Miracle-dz/UniFAR-FTUs huggingface dataset:
Imitation learning through self-exploration : from body-babbling to visuomotor association / Farhan Dawood

Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Imitation learning through self-exploration is essential in development of sensorimotor skills in infants. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. It is assume that crude sense of self is the prerequisite for social interaction rather than an outcome of it. However, role of mirror neuron in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this thesis, I present a computational model for development of mirror neuron system based on the hypothesis that infants acquire mirror neuron system by sensorimotor associative learning through self-exploration empowering it to understand the perceived action by taking into account the view-dependency of neurons as a probable outcome of their associative connectivity. In our mirror experiment, a humanoid robot stands in front of a glass mirror in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. First, the continuous flow of motion patterns is segmented into motion primitives by identifying the boundaries of actions through Incremental Kernel Slow Feature Analysis. The segmentation model directly operates on the images acquired from the robot’s vision sensor (camera) without requiring any kinematic model of the demonstrator. After segmentation, the spatio-temporal motion sequences are learned incrementally through Topological Gaussian Adaptive Resonance Hidden Markov Model. Later, a visuo-motor association is developed through novel Topological Gaussian Adaptive Resonance Associative Memory. The learning model dynamically generates the topoiii logical structure in a self-stabilizing manner. Finally, after learning, the robot partner performs a similar action in front of the robot and the robot recalls the corresponding motor command from the memory. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot. The results show that the imitation learning algorithm is able to incrementally learn and associate the observed motion patterns based on the segmentation of motion primitives.
Time-Domain Selected Mapping with Polyphase Rotations and Blind Detection for Filtered Single-Carrier Signals

Single-carrier (SC) signal has a low peak-to-average power ratio (PAPR) property. However, the PAPR of filtered SC signal increases if high-level modulation is applied. We have recently proposed a time-domain selected mapping (TD-SLM) which effectively reduces the PAPR of filtered SC signals by applying the binary phase rotation before transmit filtering, but the necessity of side-information decreases the spectrum efficiency (SE). In this paper, a TD-SLM with maximum likelihood (ML) joint blind identification of phase rotation pattern and data detection (hereafter joint blind identification and detection) is introduced. Unlike the frequency-domain SLM (FD-SLM), polyphase rotations are used in order to allow the blind detection. Performance evaluation of the proposed TD-SLM with joint blind identification and detection is done by computer simulation assuming uncoded filtered SC block signal transmission in aspects of PAPR, bit-error rate (BER) and throughput to show that no significant degradation is caused in the BER performance. Keyword Single-carrier (SC) transmission, peak-to-average power ratio (PAPR), selective mapping (SLM), frequency-domain equalization (FDE)
Fusion of SAR and SPOT image data for crop mapping

This paper describes image processing and fusion for land cover mapping focusing on mapping agricultural crops. To achieve a frequent update of crop maps, classification and segmentation algorithms were applied to 13 test cases. Reference data for classification were available from 302 crop fields, and reference data for segmentation from vector data of field boundaries. The reference vector dataset was enhanced by means of visual image interpretation. Radiometric accuracy of segmentation results was evaluated using ratio images. These studies show synergy of radar and optical imagery, with dependence on the sequence of pre-processing and processing techniques in the mapping procedure. Classification accuracy of crop maps based on synthetic aperture radar (SAR), visible-infrared (VIR) and fused imagery reached 82%, 92% and 76% respectively. We conclude that majority based object classification does not improve significantly the overall accuracy. Subsets resulting from the optimum index factor (OIF) algorithm proved slightly better than principal component analysis (PCA) fusion and intensity-hue-saturation (IHS) colour transformation proved worse.
An Implementation of an Implicit POI application with Tangible Reality using Google Place APIs

In recent, the smartphone becomes a center of smart life. People can get various information based on their nearby location, therefore they want to use the information for their rich life. In this paper, we propose Implicit POI application based on user location and selected category. This POI application supports that user can get the information of nearby places without explicit selecting location from the user.
Eye Movement Analysis of Text-Based Web Page Layouts

Summary. User eye movements of five different text-based web page layouts were compared. The layouts evaluated were a narrow 1-column, narrow 2-column and full-length text layouts, including those with a picture on the right and a picture on the left of the article paragraph. All of these were evaluated while users conducted a simple browse task. Heatmap and fixation count data showed no significant difference in the fixation count between the different page layouts. The heatmaps of the layouts shared a similar F-pattern for fixations, with the concentration of the fixations above the fold. Further analysis showed a similar order of fixations on the 2-column and the full-length layout conditions and an identical order of fixations on the picture-left and the picture-right layout conditions. The narrow 1-column layout showed a fixation order different from the other layouts.
Image Quality in Augmented Binocular Vision: QoE Approximations with QoS and EEG

