Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Optimization of public WLANs

Now-A days WLAN is used in most of public place and workplaces. Many researchers have published their work on optimizing signal strength of WLANs in homes and work places, but has not been extensively investigated the WLANs at public places. This paper describes the analysis and optimization of the Wi-Fi signal strength and measurements on public WLANs. Experiments have been conducted at railway station, Pune as a case studies. Several measurements have been taken and the influence of various factors on the strength of the Wi-Fi signal and on the download and upload speed of the WLAN have been analyzed. Solutions have been proposed to have complete coverage of intended area of the public places.
Ultra-thin actives for embedded components: halfway between thin film technology and embedded Surface Mounted Device.

Among the electronic assembly techniques, the embedded technology allows new compact architectures with active and passive components integrated into internal layers of printed circuit boards (PCBs) [1]. Embedded components increase both integration density and performance of electronic boards in particular RF performances and electromagnetic compatibility (EMC). For more than a decade, thin film technology used for embedded passives is defined as one of the key trends in downsizing. In addition to the advantages mentioned previously, the reliability is significantly improved because of the absence of solder joint. If the embedding of thin film passives in PCBs is now well known, very few studies have been performed on active components [2]. In this study a similar approach is considered for actives and thin film passives: the active components are embedded into the PCB without any package and are thinned with a specific method to ensure the mechanical handling. Consequently the PCB substitutes the classic housing of actives [3]. Moreover a dedicated manufacturing process makes it possible to replace solder joints with copper vias. In order to assess the reliability of the assemblies, simulations based on Finite Element Method (FEM) are performed to study the thermo-mechanical behavior of such innovative embedded active components during its operating lifetime considering the manufacturing process conditions. Indeed for the first time manufacturing stresses on assemblies are evaluated in a global multilayers PCB with bared and thinned embedded active components. The objectives of the present study are to determine a realistic assessment of the estimated lifetime of such assemblies with innovate embedded active components and to compare with classical actives. This study is carried out in the framework of a European project in collaboration with VALEO (automotive equipment supplier). Indeed, the requirements in reliability for VALEO are substantial which justifies the need for a predictive approach. In the medium term the aim is to make general design rules for actives and passives embedded in several layers of multilayered PCBs as part of a Design for Reliability (DFR).
A Novel Topology for Process Variation-Tolerant Piezoelectric Micromachined Ultrasonic Transducers

This paper presents a novel architecture for piezoelectric micromachined ultrasonic transducers (PMUT) allowing for a drastic reduction in the impact of process variations on the accuracy of the resonant frequency. At the core of this new topology is a toroidal anchoring technique. Measurement results show that inter- and intra-die resonant frequency standard variations can be reduced from 101 kHz to 23 kHz and from 20 kHz to 5.9 kHz, respectively, by using the method proposed in this paper, showcasing devices fabricated using the PiezoMUMPs commercial fabrication technology. From the 16 chips that have been fabricated, each holding 12 PMUT devices of two different topologies, resonant frequency results are presented and analyzed to assess the effectiveness of the proposed approach. In addition, as a proof of concept, the fabricated PMUT are demonstrated to perform distance ranging measurements. [2018-0132]
Simple Admittance Expression Derivation of an Electrically Dense Loaded Slot Array at a Material Interface

The equivalent circuit admittance of a lumped element periodically loaded and electrically dense array of slots at a material interface is derived for both parallel and perpendicular polarizations. The derivation is based on the methodology of Casey for deriving the equivalent circuit impedance of an unloaded wire mesh at a dielectric interface using the Wait–Hill formulation. Using the derived equivalent circuit admittance, a Booker-type relation for unloaded wire grid and slot arrays, at general isotropic media interfaces, is obtained.
Automatic Singable Tamil Lyric Generation for a Situation and Tune Based on Causal Effect

Poems reveal the linguistic capability and creativity of a person. Poets being driven by a creative impulse, frame imaginative poems that narrate a story, and express emotions in an artistic manner. Poets in film industries write poems considering a situation in the movie and the tune composed by the music director. In this paper, we discuss the design and analysis of an automatic lyric generation system for the Tamil language, which takes scene description (in prosaic form) and tune (in swara format) as inputs, and generates lyrics with the necessary poetic components, such as alliteration and simile, which adhere to the situation and the tune. The idea of the input scenario, which drives the lyric generation process is extracted by identifying causal-effect relationships. Also, the dialect of the speaker (in the input scene) will be reflected in the lyrics, if it is specified in the input scene.
Review of Concurrent Engineering Design practice in the space sector: state of the art and future perspectives

