation 20.5%). Due to the advantage of small void content control, it is possible to fabricate high power LED package with low thermal resistance, consistent and reliable die attach quality by using heated collet die attach process.

Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models <=SEP=> Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.

Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge <=SEP=> We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and nally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.

AdGraph: A Machine Learning Approach to Automatic and Effective Adblocking <=SEP=> Filter lists are widely deployed by adblockers to block ads and other forms of undesirable content in web browsers. However, these filter lists are manually curated based on informal crowdsourced feedback, which brings with it a significant number of maintenance challenges. To address these challenges, we propose a machine learning approach for automatic and effective adblocking called AdGraph. Our approach relies on information obtained from multiple layers of the web stack (HTML, HTTP, and JavaScript) to train a machine learning classifier to block ads and trackers. Our evaluation on Alexa top-10K websites shows that AdGraph automatically and effectively blocks ads and trackers with 97.7% accuracy. Our manual analysis shows that AdGraph has better recall than filter lists, it blocks 16%more ads and trackers with 65% accuracy. We also show that AdGraph is fairly robust against adversarial obfuscation by publishers and advertisers that bypass filter lists.

Multiple templates auto exposure control based on luminance histogram for onboard camera <=SEP=> In order to adjust the exposure of high resolution and frequency images from an on-board camera in real time, a simple exposure control approach based on luminance histogram analysis is presented to realize light control precisely and rapidly. The algorithm divides the initial image into nine blocks and calculates the mean brightness of every part. It uses the luminance histogram to analyze the degree of exposure of both: the global image and every part, especially interest areas. According to the area size of the neighborhood on the two edges of the histogram, the different weight matrix can be decided to quickly calculate the optimal brightness for the next frame, and then the optimal exposure time can be determined accurately. The algorithm does not need to determine an expensive quadratic function to decide the weight of every part so as to fast perform auto exposure control. The experimental results show that the algorithm can correctly and fast adjust the exposure time of the on-board camera on an autonomous robot, and has strong adaptability for various shootings outdoor.

