Query: Real-Time Inter-Frame Histogram Builder for SPAD Image Sensors

Relevant passages:
100 000 Frames/s 64 × 32 Single-Photon Detector Array for 2-D Imaging and 3-D Ranging <=SEP=> We report on the design and characterization of a multipurpose 64 × 32 CMOS single-photon avalanche diode (SPAD) array. The chip is fabricated in a high-voltage 0.35-μm CMOS technology and consists of 2048 pixels, each combining a very low noise (100 cps at 5-V excess bias) 30-μm SPAD, a prompt avalanche sensing circuit, and digital processing electronics. The array not only delivers two-dimensional intensity information through photon counting in either free-running (down to 10-μs integration time) or time-gated mode, but can also perform smart light demodulation with in-pixel background suppression. The latter feature enables phase-resolved imaging for extracting either three-dimensional depth-resolved images or decay lifetime maps, by measuring the phase shift between a modulated excitation light and the reflected photons. Pixel-level memories enable fully parallel processing and global-shutter readout, preventing motion artifacts (e.g., skew, wobble, motion blur) and partial exposure effects. The array is able to acquire very fast optical events at high frame-rate (up to 100 000 fps) and at single-photon level. Low-noise SPADs ensure high dynamic range (up to 110 dB at 100 fps) with peak photon detection efficiency of almost 50% at 410 nm. The SPAD imager provides different operating modes, thus, enabling both time-domain applications, like fluorescence lifetime imaging (FLIM) and fluorescence correlation spectroscopy, as well as frequency-domain FLIM and lock-in 3-D ranging for automotive vision and lidar.

CMOS SPADs: Design Issues and Research Challenges for Detectors, Circuits, and Arrays <=SEP=> Solid-state single photon detectors are playing a significant role in the development of high-performance single-photon imaging systems for fluorescence lifetime imaging, time-of-flight positron emission tomography and Raman Spectroscopy applications. The main driving factors are the unparalleled levels of miniaturization and portability, low fabrication costs, and high overall performance resulting from the integration of single-photon avalanche diodes (SPADs) with mixed-signal circuits in deep-submicron (DSM) complementary metal-oxide-semiconductor (CMOS) technology. At the heart of such imaging systems is the SPAD, capable of single-photon sensitivity and sub-nanosecond time resolution, and its associated circuitry, which in DSM CMOS, is capable of high-speed, low-power mixed-mode signal processing. In this paper, we review and discuss the most recent developments in DSM CMOS SPAD detectors, circuits and arrays and investigate issues of scalability, miniaturization and performance trade-offs involved in designing SPAD imaging systems. Design considerations, research challenges, and future directions for CMOS SPAD image sensors will be highlighted and addressed.

Photon counting and direct ToF camera prototype based on CMOS SPADs <=SEP=> This paper presents a camera prototype for 2D/3D image capture in low illumination conditions based on single-photon avalanche-diode (SPAD) image sensor for direct time-offlight (d-ToF). The imager is a 64×64 array with in-pixel TDC for high frame rate acquisition. Circuit design techniques are combined to ensure successful 3D image capturing under low sensitivity conditions and high level of uncorrelated noise such as dark count and background illumination. Among them an innovative time gated front-end for the SPAD detector, a reverse start-stop scheme and real-time image reconstruction at Ikfps are incorporated by the imager. To the best of our knowledge, this is the first ToF camera based on a SPAD sensor fabricated and proved for 3D image reconstruction in a standard CMOS process without any opto-flavor or high voltage option. It has a depth resolution of 1cm at an illumination power from less than 6nW/mm2 down to 0.1nW/mm2.