Augmented reality (AR) applications become increasingly popular, however, little is known about how multimedia consumption interplays with the Quality of Experience (QoE) in these settings. We experimentally evaluate binocular vision augmentation with optical see-through devices by contrasting QoE, expressed by Mean Opinion Scores (MOS), with a ground truth reference data set. We find that the QoE in AR settings (i) is higher for small media impairments and lower for high impairment levels compared to opaque settings, (ii) exhibits an emerging quantifiable relationship with the QoE in traditional setups, (iii) can be approached with common objective image quality metrics as Quality of Service (QoS) factors, and (iv) exhibits a relationship between the Low Gamma frequency band levels determined with consumer-grade electroencephalograms (EEG) and image quality levels as rated by the participants.
Has mobile technology disrupted the pedagogy of Problem Based Learning: The tutors perspective -

Background: At Manchester Medical School (MMS), mobile tablet technology is embedded within problem based learning (PBL). Anecdotally, tutors perceived a disruption to PBL. Summary of work: We employed a census survey of phase 2 PBL tutors in MMS. We queried a) demographics b) technological abilities c) perceptions of ipad use, and d) of mobile technology in PBL. Nominal and ordinal data were analysed descriptively, text responses with content analysis. Summary of results: Response rate was 58%. 82% of tutors thought ipads had changed PBL, commenting that PBL was more interactive (44%). The strongest theme, was that facilitators did not feel ipads had altered preparatory work. Emerging themes concerned the reduction of interaction within group and with facilitators. Facilitators felt groups used their ipads to clarify the setting (20%), define the problem (14%) and document intended learning outcomes(18%). Many perceived students using ipads for individual learning (21%), and group feedback (13%). Few facilitators felt that mobile tablet use had changed the pedagogic order of PBL, but had altered students approach to information gathering and presentation. Conclusion: This survey has illustrated the influence of mobile technology on the process of PBL, demonstrating tablet use in most of the 7 stages. A strong perspective is that this enhanced this process. Original Research
Prediction Analysis in Data Mining: A Review

:- The data mining is the technology which is applied to extract the useful information from the rouge information. The clustering is the efficient technique which is applied to cluster the similar and dissimilar type of data. The prediction analysis is applied in which technique of clustering is applied which will cluster the data and in the second step technique of classification is applied which will classify the similar and dissimilar type of data. In this paper, various technique of prediction analysis is reviewed and discussed in terms of various parameters
DC Voltage Control of the DC Micro-Grid for Super High Quality Electric Power Distribution

“DC Micro-grid” is a novel super high quality electric power system with using dc distribution (Fig. 1). The power is distributed through dc distribution line and converted to required ac or dc voltage by converters placed near loads. The load side converters do not need transformers by choosing proper distributed dc voltage (±170 V). The dc distribution line is composed of 3 power lines: +170 V line, neutral line and −170 V line, and the voltages of ±170 V have to be balanced to supply high quality power. In this paper, a voltage balance control method for dc distribution is proposed. Computer simulation results demonstrated that dc micro-grid was able to supply both ac and dc power to loads simultaneously and stably by 3-wire dc distribution and load side converters. 3-wire dc distribution system is adopted in DC micro-grid because one side of single phase 100 V lines become neutral line as same as Japanese standard distribution system, and dc distribution line voltage to ground become low level as ±170 V. Load side dc-dc converter is connected with +170 V line and neutral line. So if there is some dc loads in the system, dc voltage balance control is indispensable. There are some voltage balance control methods. In this paper, we chose a bi-directional chopper
Point-wise Adaptive Filtering for Fast Monte Carlo Noise Reduction

Monte Carlo based photorealistic image synthesis has proven to be one of the most ﬂexible and powerful rendering techniques, but is plagued with undesirable artifacts known as Monte Carlo noise. We present an adaptive ﬁltering method designed for Monte Carlo rendering systems that counteracts noise while respecting sharp features. The ﬁlter operates as a post-process on a noisy image augmented with three screen-space geometric attribute buffers, and by using a point-wise adaptive (varying window size) ﬁltering kernel, this method is able to reinforce the preservation of important scene reﬂected edges, in less time. Comparative results demonstrate the simplicity and efﬁciency of our method, which makes it a feasible and robust solution for smoothing noisy images generated by Monte Carlo rendering techniques. CUDA implementation also makes the algorithm potentially practical for interactive Monte Carlo rendering in the near future.
An Overview of Location and Identity Technologies

