Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Speech Production System Based on Formant Estimation of a Tongue Articulatory System Using Human Tongue Orientation

An algorithm for a potentially non-obtrusive speech production system was developed and characterized. The algorithm is primarily based on the articulation of the human tongue referred as tongue articulatory system (TAS) and was cascaded with a previously developed laryngeal model. We developed and optimized statistical formulae for formants of vowels and consonants and studied the model for different ages and genders. The difference between the formant frequencies obtained using both the established vocal tract system and proposed cascaded system was found to be < 5%. The proposed model shows the significance of the articulatory nature of the tongue in human speech production. An algorithmic speech synthesizer was developed, and its output was matched with original speech signals for English vowels and consonants with an Normalized Root-Mean-Square deviation error (NRMSE) of < 0.15ms. Further, an experimental implementation of the developed algorithm was done, with flex-sensors emulating the tongue in an artificial oral cavity. The experimental test results further confirmed the effectiveness of the algorithm, revealing interesting features under tolerance analyses. This idea relates to a means for compensating for a whole or partial loss of speech. Such a model can be useful to interpret speech for tracheostomised patients who have undergone larynx surgery, speech-disabled due to accidents or voice disorders, medical rehabilitation and for robotics.
uMemristorToolbox: Open source framework to control memristors in Unity for ternary applications

This paper presents uMemristorToolbox, a novel open source framework that reads and writes non-volatile ternary states to memristors. The Unity (C#) framework is a port of the open source Java project Memristor-Discovery and adds a closed-loop ternary memory controller to enable both PC and real-time embedded ternary applications. We validate the closed-loop ternary memory controller in an embedded system case study with 16 M+SDC Tungsten dopant memristors. We measure an average switching speed of 3 Hz, worst case energy usage of 1 μW per switch, 0.03% random write error and no decay in (non-volatile) state retention after 15 minutes. We conclude with observations and open questions when working with memristors for ternary applications.
Investment in Research & Development or Size Expansion? The Case of Internet of Things Companies

The Internet of Things (IoT) is at the height of its hype cycle and one can argue that the IoT construct will subsume housing, infrastructures, industrial plants, and other systems in the near future. Big data that is associated with IoT could control systems, affect automation and the process industries require R&D support to shape the concept into a functional asset that resolves the problems of users. Hence, investment in research and development (R&D) as an integral part of any IoT project could be the first step towards project success. The objective of this study is to examine the effects of an investment in R&D and size expansion on the firm value of the top Internet of Things companies. This study examines the top 20 Internet of Things companies in the world spanning the period from 2012 to 2019. By using the panel regression random effect model, this study yields two main conclusions. First, the expansion of size by the companies has a significant impact on promoting the firm value. Second, investment in R&D is negatively associated with the firm value of the IoT companies at the initial stage but the lag effect of investment in R&D is associated positively with the firm value. The significant size expansion impact on the firm value in the IoT firms suggests that the agile size could lead to more efficient use of resources and easy identification of growth opportunities. The second conclusion demonstrates the need for a budget allocation for the investment in R&D for the technological progression and scientific advancement in the IoT companies. Although it takes a few years to observe the significant impacts of investment in R&D on IoT firms, in the long term, it could improve the performance of IoT companies in driving the proliferation of connected devices to enhance human productivity and efficiency.
Road Curb Detection using Traversable Ground Segmentation: Application to Autonomous Shuttle Vehicle Navigation

This work addresses the problem of road curb detection using a LIDAR 3D sensor in urban environments, which is an important task in autonomous vehicle technology. Existing road curb detection methods often rely on a ground segmentation process and geometric features extraction on ground data. Existing ground segmentation methods, used for road curb detection purposes, have been developed with the flat terrain assumption in mind. However, in real applications, the shape of the terrain is not always flat. Moreover, the surrounding environment (walls, trees, and other obstacles) and the sensor orientation might influence the results of the existing ground segmentation methods. Thus, we propose an efficient ground segmentation algorithm to extract points belonging to the road surface, road curbs, sidewalks, etc. Geometrical features are used to detect road curb like changes in LIDAR 3D points. However, geometrical features cannot distinguish between road curbs, small obstacles and, terrain variations whose shape is similar to road curbs. Then, in addition to geometrical features commonly used in curb detection methods, we use the LIDAR 3D reflectance feature to detect road curbs. Experiments on an autonomous shuttle vehicle demonstrate that the proposed method is capable of detecting road curbs on roads with complicated geometry such as curved roads, roundabouts, and banked turns.
Improved visual inspection for nozzle inner radius based on panoramic imaging