11.5 A time-correlated single-photon-counting sensor with 14GS/S histogramming time-to-digital converter <=SEP=> Time-correlated single photon counting (TCSPC) is a photon-efficient technique to record ultra-fast optical waveforms found in numerous applications such as time-of-flight (ToF) range measurement (LIDAR) [1], ToF 3D imaging [2], scanning optical microscopy [3], diffuse optical tomography (DOT) and Raman sensing [4]. Typical instrumentation consists of a pulsed laser source, a discrete detector such as an avalanche photodiode (APD) or photomultiplier tube (PMT), time-to-digital converter (TDC) card and a FPGA or PC to assemble and compute histograms of photon time stamps. Cost and size restrict the number of channels of TCSPC hardware. Having few detection and conversion channels, the technique is limited to processing optical waveforms with low intensity, with less than one returned photon per laser pulse, to avoid pile-up distortion [4]. However, many ultra-fast optical waveforms exhibit high dynamic range in the number of photons emitted per laser pulse. Examples are signals observed at close range in ToF with multiple reflections, diffuse reflected photons in DOT or local variations in fluorescent dye concentration in microscopy. This paper provides a single integrated chip that reduces conventional TCSPC pile-up mechanisms by an order of magnitude through ultra-parallel realizations of both photon detection and time-resolving hardware. A TDC architecture is presented which combines the two step iterated TCSPC process of time-code generation, followed by memory lookup, increment and write, into one parallel direct-to-histogram conversion. The sensor achieves 71.4ps resolution, over 18.85ns dynamic range, with 14GS/s throughput. The sensor can process 1.7Gphoton/s and generate 21k histograms/s (with 4.6μs readout time), each capturing a total of 1.7kphotons in a 1μs exposure.

Multiple-event direct to histogram TDC in 65nm FPGA technology <=SEP=> A novel multiple-event Time to Digital Converter (TDC) with direct to histogram output is implemented in a 65nm Xilinx Virtex 5 FPGA. The delay-line based architecture achieves 16.3 ps temporal accuracy over a 2.86ns dynamic range. The measured maximum conversion rate of 6.17 Gsamples/s and the sampling rate of 61.7 Gsamples/s are the highest published in the literature. The system achieves a linearity of -0.9/+3 LSB DNL and -1.5/+5 LSB INL. The TDC is demonstrated in a direct time of flight optical ranging application with 12mm error over a 350mm range.

Human-level concept learning through probabilistic program induction <=SEP=> People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several “visual Turing tests” probing the model’s creative generalization abilities, which in many cases are indistinguishable from human behavior.

Analyzing Factors Affecting Users ’ Behavior Intention to Use Social Media : Twitter Case <=SEP=> Advancement of technology and the Internet proliferation have visible effects in the world. One of the most important effects is the increased social media usage among the Internet users. For this purpose, factors having impacts on users’ behavior intention to social media usage are investigated within the scope of the study. A research model based on technology acceptance model 2 is proposed and revised by the taking social media platforms into consideration. The effects of perceived ease of use, perceived usefulness, social influence, facilitating conditions, playfulness, and trust are measured. Data collected from 462 respondents are analyzed by structural equation modeling technique. According to results of the measurement and structural model validities, a fit and acceptable model is achieved to measure the related effects of variables. The results of study reveal the both direct and indirect positive impacts of the factors on users’ behavior intention to use social media.

Detecting Topic Drift with Compound Topic Models <=SEP=> The Latent Dirichlet Allocation topic model of Blei, Ng, & Jordan (2003) is well-established as an effective approach to recovering meaningful topics of conversation from a set of documents. However, a useful analysis of user-generated content is concerned not only with the recovery of topics from a static data set, but with the evolution of topics over time. We employ a compound topic model (CTM) to track topics across two distinct data sets (i.e. past and present) and to visualize trends in topics over time; we evaluate several metrics for detecting a change in the distribution of topics within a time-window; and we illustrate how our approach discovers emerging conversation topics related to current events in real data sets.

A Survey on Automatically Mining Facets for Web Queries <=SEP=> Received Jan 4, 2017 Revised Jun 2, 2017 Accepted Jun 26, 2017 In this paper, a detailed survey on different facet mining techniques, their advantages and disadvantages is carried out. Facets are any word or phrase which summarize an important aspect about the web query. Researchers proposed different efficient techniques which improves the user’s web query search experiences magnificently. Users are happy when they find the relevant information to their query in the top results. The objectives of their research are: (1) To present automated solution to derive the query facets by analyzing the text query; (2) To create taxonomy of query refinement strategies for efficient results; and (3) To personalize search according to user interest. Keyword:

Spatial reconstruction of single-cell gene expression data <=SEP=> Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

Defending against Internet worms: a signature-based approach <=SEP=> With the capability of infecting hundreds of thousands of hosts, worms represent a major threat to the Internet. The defense against Internet worms is largely an open problem. This paper investigates two important problems. Can a localized defense system detect new worms that were not seen before and moreover, capture the attack packets? How to identify polymorphic worms from the normal background traffic? We have two major contributions here. The first contribution is the design of a novel double-honeypot system, which is able to automatically detect new worms and isolate the attack traffic. The second contribution is the proposal of a new type of position-aware distribution signatures (PADS), which fit in the gap between the traditional signatures and the anomaly-based systems. We propose two algorithms based on expectation-maximization (EM) and Gibbs sampling for efficient computation of PADS from polymorphic worm samples. The new signature is capable of handling certain polymorphic worms. Our experiments show that the algorithms accurately separate new variants of the MSBlaster worm from the normal-traffic background.