We present a survey conducted across industry, government agencies and academic organizations running concurrent design facilities for space applications, to analyze the differences in people management, tools, processes and facilities across many organizations using concurrent design. Our analysis is based on a literature review and structured interviews. Most organizations applying concurrent design also participate in related conferences and publish on their experiences with the approach. We collected data through a survey among subject matter experts involved in various roles in this type of concurrent design. The answers are evaluated quantitatively to allow a clear characterization of the state-of the-art. The data underpins common ideas about benefits and challenges of the concurrent design approach with data. We compiled statistics about minimal, typical and maximal team sizes, study duration, as well as size and critical features of facilities, and many more aspects. This review summarizes the learnings from concurrent design practice and describes open challenges to be addressed in future research.
High Frequency Noise Detection and Handling in ECG Signals

After acquisition of new clinical electrocardiogram (ECG) signals the first step is often to preprocess and have a signal quality assessment to uncover noise. There might be restriction on the signal length and other issue that impose limitation where it is not possible to discard the whole signal if noise is present. Thus there is a great need to retain as much noise free regions as possible. A noise detection method is evaluated on a manually annotated subset (2146 leads) of a data base of 12-lead ECG recordings from 1006 bicycle race participants. The aim is to apply the noise detector on the unlabelled part of the data set before any further analysis is conducted. The proposed noise detector can be divided into 3 parts: 1) Select a high frequency signal as a base signal. 2) Apply a thresholding strategy on the base signal. 3) Use a noise detection strategy. In this work receiver operating characteristic (ROC) curve and area under the curve (AUC) will be used to assess a high frequency noise detector designed for ECG signals. Even though ROC analysis is widely used to assess prediction models, it has its own limitation. However, it is a good starting point to assess discriminatory ability. To generate the ROC curve the performance evaluation is based on sample-level. That is, each sample has a label whether it is noise or not. The threshold strategy and the chosen threshold will be the varying factor to generate ROC curves. The best model has an average AUC of 0.862, which shows a good detector to discriminate noise. This threshold strategy will be used for noise detection on the unlabelled part of the data set.
On Mugshot-based Arbitrary View Face Recognition

Despite the wide usage of mugshot images in forensic applications, they are underutilized in existing automated face recognition systems. In this paper, we propose a novel mugshot-based arbitrary view face recognition method. Our approach reconstructs full 3D faces via cascaded regression in shape space with efficient seamless texture recovery. Unlike existing methods, it makes full use of the frontal and profile views available in mugshot images, and thus generates accurate and realistic 3D faces. Multi-view face images are synthesized from the reconstructed 3D faces to enlarge the gallery so that arbitrary view faces can be better recognized. Evaluation experiments were conducted on BFM and Multi-PIE databases by using state-of-the-art deep learning (DL) based face matchers. The results demonstrate the effectiveness of our proposed method and show that DL-based face matchers can benefit from mugshot images and the reconstructed 3D faces, especially for recognizing large off-angle faces.
Clinical Utility of Beck Anxiety Inventory in Clinical and Nonclinical Korean Samples

Despite the prominent use of the Beck Anxiety Inventory (BAI) in primary healthcare systems, few studies have confirmed its diagnostic utility and psychometric properties in non-Western countries. This study aims to clarify the clinical utility of the BAI as a screening tool for anxiety disorders according to DSM-IV criteria, based on blind recruitment and diagnostic interviews of both clinical and non-clinical participants in the Korean population. A total of 1,157 participants were involved in the final psychometric analysis, which included correlational analysis with other anxiety and depression self-report measures and mean score comparison with the Beck Depression Inventory (BDI). ROC analysis and calculation of positive and negative predictive values were conducted to examine diagnostic utility. The BAI was found to have high correlations with depression-related self-report measures (0.747–0.796) and moderate to high correlations with anxiety-related self-report measures (0.518–0.776). The ROC analysis failed to provide cutoff scores with adequate sensitivity and specificity for identifying participants with anxiety disorders (85.0% sensitivity, 88.1% specificity, and 92.8% AUC). The comparison of BAI and BDI mean scores for different diagnostic groups revealed that BAI and BDI scores were higher in the depressive or anxiety disorders group than in the non-clinical group. However, BAI mean score was not higher for the anxiety-only group than the depression-only group. Our data supports the BAI reliability and validity as a tool to measure the severity of general anxiety in clinical and non-clinical populations; however, it fails to capture the unique characteristics of anxiety disorders that distinguish them from depressive disorders. Further clinical implications of the BAI based on these results and some limitations of the study are discussed.
Model Predictive Control of Dual Three-Phase Interior Permanent Magnet Synchronous Generator for More Electrical Aircraft

