Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional neural network in network (CNNiN): hyperspectral image classification and dimensionality reduction

Classification is a principle technique in hyperspectral images (HSIs), where a label is assigned to each pixel based on its characteristics. However, due to lack of labelled training instances in HSIs and also its ultra-high dimensionality, deep learning approaches need a special consideration for HSI classification. As one of the first works in the HSI classification, this study proposes a novel network pipeline called convolutional neural network in network (which is deeper than the existing approaches) by jointly utilising the spatial and spectral information and produces high-level features from the original HSI. This can occur by using spatial-spectral relationships of individual pixel vector at the initial component of the proposed pipeline; the extracted features are then combined to form a joint spatial-spectral feature map. Finally, a recurrent neural network is trained on the extracted features which contain wealthy spectral and spatial properties of the HSI to predict the corresponding label of each vector. The model has been tested on two large scale hyperspectral datasets in terms of classification accuracy, training error, and computational time.
Improving Arabic Text to Image Mapping Using a Robust Machine Learning Technique

In this paper, we introduce an approach to automatically convert simple modern standard Arabic children’s stories to the best representative images that can efficiently illustrate the meaning of words. It is a kind of imitating the imaginative process when children read a story, yet a great challenge for a machine to achieve it. For simplification issues, we apply several techniques to find the images and we associate them with related words dynamically. First, we apply natural language processing techniques to analyze the text in stories and we extract keywords of all characters and events in each sentence. Second, we apply an image captioning process through a pre-trained deep learning model for all retrieved images from our multimedia database as well as the Google search engine. Third, using sentence similarities, most significant images are retrieved back by selecting top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> highest similarity values. It is worth mentioning that using the captioning process, to rank top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> images, has shown reasonable precision values as per our preliminary results. The option to refine or validate the ranked images to compose the final visualization for each story is also provided to ensure a flexible and safe learning environment.
Spatial-spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling

For the spatial-spectral classification of hyperspectral images (HSIs), a deep learning framework is proposed in this study, which consists of convolutional neural networks (CNNs) and Markov random fields (MRFs). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilised as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic prior for regularising the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation. In comparison with several state-of-the-art approaches for data classification on three publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology.
Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets

As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling massive or online datasets. To overcome this drawback of KPCA, in this paper, we propose a two-phase incremental KPCA (TP-IKPCA) algorithm which can incorporate data into KPCA in an incremental fashion. In the first phase, an incremental algorithm is developed to explicitly express the data in the kernel space. In the second phase, we extend an incremental principal component analysis (IPCA) to estimate the kernel principal components. Extensive experimental results on both synthesized and real datasets showed that the proposed TP-IKPCA produces similar principal components as conventional batch-based KPCA but is computationally faster than KPCA and its several incremental variants. Therefore, our algorithm can be applied to massive or online datasets where the batch method is not available.
Small Cells in the Forthcoming 5G/IoT: Traffic Modelling and Deployment Overview

This paper provides an overview of the use of small cells (e.g., femtocells) in the Internet of Things (IoT) environments. As a result of rapid increase in the number of mobile connected devices such as smart-phones and tablets, the demand for data traffic is exponentially increasing. In order to satisfy mobile users’ requests and meet the requirements of high data traffic, mobile operators have to increase the network capacities dramatically. One of the promising solutions for the network operators to improve coverage and capacity, and provide high data rate services in a less costly manner is the deployment of femtocells related technologies. Femtocells will help mobile operators to provide a basis for the next generation of services which are a combination of voice, video, and data services to mobile users. Moreover, proper traffic modelling and deployment strategies of femtocells will improve the overall performance of the femtocell network. Therefore, in this survey paper, we overview modelling traffic and deployment strategies in femtocells and provide a review for the use of femtocells and their applications in the IoT environments. In addition, we present open research issues associated with IoT-femtocell based applications.
Additive Manufacturing Technologies: An Overview about 3D Printing Methods and Future Prospects