USim: a user behavior simulation framework for training and testing IDSes in GUI based systems <=SEP=> Anomaly detection systems largely depend on user profile data to be able to detect deviations from normal activity. Most of this profile data is currently based on command-line instructions/directives executed by users on a system. With the advent and extensive usage of graphical user interfaces (GUIs), command-line data can no longer fully represent user's complete behavior which is essential for effectively detecting the anomalies in these GUI based systems. Collection of user behavior data is a slow and time consuming process. In this paper, we present a new approach to automate the generation of user data by parameterizing user behavior in terms of user intention (malicious/normal), user skill level, set of applications installed on a machine, mouse movement and keyboard activity. The user behavior parameters are used to generate templates, which can be further customized. The framework is called USim which can achieve rapid generation of user behavior data based on these templates for GUI based systems. The data thus generated can be utilized for rapidly training and testing intrusion detection systems (IDSes) and improving their detection precision.

Learning Pulse: Using Wearable Biosensors and Learning Analytics to Investigate and Predict Learning Success in Self-regulated Learning <=SEP=> The Learning Pulse study aims to explore whether physiological data such as heart rate and step count correlate with learning activity data and whether they are good predictors for learning success during self-regulated learning. To verify this hypothesis an experiment was set up involving eight doctoral students at the Open University of the Netherlands. Through wearable sensors, heart rate and step count were constantly monitored and learning activity data were collected. All data were stored in a Learning Record Store in xAPI format. Additionally, with an Activity Rating Tool, the participants rated their learning and working experience by indicating the perceived levels of productivity, stress, challenge and abilities along with the type of activity. These human annotated labels can be used for supervising machine learning algorithms to discriminate the successful learning moments from the unsuccessful ones and eventually discover the attributes that most influence the learning process.

Moving beyond linearity and independence in top-N recommender systems <=SEP=> This paper suggests a number of research directions in which the recommender systems can improve their quality, by moving beyond the assumptions of linearity and independence that are traditionally made. These assumptions, while producing effective and meaningful results, can be suboptimal, as in lots of cases they do not represent the real datasets. In this paper, we discuss three different ways to address some of the previous constraints. More specifically, we focus on the development of methods capturing higher-order relations between the items, cross-feature interactions and intra-set dependencies which can potentially lead to a considerable enhancement of the recommendation accuracy.

Adversarial Networks for the Detection of Aggressive Prostate Cancer <=SEP=> Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of trainable parameters of deep neural networks however renders them inherently data hungry, a characteristic that heavily challenges the medical imaging community. Though interestingly, with the de facto standard training of fully convolutional networks (FCNs) for semantic segmentation being agnostic towards the ‘structure’ of the predicted label maps, valuable complementary information about the global quality of the segmentation lies idle. In order to tap into this potential, we propose utilizing an adversarial network which discriminates between expert and generated annotations in order to train FCNs for semantic segmentation. Because the adversary constitutes a learned parametrization of what makes a good segmentation at a global level, we hypothesize that the method holds particular advantages for segmentation tasks on complex structured, small datasets. This holds true in our experiments: We learn to segment aggressive prostate cancer utilizing MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit.

Network virtualization: state of the art and research challenges <=SEP=> Recently network virtualization has been pushed forward by its proponents as a long-term solution to the gradual ossification problem faced by the existing Internet and proposed to be an integral part of the next-generation networking paradigm. By allowing multiple heterogeneous network architectures to cohabit on a shared physical substrate, network virtualization provides flexibility, promotes diversity, and promises security and increased manageability. However, many technical issues stand in the way of its successful realization. This article investigates the past and the state of the art in network virtualization along with the future challenges that must be addressed to realize a viable network virtualization environment.