The dual three-phase machine drive system has been widely studied for its high power density and high reliability. The fault tolerance ability of the dual three-phase machine makes it suitable for the more electric aircraft (MEA). The MEA-used power system requires a low dc-link voltage ripple and torque ripple. This paper studies a high performance dual three-phase interior permanent magnet synchronous generator (IPMSG) drives for MEA. In addition, simulations have verified the performance. The dc-link voltage ripple is less than 0.2%.
Active Directional Modifier Adaptation with Trust Region- Application to Energy-Harvesting Kites

Many real-time optimization schemes maximize process performance by performing a model-based optimization. However, due to plant-model mismatch, the model-based solution is often suboptimal. In modifier adaptation, measurements are used to correct the model in such a way that the first-order necessary conditions of optimality are satisfied for the plant. However, performing experiments to obtain measurements can be costly. This paper uses a sensitivity analysis that allows making only partial corrections to the model, thereby relying on fewer experiments. Furthermore, this sensitivity analysis is of global nature, which ensures that the corrections are sufficient in the presence of large parametric uncertainties. However, since the corrections are still only locally valid, this paper proposes to control the update step length via a trustregion technique. The resulting algorithm is illustrated via the simulation of an energy-harvesting kite.
Egocentric Shopping Cart Localization

This work investigates the new problem of image-based egocentric shopping cart localization in retail stores. The contribution of our work is two-fold. First, we propose a novel large-scale dataset for image-based egocentric shopping cart localization. The dataset has been collected using cameras placed on shopping carts in a large retail store. It contains a total of 19,531 image frames, each labelled with its six Degrees Of Freedom pose. We study the localization problem by analysing how cart locations should be represented and estimated, and how to assess the localization results. Second, we benchmark different image-based techniques to address the task. Specifically, we investigate two families of algorithms: classic methods based on image retrieval and emerging methods based on regression. Experimental results show that methods based on image retrieval largely outperform regression-based approaches. We also point out that deep metric learning techniques allow to learn better visual representations w.r. t. other architectures, and are useful to improve the localization results of both retrieval-based and regression-based approaches. Our findings suggest that deep metric learning techniques can help bridge the gap between retrieval-based and regression-based methods.
Consensus of overflowing clocks via repulsive Laplacian laws

The main objective of this paper consists in imposing consensus in a network of overflowing clocks with identical speeds but potentially different initial offsets. Each (overflowing) clock is modeled as a single integrator with the state confined in a bounded set such that, whenever the state reaches its maximum allowed value, it is immediately reset to zero (overflowing phenomenon), thus exhibiting both continuous-time and discrete-time behaviours. In this framework, control techniques inspired by the classical Laplacian philosophy lead to a somewhat unexpected result. In fact, it is shown that both an attractive and a repulsive Laplacian law induce two periodic orbits of the closed-loop system, characterized by the feature that along only one of these trajectories consensus is reached. It is then proved that the error-zeroing periodic orbit is unstable with the attractive Laplacian, hence agreement of the clocks is not achieved, while an asymptotic convergence on it is guaranteed with the repulsive Laplacian, hence consensus is reached with a repulsive control law among the clocks.
Fast and Robust Pose Estimation Algorithm for Bin Picking Using Point Pair Feature

Bin picking refers to picking up the objects randomly piled in the container (bin) and robotic bin picking is always used to improve the industrial production efficiency. A pose estimation algorithm is necessary to tell the poses of the objects to the robot. This paper proposes a pose estimation algorithm for bin picking using 3D point cloud data. Point Pair Feature algorithm is performed in a fast way to propose possible poses and the poses are verified by a voxel-based verification method. Iterative Closest Point is used to refine the result poses. Our algorithm is proved to be more accurate and faster than Curve Set Feature algorithm and Point Pair Feature algorithm, robust to occlusion and able to detect multiple poses in one scene.
Affective Computational Model to Extract Natural Affective States of Students With Asperger Syndrome (AS) in Computer-Based Learning Environment