In automation systems, the sensing of location and identity is essential to provide the interface between the physical space and the computer logic which runs the system. It is important to understand, however, that the two are not necessarily synonymous, and most technologies provide each datum to varying degrees. This paper provides a survey of some existing sensing technologies, and positions them in a framework within the general sensing space.
High-Level Synthesis Based VLSI Architectures for Video Coding

High Efficiency Video Coding (HEVC) is state-of-the-art video coding standard. Emerging applications like free-viewpoint video, 360degree video, augmented reality, 3D movies etc. require standardized extensions of HEVC. The standardized extensions of HEVC include HEVC Scalable Video Coding (SHVC), HEVC Multiview Video Coding (MV-HEVC), MV-HEVC+ Depth (3D-HEVC) and HEVC Screen Content Coding. 3D-HEVC is used for applications like view synthesis generation, free-viewpoint video. Coding and transmission of depth maps in 3D-HEVC is used for the virtual view synthesis by the algorithms like Depth Image Based Rendering (DIBR). As first step, we performed the profiling of the 3D-HEVC standard. Computational intensive parts of the standard are identified for the efficient hardware implementation. One of the computational intensive part of the 3D-HEVC, HEVC and H.264/AVC is the Interpolation Filtering used for Fractional Motion Estimation (FME). The hardware implementation of the interpolation filtering is carried out using High-Level Synthesis (HLS) tools. Xilinx Vivado Design Suite is used for the HLS implementation of the interpolation filters of HEVC and H.264/AVC. The complexity of the digital systems is greatly increased. High-Level Synthesis is the methodology which offers great benefits such as late architectural or functional changes without time consuming in rewriting of RTL-code, algorithms can be tested and evaluated early in the design cycle and development of accurate models against which the final hardware can be verified.
NANOROBOTICS IN ADVANCES IN PHARMACEUTICAL SCIENCES

Nanorobotics is the technology of creating machines or robots at or close to the scale of a nanometre (10-9 metres), machines constructed at the molecular level (nanomachines) may be used to cure the human body of its various ills. This application of nanotechnology to the field of medicine is commonly called as nanomedicine.Nanotechnology promises futuristic applications such as microscopic robots that assemble other machines or travel inside the body to deliver drugs or do microsurgery. Taking inspiration from the biological motors of living cells, chemists are learning how to utilize protein dynamics to power microsize and nanosize machines with catalytic reactions. Nanorobot? s toolkit contains features like medicine cavity containing medicine, probes, knives and chisels to remove blockages and plaque, microwave emitters and ultrasonic signal generators to destroy cancerous cells, two electrodes generating an electric current, heating the cell up until it dies, powerful lasers could burn away harmful material like arterial plaque.To cure skin diseases, a cream containing nanorobots may be used which remove the right amount of dead skin, remove excess oils, add missing oils, apply the right amounts of natural moisturising compounds, and even achieve the elusive goal of ’deep pore cleaning’. other fields of applications are to clean the wounds,to break the kidney stones, to treat gout, for parasite removal, for cancer treatment, treatment of arteriosclerosis.
COMPARATIVE ANALYSIS OF C99 AND TOPICTILING TEXT SEGMENTATION ALGORITHMS

In this paper, the work done includes the extractio n of information from image datasets which contain natural text. The difficulty level of segmenting natural text from an image is t oo high and so precision is the most important fact or to be kept in mind. To minimize the error rates, error filtration techniqu e is provided, as filtration is adopted while doing image segmentation basically text segmentation present in images. Furthermore, a comparative analysis of two different text segment ation algorithms namely C99 and TopicTiling on image documents is presented . To assess how well each algorithm works, each was applied on different datasets and results were compared. The work done aproves the efficiency of TopicTiling over C99.
A study of varied threshold voltage MOSFET (VTMOS) performance and principle

In this paper, we suggest a novel operation of a SOI MOSFET, varied threshold voltage MOSFET (VTMOS), which can work very well under ultra-low voltage(0.6 V and below) in VLSI circuits. Experiments show that the threshold voltage of the device varies with its gate voltage, which results in better performance than conventional SOI MOSFET (for example, larger drive current and lower leakage). We also provide two-dimensional (2-D) device simulation to perspective its operational principle.
Effect of Origami on Students’ Retention in Geometry