Induction and molecular signature of pathogenic TH17 cells <=SEP=> Interleukin 17 (IL-17)-producing helper T cells (TH17 cells) are often present at the sites of tissue inflammation in autoimmune diseases, which has led to the conclusion that TH17 cells are main drivers of autoimmune tissue injury. However, not all TH17 cells are pathogenic; in fact, TH17 cells generated with transforming growth factor-β1 (TGF-β1) and IL-6 produce IL-17 but do not readily induce autoimmune disease without further exposure to IL-23. Here we found that the production of TGF-β3 by developing TH17 cells was dependent on IL-23, which together with IL-6 induced very pathogenic TH17 cells. Moreover, TGF-β3-induced TH17 cells were functionally and molecularly distinct from TGF-β1-induced TH17 cells and had a molecular signature that defined pathogenic effector TH17 cells in autoimmune disease.

Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management <=SEP=> Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data.

Deep Voice 2 : Multi-Speaker Neural Text-to-Speech <=SEP=> We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.

MAUI: making smartphones last longer with code offload <=SEP=> This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.

Evaluation of Talent Management on Employees Performance in Beverage Industry : A Case of Delmonte Kenya Limited <=SEP=> Talent management practice within organization is an international human resource strategy that seeks to identify, develop, deploy and retain talented and high potential employees. The objectives of the study were to determine the effect of talent retention on employees performance, assess how talent attraction impacts, effect of learning and development on employees performance in beverage industry in Kenya. The study adopted a descriptive research design in which the target population of 2,500 employees of Del Monte Kenya. The study used stratified sampling method to select 83 employees according to their job cadres. Descriptive statistics such as the standard deviation, percentages and frequency distribution were used. The study established that the job retention motivated the employees of Del Monte leading to ultimate performance. The study recommended that the management should ensure the work environment was attractive to the employees so as to motivate, thus leading to better performance.

Kinematics and dynamics analysis of a 2-DOF spherical parallel robot <=SEP=> In this paper, the kinematic and dynamic analysis of a 2-degree-of-freedom spherical parallel manipulator is presented. The mobility and motion pattern of the manipulator are analyzed using screw theory, which resulted in obtaining the direct and inverse Jacobian matrices. Position, velocity and acceleration relations between different parts of the manipulator and the actuated joint angles are obtained, which are prerequisite for dynamic analysis. Since the manipulator, belongs to a class of parallel mechanism known as over-constrained mechanism, thus to obtain a dynamical model, a modification should be applied in its kinematic arrangement by preserving the performed motion pattern. Finally, accuracy of the dynamical model is verified by comparing the results obtained from the formulated model with results obtained from a SimMechanics model of the under study manipulator.

A FRAMEWORK FOR DETERMINING CRITICAL SUCCESS FACTORS INFLUENCING CONSTRUCTION BUSINESS PERFORMANCE <=SEP=> Recent reports by Latham (1994) and Egan (1998) have emphasized the need for the construction industry to increase its competitiveness, and have suggested the use of performance measurement as a tool for continuous improvement. Comprehensive measurement of a company’s performance and subsequent feedback to its managers is vital for business transformation. Measurement also enables businesses to be compared with each other on the basis of standardized information, allowing best practices to be identified and applied more widely. Historically, business performance has been determined principally through the use of financial performance criteria, but recently, it has been established that performance measurement needs to go beyond this. This paper reviews the various financial and non-financial factors that influence construction business. A methodology for defining the critical success factors relevant to construction businesses is further outlined. The critical success factors associated with construction business are also reviewed. Finally, it is concluded that more important than presenting a list of critical success factors (which are bound to change) is the need to have a holistic framework for identifying and implementing the required success factors.

Interactive Visualization of Small World Graphs <=SEP=> Many real world graphs have small world characteristics, that is, they have a small diameter compared to the number of nodes and exhibit a local cluster structure. Examples are social networks, software structures, bibliographic references and biological neural nets. Their high connectivity makes both finding a pleasing layout and a suitable clustering hard. In this paper we present a method to create scalable, interactive visualizations of small world graphs, allowing the user to inspect local clusters while maintaining a global overview of the entire structure. The visualization method uses a combination of both semantical and geometrical distortions, while the layout is generated by a spring embedder algorithm using recently developed force model. We use a cross referenced database of 500 artists as a running example