What campus-based students think about the quality and benefits of e-learning <=SEP=> There is a trend in Irish universities to utilise the benefits of the e-learning as a mechanism to improve learning performance of campus-based students. Whilst traditional methods, such as face-to-face lectures, tutorials, and mentoring, remain dominant in the educational sector, universities are investing heavily in learning technologies, to facilitate improvements with respect to the quality of learning. The technology to support reuse and sharing of educational resources, or learning objects, is becoming more stable, with interoperability standards maturing. However, debate has raged about what constitutes effective use of learning technology. This research expands upon a study carried out in 2003 examining students’ perceptions of e-learning in a large undergraduate accounting class environment. As a result, improvements were made to the instructional design of the course, to enable students to engage interactively with content. The subsequent study, reported in this paper, adopted a broad range of techniques to understand students’ learning experience in depth. The findings of this research provide an insight into how these students really work and learn using technologies, if at all. It is hoped that our findings will improve the experience for both students and lecturers who engage in teaching and learning through this medium. 502 British Journal of Educational Technology Vol 36 No 3 2005 © British Educational Communications and Technology Agency, 2005. Introduction A recent trend in higher education is to create and provide online access to course materials. Over the past two decades academics and institutes of higher education have been diversifying their delivery of instruction through new Internet media such as learning management systems, asynchronous distance learning, and online classrooms amongst a myriad of other burgeoning educational technologies. This combination of traditional face-to-face lectures or tutorials, and web-based course content is better known as “blended learning,” purporting to blend the best aspects of real and virtual environments. Many Irish universities have invested in architectures and platforms to support their teaching staff in delivering material to students in a blended manner. Other institutions have adopted a lecturer-driven approach, whereby teaching staff are left to their own devices to supplement their lectures and tutorials with online material, hosted via their own web servers and typically open source, freeware software, or basic web pages. The reasons behind the drive to incorporate technology into the educational process are threefold. First, pressure to utilise information and communications technology (ICT) at university level comes from changes in the student demography. The rise of “full time part time students” is a phenomenon of recent years, where school leavers take part-time jobs whilst attending university, leaving less time for evening tutorials or weekend study. In addition, there is a drive for what is known as lifelong learning, whereby adults are increasingly returning to institutions of higher education to take supplementary courses whilst in full-time employment, or during short career breaks. In this regard, the Irish government has made a commitment to have 15% of adults in continuing higher education by 2006. The figure currently stands at less than 5% (Department of Education and Science, 2000). This movement to adult continuing education was also reflected in a statement made by Information Commission Society (Ireland): “The issue of integration IT to the teaching process is an important part of future improvements, which it will be crucial to pursue” (Boylan, 2000, p. 31). Second, changes in the market for delivery of education is also shifting. Private for-profit higher education institutions are offering a wide range of certificate and degree courses. The Irish Open University Initiative “Oscail” and its UK equivalent offer distance learning diploma and degree courses. Many of these courses are delivered through ICT, along with weekend face-to-face tutorials. This distance learning market has also seen the arrival of new entrants, such as the Atlantic University Alliance, a collaboration between National University of Ireland, Galway, University College Cork, and the University of Limerick. Similar undertakings are underway in other institutes of higher education, whereby expertise in delivering blended learning within the institution is being extended towards distance learning courses by using a significant amount of ICT to overcome geographic and time barriers. Third, another pressure on traditional institutions of higher education comes from innovations in new technologies. John Seely Brown and Paul Duiguid claim that these technologies “offer new ways to think of producing, distributing and consuming acaWhat students think about e-learning 503 © British Educational Communications and Technology Agency, 2005. demic material. As with so many other institutions, new technologies have caused universities to rethink not simply isolated features but their entire mission and how they go about it” (Seely Brown & Duiguid, 2000). This sentiment is also being echoed by government policy towards higher education where the Irish State is attempting to set certain parameters of performance covering such areas as equity of access, commitment to e-learning and to ICT, commitment to life-long learning, and outreach to other communities. In the subject domain of accountancy, the integration of technology into the classroom is also becoming a central issue for accounting educators for an additional reason. Accountancy professionals in industry require competency in accountancy-related information technology (IT) skills. Higher education institutions, in response, have tried to simulate authentic learning scenarios in the accountancy class and incorporate IT topics into their programmes. However, the efficacy of introducing technology in accounting education remains unclear. Positive learning effects associated with IT in accounting education have been noted (Abdolmohammadi et al , 1998; Fetters, McKenzie & Callaghan, 1986; Friedman, 1981). Equally, evidence has shown that technology has negatively impacted on learning (Dickens & Harper, 1986; Togo & McNamee, 1997). Indeed, some critics harshly criticise its introduction to educational contexts. Selwyn (2002, p. 172) claims in generic terms that “...in the final analysis educational technology remains a diverting pastime and ‘add-on’ to the curriculum for some teaching and learners, whilst for most in education its impact has been slight.” This paper hopes to look at some of the issues raised by individual students with regard to the use of technology in the teaching and learning process. Rather than focusing on macro issues, such as the political decisions behind the integration of technology, this research aims to examine individuals’ acceptance or rejection of using ICT. Exploring the perspective of the individual student when discussing the use of technology is paramount to beginning to understand the nature of ICT in higher educational settings. This is of importance both to policy makers and education technologists in the debate on the role of ICT in education at an organisational level. The module and the medium This research focused on one module of The Principles of Accounting being taught to approximately 600 first-year undergraduate students at the University of Limerick. The module under investigation was part of a wider course, leading to a degree in Business Studies or Law and Accounting. The majority of students were between 17 and 19 years of age, coming from distributed second-level schools in Ireland. A minority were Erasmus students from various European countries, or mature students over the age of 23. For all, except overseas students, this was their first experience in using an e-learning platform, although most had previously informal used the web to gather information, or prepare coursework in second-level education, prior to entering university. This module was delivered by using a blended learning approach, supplementing weekly lectures, tutorials, and laboratory sessions with online course content, interac504 British Journal of Educational Technology Vol 36 No 3 2005 © British Educational Communications and Technology Agency, 2005. tive quizzes, and Excel tasks. A range of different e-learning assessments complemented the traditional weekly meetings. Students were asked to submit a compulsory paperbased accounting case study, and additionally were offered two optional ICT assignments, a stream of online multiple-choice quizzes, and an Excel project. A module web site provided course details, additional readings, and supplementary links. Employing multiple teaching methods simultaneously is a form of blended learning (Saunders & Werner, 2003). Alternative instructional resources can stimulate positive effects on accounting students’ learning experiences, according to (Rebele et al , 1998). Furthermore, instructional innovations are desirable to develop accounting students’ IT competencies (Albrecht & Sack, 2000). Figure 1 illustrates the staged delivery of both the traditional and online elements of the module. The university does not provide institutional support for e-learning on a campus-wide scale, but two other modules in their first year also used a blended learning delivery approach, providing lecture notes online, or sample answers, in the subject area of economics and mathematics. Both of these other modules differ in marking scheme and in the extent to which online supports are provided such as discussion boards, or positive marking schemes for continuous online assessments. As such, blended learning is not yet a norm across modules for first-year students at this current time. Methods The research developed on experience gained in a previous study on a prior

A Facial Expression Recognition System Using Convolutional Networks <=SEP=> Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

Analysis of user keyword similarity in online social networks <=SEP=> How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.

A multidisciplinary approach towards computational thinking for science majors <=SEP=> This paper describes the development and initial evaluation of a new course ``Introduction to Computational Thinking'' taken by science majors to fulfill a college computing requirement. The course was developed by computer science faculty in collaboration with science faculty and it focuses on the role of computing and computational principles in scientific inquiry. It uses Python and Python libraries to teach computational thinking via basic programming concepts, data management concepts, simulation, and visualization. Problems with a computational aspect are drawn from different scientific disciplines and are complemented with lectures from faculty in those areas. Our initial evaluation indicates that the problem-driven approach focused on scientific discovery and computational principles increases the student's interest in computing.