This paper was inspired by looking at the central role of emotion in the learning process, its impact on students’ performance; as well as the lack of affective computing models to detect and infer affective-cognitive states in real time for students with and without Asperger Syndrome (AS). This model overcomes gaps in other models that were designed for people with autism, which needed the use of sensors or physiological instrumentations to collect data. The model uses a webcam to capture students’ affective-cognitive states of confidence, uncertainty, engagement, anxiety, and boredom. These states have a dominant effect on the learning process. The model was trained and tested on a natural-spontaneous affective dataset for students with and without AS, which was collected for this purpose. The dataset was collected in an uncontrolled environment and included variations in culture, ethnicity, gender, facial and hairstyle, head movement, talking, glasses, illumination changes, and background variation. The model structure used deep learning (DL) techniques like convolutional neural network and long short-term memory. The DL is the-state-of-art tool that used to reduce data dimensionality and capturing non-linear complex features from simpler representations. The affective model provides reliable results with accuracy 90.06%. This model is the first model to detected affective states for adult students with AS without physiological or wearable instruments. For the first time, the occlusions in this model, like hand over face or head were considered an important indicator for affective states like boredom, anxiety, and uncertainty. These occlusions have been ignored in most other affective models. The essential information channels in this model are facial expressions, head movement, and eye gaze. The model can serve as an aided-technology for tutors to monitor and detect the behaviors of all students at the same time and help in predicting negative affective states during learning process.
Generative Band Feature Enhancement for Hyperspectral Image Classification

In this paper, we propose a generative method for the feature enhancement of hyperspectral image bands. The method can significantly improve the discriminative information and visible quality of hyperspectral image. Based on the generative adversarial networks scheme, randomly sampled small band subset from original hyperspectral image cube can be used to disentangle spectral signals from noisy bands and generate new bands which achieves much better performance in land-cover classification. Experiments on real hyperspectral datasets demonstrate the effectiveness of the generative band feature enhancement method.
A Document Clustering Approach for Automatic Building of Ontologies

In the context of globalization, companies need to capitalize on their knowledge. The knowledge of a company is present in two forms tacit and explicit. Explicit knowledge represents all formalized information i.e all documents (pdf, words …). Tacit knowledge is present in documents and mind of employees, this kind of knowledge is not formalized, it needs a reasoning process to discover it. In this paper, we propose a novel approach for documents clustering that is based on word clusters automatically extracted from the documents. The word concepts are considered as concepts candidates. In a second step, word clusters are used to ease the automatic building of ontologies from a given corpus. Some experiments allows for a validation of the whole approach. The chosen corpus is Reuters-21578 that is among the most used for text categorization research.
Hospital Information System (HIS) Performance in the Post-implementation Stage

Hospital Information Systems (HIS) have been used widely in different healthcare settings and yet not many of them can gain the expected benefits of it. Despite the initial successful adoption, many evidences suggest that hospitals are not able to fully utilize the advantages of HIS in post-implementation stage. This paper aims to discover the influential dimension and their respective factors affecting HIS performance in the post-implementation stage. A quantitative study of Iranian public hospitals was performed to determine the influential dimensions as organization, technology and continuous process improvement and their respective factors including user participation, efficiency, user training, competency of HIS internal team, and inter-department collaboration/communication. Potential dimensional factors describing the successful use of HIS to improve hospital performance in the post-implementation stage was proposed and tested through administration of a questionnaire in 16 public hospitals affiliated to Tehran University of Medical Sciences. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22 with data scored in Likert Scale (score 1–5). The results show that there are two influential dimensions including HIS use and continuous process improvement having positive effect on successful performance of HIS in post-implementation stage. Findings of this study reveal how HIS is exploited in Iranian hospitals and giving added values to their users. Despite the focus of many HIS literature on system adoption, this paper discovers the post-implementation stage and tries to shed light on the actual use of HIS and its performance under the influence of its predominant factors.
Nonnegative and Adaptive Multi-view Clustering

This paper proposes a novel Nonnegative and Adaptive Multi-view Clustering (NAMC) method. NAMC integrates the nonnegative matrix factorization (NMF), adaptive neighborhood learning and consensus adaptive similarity matrix fusion. More specifically, NAMC performs the nonnegative weight learning over the original data and the parts-based representations of NMF for more accurate measure and representation. For nonnegative adaptive feature extraction, our model first utilizes NMF to obtain the local parts-based representation of the original high-dimensional data. To keep the local structure of parts-based representations, we minimize the adaptive neighborhood reconstruction error. Then the optimal consensus similarity matrix can be iteratively obtained according to the nonnegative adaptive similarity matrix of each view. What's more, the proposed NAMC is totally self-weighted. Once the target graph is obtained in our model, it can be partitioned into specific clusters directly. Extensive simulations show that NAMC can achieve good performance on several public databases for multi-view clustering, compared with other related methods.