The use of conventional manufacturing methods is mainly limited by the size of the production run and the geometrical complexity of the component, and as a result we are occasionally forced to use processes and tools that increase the final cost of the element being produced. Additive manufacturing techniques provide major competitive advantages due to the fact that they adapt to the geometrical complexity and customised design of the part to be manufactured. The following may also be achieved according to field of application: lighter weight products, multimaterial products, ergonomic products, efficient short production runs, fewer assembly errors and, therefore, lower associated costs, lower tool investment costs, a combination of different manufacturing processes, an optimised use of materials, and a more sustainable manufacturing process. Additive manufacturing is seen as being one of the major revolutionary industrial processes of the next few years. Additive manufacturing has several alternatives ranging from simple RepRap machines to complex fused metal deposition systems. This paper will expand upon the structural design of the machines, their history, classification, the alternatives existing today, materials used and their characteristics, the technology limitations, and also the prospects that are opening up for different technologies both in the professional field of innovation and the academic field of research. It is important to say that the choice of technology is directly dependent on the particular application being planned: first the application and then the technology.
Build Your Own EV: A Rapid Energy-Aware Synthesis of Electric Vehicles

Electric vehicles (EVs) represent a quantum leap towards addressing climate-change effects and building a more sustainable environment. This article provides a comprehensive overview of the EV benefits, summarizes the main challenges in designing EVs, and describes a pioneering design automation inspired framework for rapid energy-aware electric vehicle synthesis. The opportunities for future work stimulated by these rigorous research efforts are endless. —Paul Bogdan, University of Southern California
Equity of Incentives: Agent-Based Explorations of How Social Networks Influence the Efficacy of Programs to Promote Solar Adoption

Agent-based models are used to explore how social networks influence the effectiveness of governmental programs to promote the adoption of solar photovoltaics (solar PV) by residential households. This paper examines how a common characteristic of social networks, known as network segregation, can dampen the indirect benefits of solar incentive programs that arise from peer effects. Peer effects cause an agent to be more likely to adopt a technology if they are socially connected to other adopters. Due to network segregation, programs that target relatively affluent agents can generate rapid increases in overall adoption levels but at the cost of increasing disparities in access to solar technology between rich and poor communities. These dynamics are explored through theoretical agent-based models of solar adoption within hypothetical social systems. The effectiveness of three types of solar incentive programs, the feed-in tariff, leasing programs, and seeding programs, is explored. Even though these programs promote rapid adoption in the short term, results demonstrate that network segregation can create serious distributional justice problems in the long term for some programs. The distributional justice effects are particularly severe with the feed-in tariff. Overall, this paper provides an illustration of how agent-based models may be used to evaluate and experiment with policy interventions in a virtual space, which enhances the scientific basis of policymaking.
The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial

The application scenarios and requirements are more diverse in the fifth-generation (5G) era than before. In order to successfully support the system design and deployment, accurate channel modeling is important. Ray-tracing (RT) based deterministic modeling approach is accurate with detailed angular information and is a suitable candidate for predicting time-varying channel and multiple-input multiple-output (MIMO) channel for various frequency bands. However, the computational complexity and the utility of RT are the main concerns of users. Aiming at 5G and beyond wireless communications, this paper presents a comprehensive tutorial on the design of RT and the applications. The role of RT and the state-of-the-art RT techniques are reviewed. The features of academic and commercial RT based simulators are summarized and compared. The requirements, challenges, and developing trends of RT to enable the visions are discussed. The practices of the design of high-performance RT simulation platform for 5G and beyond communications are introduced, with the publicly available high-performance cloud-based RT simulation platform as the main reference. The hardware structure, networking, workflow, data flow and fundamental functions of a flexible high-performance RT platform are discussed. The applications of high-performance RT are presented based on two 5G scenarios, i.e., a 3.5 GHz Beijing vehicle-to-infrastructure scenario and a 28 GHz Manhattan outdoor scenario. The questions on how to calibrate and validate RT based on measurements, how to apply RT for mobile communications in moving scenarios, and how to evaluate MIMO beamforming technologies are answered. This tutorial will be especially useful for researchers who work on RT algorithms development and channel modeling to meet the evaluation requirements of 5G and beyond technologies.
Diseño de un espacio semántico sobre la base de la Wikipedia. Una propuesta de análisis de la semántica latente para el idioma español