"Industrie 4.0" and Smart Manufacturing - A Review of Research Issues and Application Examples <=SEP=> 

Low voltage low power analog circuit design OTA using signal attenuation technique in universal filter application <=SEP=> This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18μm. The simulation results of this circuit showed a high DC gain of 73.6dB with a unity frequency of 50.19MHz and a total harmonic distortion of -60.81dB at 100 kHz for an input voltage of 1Vpp. Based on this circuit, a voltage mode universal filter has been implemented. The simulation results are in a good agreement with the theoretical calculations.

Effects of energy drinks on the cardiovascular system <=SEP=> Throughout the last decade, the use of energy drinks has been increasingly looked upon with caution as potentially dangerous due to their perceived strong concentration of caffeine aside from other substances such as taurine, guarana, and L-carnitine that are largely unknown to the general public. In addition, a large number of energy drink intoxications have been reported all over the world including cases of seizures and arrhythmias. In this paper, we focus on the effect of energy drinks on the cardiovascular system and whether the current ongoing call for the products' sales and regulation of their contents should continue.

Emotion regulation: affective, cognitive, and social consequences. <=SEP=> One of life's great challenges is successfully regulating emotions. Do some emotion regulation strategies have more to recommend them than others? According to Gross's (1998, Review of General Psychology, 2, 271-299) process model of emotion regulation, strategies that act early in the emotion-generative process should have a different profile of consequences than strategies that act later on. This review focuses on two commonly used strategies for down-regulating emotion. The first, reappraisal, comes early in the emotion-generative process. It consists of changing the way a situation is construed so as to decrease its emotional impact. The second, suppression, comes later in the emotion-generative process. It consists of inhibiting the outward signs of inner feelings. Experimental and individual-difference studies find reappraisal is often more effective than suppression. Reappraisal decreases emotion experience and behavioral expression, and has no impact on memory. By contrast, suppression decreases behavioral expression, but fails to decrease emotion experience, and actually impairs memory. Suppression also increases physiological responding for suppressors and their social partners. This review concludes with a consideration of five important directions for future research on emotion regulation processes.

Boosting item keyword search with spreading activation <=SEP=> Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.

Advanced Cooling for Power Electronics <=SEP=> Power electronics devices such as MOSFET's, GTO's, IGBT's, IGCT's etc. are now widely used to efficiently deliver electrical power in home electronics, industrial drives, telecommunication, transport, electric grid and numerous other applications. This paper discusses cooling technologies that have evolved in step to remove increasing levels of heat dissipation and manage junction temperatures to achieve goals for efficiency, cost, and reliability. Cooling technologies rely on heat spreading and convection. In applications that use natural or forced air cooling, water heat pipes provide efficient heat spreading for size and weight reduction. Previous concepts are reviewed and an improved heat sink concept with staggered fin density is described for more isothermal cooling. Where gravity can drive liquid flow, thermosiphons provide efficient heat transport to remote fin volumes that can be oriented for natural and/or forced air cooling. Liquid cold plates (LCP's) offer the means to cool high heat loads and heat fluxes including double sided cooling for the highest density packaging. LCP's can be used both in single phase cooling systems with aqueous or oil based coolants and in two-phase cooling systems with dielectric fluids and refrigerants. Previous concepts are reviewed and new concepts including an air cooled heat sink, a thermosiphon heat sink, a vortex flow LCP and a shear flow direct contact cooling concept are described.

Evaluating a PID, pure pursuit, and weighted steering controller for an autonomous land vehicle <=SEP=> Wright Laboratory, at Tyndall AFB, Florida, has contracted the University ofFlorida to develop autonomous navigation systems for a variety ofrobotic vehicles, capable ofperforming tasks associated with the location and removal of bombs and mines. One ofthe tasks involves surveying closed target ranges for unexploded buried munitions. Accuracy in path following is critical to the task. There are hundreds of acres that currently require surveying. The sites are typically divided into regions, where each mission can take up to 4.5 hours. These sites are usually surveyed along parallel rows. By improving the accuracy ofpath following, the distance betweenthe rows can be increased to nearly the detection width ofthe ground penetrating sensors, resulting in increased acreage surveyed per mission. This paper evaluates a high-level PID and a pure pursuit steering controller. The controllers were combined into a weighted solution so that the desirable features of each controller is preserved. This strategy was demonstrated in simulation and implemented on a Navigation Test Vehicle (NTV). For a test path ofvarying curvature, the average lateral control error was 2 cm at a vehicle speed of 1.34 mIs.

New clock-gating techniques for low-power flip-flops <=SEP=> Two novel low power flip-flops are presented in the paper. Proposed flip-flops use new gating techniques that reduce power dissipation deactivating the clock signal. Presented circuits overcome the clock duty-cycle limitation of previously reported gated flip-flops.
Circuit simulations with the inclusion of parasitics show that sensible power dissipation reduction is possible if input signal has reduced switching activity. A 16-bit counter is presented as a simple low power application.