This study was designed to determine the effects of Origami instructional approach on JS I students’ retention in geometry. Two research questions and three hypotheses were formulated to guide the study. The study adopted a quasi-experimental non-equivalent pretest posttest control group design and was restricted to Nsukka local Government Area of Enugu State. Two Co-educational Secondary Schools were drawn for the study, using simple random sampling technique. Out of the two drawn schools, one was randomly assigned to Origami Group (OG) while the other one to the Control Group (CG). A sample of 101 JS one students was involved (65 female and 36 male). The instrument for data collection was geometry retention test (GRT). Data collected were analyzed using mean, standard deviation and analysis of covariance (ANCOVA). The result of the study revealed that use of Origami in teaching geometry to junior secondary school students enhanced their retention in geometry. The study also revealed that the use of Origami had no statistically differential effect on male and female students’ retention. The study also revealed that there was a significant interaction effect between gender and instructional material, on retention of the concepts taught during the study. Based on the findings, the researchers recommended that use of Origami should be adopted in the teaching of geometry (mathematics) in primary, secondary, and tertiary levels of education system. It was also recommended that seminars, workshops and conferences should be mounted by professional bodies, federal and state ministries of education on the use of Origami for mathematics teachers, students and others. This will enable the mathematics educators, serving teachers, students and all to benefit from such an approach.
Analysis of stiffness of human fingertip and comparison with artificial fingers

Human fingers have mechanical characteristics which enable human hands to manipulate objects dexterously in complex environments. Research on such characteristics will help in the design of robotic fingers with capability of dexterous manipulation. In this work, the stiffness of human fingertips tissue is investigated for loading and unloading situations at various contact angles and forces on an acrylic block. The stiffness shows strong nonlinearity for each condition. Based on experimental results, we propose two models to express the stiffness of soft fingertips tissue. Also the contact area of human fingertips exhibits a proportional relation with the deformation of fingertips tissue. Subsequently, the stiffness of fingertips can be expressed to vary linearly with the mean pressure which is calculated from the force and the contact area. It is revealed how the natural frequency in peg-in-hole tasks changes depending on the nonlinear stiffness of fingertips tissue and the mass of object. These stiffness properties have an important role in understanding the degree of smoothness and skill in execution of the task by human hands. Finally, these properties obtained from human fingertips are compared with those of artificial fingertips, made of silicone rubber and silicone gel.
A New Face Recognition Algorithm Based on Haar-Like Features and SRC with Gentle Adaboost

In this paper, a new face recognition algorithm based on Haar-Like features and Gentle Adaboost (GA) feature selection via sparse representation was proposed. Firstly, all the images including face images and non-face images were normalized to size 20 × 20, then Haar-Like features were extracted from the images after size normalization. The number of the extracted features is more than 125,199. Then, Gentle Adaboost algorithm was exploited to feature selection that reduced the feature dimension and retained the most effective features for face recognition. Finally, the samples were classified and identified for face recognition via sparse representation classification (SRC) algorithm. Comparing it with SRC, Nearest Neighbor (NN), Nearest Subspace (NS), Support Vector Machine (SVM) algorithms, the experiment results on AR database demonstrate that the new proposed algorithm can achieve a higher recognition rate than these traditional algorithms. Even though the increase of the dimension, the new proposed algorithm always got higher recognition rate than SRC and other algorithms, which proved the new algorithm can get better identification effects and stronger stability in the low dimension feature space. Keywords-Haar-Like features; Gentle Adaboost; sparse representation; face recognition
Multi-Camera Active-vision System Reconfiguration for Deformable Object Motion Capture

To improve the accuracy in capturing the motion of deformable objects, a reconfigurable multicamera active-vision system which can dynamically reposition its cameras online is proposed, and a design for such a system, along with a methodology to select the near-optimal positions and orientations of the set of cameras, is presented. The active-vision system accounts for the deformation of the object-of-interest by tracking triangulated vertices in order to predict the shape of the object at subsequent demand instants. It then selects a system configuration that minimizes the expected error in the recovered position of each of these vertices. Extensive simulations and experiments have verified that using the proposed reconfigurable system to both translate and rotate cameras to near-optimal poses is tangibly superior to using cameras which are either static, or can only rotate, in minimizing the error in recovered vertex positions.
Smile Detection Using Multi-scale Gaussian Derivatives

In this paper we show that Multi-scale Gaussian Derivatives combined with Support Vector Machines provide an effective approach to facial expression analysis.The approach was tested on the GENKI and Cohn-Kanade dataset for detecting smiles. Our approach outperformed the current benchmark approach of using Gabor Energy Filters with Support Vector Machines.