PENS-wheel (one-wheeled self balancing vehicle) balancing control using PID controller <=SEP=> Pens-Wheel is an electric vehicle which uses one wheel that able to balance itself so the rider does not fall forward or backward while riding it. This vehicle uses one brushless DC motor as actuator which capable to rotate in both directions symmetrically. The vehicle uses a combination of accelerometer and gyroscope contained in IMU (Inertial Measurement Unit) for balancing sensor. The controlled motion on the vehicle occurs only on the x-axis (pitch angle), in forward and backward directions. The PID (Proportional Integral Derivative) control algorithm is used to maintain the balance and movement of the vehicle. From the simulation and application in the real vehicle, the use of PID control is capable driving the vehicle in maintaining the balance condition within ±10° tilt angle boundary on flat surface, bumpy road, and inclining road up to 15° slope.

A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems <=SEP=> Recent spoken dialog systems have been able to recognize freely spoken user input in restricted domains thanks to statistical methods in the automatic speech recognition. These methods require a high number of natural language utterances to train the speech recognition engine and to assess the quality of the system. Since human speech offers many variants associated with a single intent, a high number of user utterances have to be elicited. Developers are therefore turning to crowdsourcing to collect this data. This paper compares three different methods to elicit multiple utterances for given semantics via crowd sourcing, namely with pictures, with text and with semantic entities. Specifically, we compare the methods with regard to the number of valid data and linguistic variance, whereby a quantitative and qualitative approach is proposed. In our study, the method with text led to a high variance in the utterances and a relatively low rate of invalid data.

DEEP-SEE FACE: A Mobile Face Recognition System Dedicated to Visually Impaired People <=SEP=> In this paper, we introduce the DEEP-SEE FACE framework, an assistive device designed to improve cognition, interaction, and communication of visually impaired (VI) people in social encounters. The proposed approach jointly exploits computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks in order to detect, track, and recognize, in real-time, various persons existent in the video streams. The major contribution of the paper concerns a global, fixed-size face representation that takes into the account of various video frames while remaining independent of the length of the image sequence. To this purpose, we introduce an effective weight adaptation scheme that is able to determine the relevance assigned to each face instance, depending on the frame degree of motion/camera blur, scale variation, and compression artifacts. Another relevant contribution involves a hard negative mining stage that helps us differentiating between known and unknown face identities. The experimental results, carried out on a large-scale data set, validate the proposed methodology with an average accuracy and recognition rates superior to 92%. When tested in real life, indoor/outdoor scenarios, the DEEP-SEE FACE prototype proves to be effective and easy to use, allowing the VI people to access visual information during social events.

Hashtag recommendation for hyperlinked tweets <=SEP=> Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link. In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.

Students ’ Attitude towards Science and Technology <=SEP=> This study assessed attitudes towards science and technology middle school students. The population included all 3rd grade students a total of 230 students (105 female and 125 male) chose through stratified random sampling method. Research instrument was the Persian translation of the Science Education questionnaire. Data analyzed by SPSS version 17.00. Reliability of the scale calculated by Cronbach's alpha coefficient (0.91). Results indicated that there is a positive attitude towards science and technology among students. However, there was not a positive attitude towards some items of science and technology. The results also showed that there is a meaningful difference between males and females points of views in attitude towards sciences and technology. According to this result, males have higher averages than the females. The results of this research provide important information about students' attitude towards science and could be used by science teachers and educators to development of science curricula and science books.

Intelligent fractional-order PID (FOPID) heart rate controller for cardiac pacemaker <=SEP=> Efficient and robust control of cardiac pacemaker is essential for providing life-saving control action to regulate Heart Rate (HR) in a dynamic environment. Several controller designs involving proportional-integral-derivative (PID) and fuzzy logic controllers (FLC) have been reported but each have their limitations to face the dynamic challenge of regulating HR. Fractional-order control (FOC) systems provide controllers that are described by fractional-order differential equations that offers fine tuning of the control parameters to provide robust and efficient performance. In this work a robust fractional-order PID (FOPID) controller is designed based on Ziegler-Nichols tuning method. The stable FOPID controller outperformed PID controllers with different tuning methods and also the FLC in terms of rise time, settling time and % overshoot. The FOPID controller also demonstrated feasibility for rate-adaptive pacing. However, the FOPID controller designed in this work is not optimal and is limited by the tuning procedure. More efficient design using optimization techniques such as particle swarm intelligence or genetic algorithm tuning can offer optimal control of the cardiac pacemaker.