Latent Semantic Analysis (LSA) was initially conceived by the cognitive psychology at the 90s decade. Since its emergence, the LSA has been used to model cognitive processes, pointing out academic texts, compare literature works and analyse political speeches, among other applications. Taking as starting point multivariate method for dimensionality reduction, this paper propose a semantic space for Spanish language. Out results include a document text matrix with dimensions 1.3 x10^6 and 5.9x10^6, which later is decomposed into singular values. Those singular values are used to semantically words or text.
Rough set-based reduction of incomplete medical datasets by reducing the number of missing values

This paper proposes a model of: firstly, dimensionality reduction of noisy medical datasets that based on minimizing the number of missing values, which achieved by cutting the original dateset, secondly, high quality of generated reduct. The original dataset was split into two subsets; the first one contains complete records and the other one contains imputed records that previously have missing values. The reducts of the two subsets based on rough set theory are merged. The reduct of the merged attributes was constructed and tested using Rule Based and Decomposition Tree classifiers. Hepdata dataset, which has 59% of its tuples with one or more missing values, is mainly used throughout this article. The proposed algorithm performs effectively and the results are as expected. The dimension of the reduct generated by the Proposed Model (PM) is decreased by 10% comparing to the Rough Set Model (RSM). The proposed model was tested against different medical incomplete datasets. Significant and insignificant difference between RSM and PM are shown in Tables 1-5.
Dual-scale weighted structural local sparse appearance model for object tracking

It is a great challenge to develop an effective appearance model for robust visual tracking due to various interfering factors, such as pose change, occlusion, background clutter etc. More and more visual tracking methods tend to exploit the local appearance model to deal with the above challenges. In this study, the authors present a simple yet effective weighted structural local sparse appearance model, which can better describe the target appearance information through patch-based generative weight. To further improve the robustness of tracking, they implement this appearance model on two-scale patches. The two derived appearance models are then combined to form a collaborative model to play their advantages. Extensive experiments on the tracking benchmark dataset show that the proposed method performs favourably against several state-of-the-art methods.
A novel discriminant multiscale representation for ear recognition

This paper proposes a novel representation for ear recognition. It introduces a new alternative of binarised statistical image features based on multiscale framework. The proposed representation allows capturing the image content at multiple resolutions. The recognition accuracy can be enhanced by the following steps. First, for a given ear image, a set of multiscale response images are derived from the bank of binarised statistical image features (B-BSIF) filter. Second, the obtained response images are summarised by concatenating their histograms, which are obtained at each scale. Finally, a discriminative ear image representation is build by projecting the above mentioned histograms into a linear discriminant analysis subspace. The proposed representation is applied on three public databases: IIT Delhi-1, IIT Delhi-2 and USTB. The obtained recognition accuracy confirms its performance than the recent existing methods.
Physiological response of Tamarix austromongolica to NaCl stress

To understand the responses of Tamarix austromongolica Nakai to various levels of salt stress(0%,0.5%,1.0%,2.0%,4.0%) and discuss its salt tolerance,this paper conducted a study with pot experiments.NaCl at low concentration of 0.5% had tiny effect on each physiological index of the plants.Upon treatment of NaCl at high concentrations of 1.0%,2.0% and 4.0%,the plants could quickly respond at stoma adjustment,protective enzyme system and osmotic regulation substances.The plants can maintain comparatively regular growth during the early treatment period(first 8 days). With time passing and the increase of NaCl concentration,obvious effects on plants' growth show up.On the 8th day,relative conductivity of plants treated with NaCl concentration of 4.0% obviously rises to 4 times of ck and MDA content has direct correlativity with relative conductivity.Proline content of T.austromongolica Nakai under salt stress gradually rises,while soluble protein content increases in early treatment period and then gradually decreases.Activities of 3 protective enzymes all increase first and then decrease under salt stress.Activities of SOD and CAD reach the peak on the 14th day,while the activity of POD reaches the peak on the 20th day.Chlorophyll content in leaves and Pn gradually decrease and have correlativity with each other.After salt-leaching,all the indices of plants treated with NaCl concentration of 1% and 2% can be recovered,while the indices of plants treated with NaCl concentration of 4.0% can barely recover.
Learning Force-Relevant Skills from Human Demonstration

Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learning-based approach of transferring force-relevant skills from human demonstration to a robot. First, the force-relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force). Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task. Finally, the learned skill model is further integrated with an adaptive controller that offers task-consistent force adaptation during online executions. The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg-in-hole assembly.
Database corpus for Yoruba handwriting

Non-availability of Yoruba handwritten database has been a major challenge affecting the validation of the Yoruba handwritten recognition system. This paper presents an offline Yoruba handwritten database corpus for validating Yoruba handwritten word recognition system (YHWR). In this research work, 50 medical pathology words were gotten from medical pathology dictionary. The medical pathology words were translated to their Yoruba equivalence and the translated words were hand written by 200 indigenous literate writers with appropriate diacritic signs. The offline handwritten data were scanned using 300 dpi. The database corpus created; converted the scanned images to different image format, different resolutions and different image sizes, to test the effect of different resolutions, different format and image sizes on Yoruba handwritten recognition system. The digitised images were used to create Yourba handwritten database, which, could be used to validate the handwritten recognition system. The database created is considered a raw data that require some level of preprocessing before it can be used for validating the YHWR system.
Improved Coupled Tensor Factorization with Its Applications in Health Data Analysis

Coupled matrix and tensor factorizations have been successfully used in many data fusion scenarios where datasets are assumed to be exactly coupled. However, in the real world, not all the datasets share the same factor matrices, which makes joint analysis of multiple heterogeneous sources challenging. For this reason, approximate coupling or partial coupling is widely used in real-world data fusion, with exact coupling as a special case of these techniques. However, to fully address the challenge of tensor factorization, in this paper, we propose two improved coupled tensor factorization methods: one for approximately coupled datasets and the other for partially coupled datasets. A series of experiments using both simulated data and three real-world datasets demonstrate the improved accuracy of these approaches over existing baselines. In particular, when experiments on MRI data is conducted, the performance of our method is improved even by 12.47% in terms of accuracy compared with traditional methods.
Selecting optimal bands for sub-pixel target detection in hyperspectral images based on implanting synthetic targets

Target detection at sub-pixel abundances is, in fact, one of the challenging issues of hyperspectral image processing. Selection of optimal bands to improve sub-pixel target detection (STD) performance is one of the common solutions, applied by many researchers. Nevertheless, the absence of sufficient training data is the main weakness of selecting optimal bands with regard to this approach. The present research introduces a new band selection method for STD in hyperspectral images, based on creating training data, in which the desired target spectrum is implanted randomly in a series of host pixels from the entire hyperspectral image. Afterwards, via running an optimisation algorithm twice, with the aim of minimising the false alarm rate (FAR) in local adaptive coherence estimator target detection algorithm, the number of optimal bands and optimal spectral bands are selected. In this study, the performance of three optimisation methods including the genetic algorithm (GA), Grey Wolf optimisation (GWO), and particle swarm optimisation (PSO) are compared. Experimental results on HyMap and Hyperion datasets show that the proposed method obtains the minimum FAR compared with the rest of the evaluated methods. Also, based on the results obtained, GWO outperforms GA and PSO optimisation methods in the STD domain.
Analyses of parasitic capacitance effects and flicker noise of the DAC capacitor array for high resolution SAR ADCs

This paper analyses the effects of parasitic capacitances of unit capacitors on the accuracy and the noise performance of the DAC capacitor array in a SAR ADC, showing that thermal noise of the array decreases while gain error is introduced. The gain error is almost independent of the number of bits, but the dynamic range of the high resolution ADC is severely reduced due to the gain error. The post-layout parasitic capacitance analysis of a 10-bit poly-poly array shows a large difference between the top-plate and bottom-plate parasitic capacitances so that the gain error can be decreased by 152 times when top-plates are connected together as the output node of the array. The switching transistors' flicker noise calculation for a 10-bit and an 18-bit SAR ADC shows that flicker noise can be safely ignored for 10-bit 1MSPS SAR, but should be considered for the higher resolution SAR ADCs.