Visual inspections of Nuclear Power Plant (NPP) reactors are important for understanding the current condition of the reactor components, such as nozzle inner radius. After addressing current deficiencies of the existing visual inspection methods, a panoramic image mosaicking method is proposed in this study to improve the efficiency and reliability of detection. However, unlike traditional mosaicking methods, the proposed method obtains the variation of the pixels between frames in a video by establishing a geometric mapping relationship between scenes and images. Then, the pixels which are varied between frames will be extracted and the frames will be combined to generate the mosaicked images. A function to discriminate overexposed pixels is also derived based on the redundant pixels in a series of frames, to reduce the impact of specular reflection on the resulting images. These above methods are applied to the detection of defects on nozzle inner radius of the mock-up. The resulting 360° panoramic images provide a large-field-of-view defect analysis approach to obtaining geometric measurements with pixel-level precision, and the image optimization method is found to mitigate the effects of specular reflection on defect identification by providing clear and reliable images. The results of this study demonstrate the promise of this new visual inspection method for NPP reactors, and establishes a potential foundation for the automatic detection of surface defects. Contribution-A method is presented to improve the reliability of defect detection, and it has been applied to the visual inspection for nozzle inner radius. Contribution-A method is presented to improve the reliability of defect detection, and it has been applied to the visual inspection for nozzle inner radius.
Synthesizing Cell Protein data for Human Protein Cell Profiling Using Dual Deep Generative Modeling

To understand the biology of health, and how molecular dysfunction leads to disease, knowledge of the human cell is essential. The protein is the core unit of the human body made from trillions of cells, forming the body's various tissues. These tissues come together to create human organs. It is essential to understand the Spatio-temporal distribution of proteins in cells and to investigate human RNA-sequencing for human genes characterization. For this, it requires a massive amount of annotated data. However, due to many considerations like the high cost of data sample collection, lack of data sample availability, and lawful clauses for patient privacy, the majority of medical data is out of reach for general public research. In this study, we propose a new dual deep generative method for synthesizing human cell protein images by using the Generative Adversarial Network technique. Specifically, for that, we pair original cell protein images with their respective Cell-protein-tree. These pairs are then used to learn the mapping from a binary cell protein to a new cell protein image. For this purpose, we use an image-to-image translation technique based on adversarial learning. The generated cell protein images are expected to preserve the structural and visual quality of the training images. Visual and quantitative analysis of the experimental results demonstrates that the synthesized data are preserving the desired quality while maintaining the different forms of original data. Contribution-We have proposed a new dual deep generative model for synthesizing cell protein data.
Aperture Field Recovery of a Reflector Radio Telescope using Phase Shifting Holography

A new method was recently proposed for reconstructing the aperture field of a reflector radio telescope with an active surface by recording three holograms having fixed phase shifts of the reference wave. We discuss the requirements to the noise characteristics of the antenna system, transmitter, and receiver that is necessary for the successful application of the method to the radio telescope surface diagnostics. In order to achieve the necessary SNR two horn transmitter or receiver scheme is proposed for RATAN-600 or a space radio telescope with an active surface. We present the results of numerical simulations, as well as the initial experimental results radio telescope.
Hybrid Nearest Neighbour and Feed-forward Neural Networks Algorithm for Indoor Positioning System

Accurate localization of people and mobile devices in building plays important roles in order to deliver helpful location-based services such as direction and object finding, and content delivery. This paper presents a hybrid feed-forward neural networks (FNNs)-based indoor localization system trained by means of recently developed metaheuristic algorithm. The received signal strength (RSS) from the access points (APs) is employed as input to the proposed algorithm to estimate the user location in the wireless local area network (LAN) environment. The performance of developed hybrid $\pmb{k}$ NN-FNNs algorithms were compared with the basic $\pmb{k}$-NN algorithm in term of error distance achieved. The comparative study has shown that the developed hybrid $\pmb{k}$-NN-FNNs algorithm is more efficient when estimating the user location with accuracy of 86.39 percent in comparison to its predecessor algorithm (with 69.67 percent) as the error distance was minimized using metaheuristic algorithm.
Fish Keypoints Detection for Ecology Monitoring Based on Underwater Visual Intelligence

This paper introduces a fishery ecology monitoring system for cultivation pools, and proposes a new stereo keypoint detection method followed by curve fitting analysis to estimate the fish posture and length. The system, which can be employed for aquaculture monitoring, is featured by its exploitation of underwater visual intelligence and deep neural-network architecture. As input, stereo image pairs are obtained by underwater binocular camera. A deep neural-network under Faster R-CNN architecture is built to detect fish from the stereo image inputs. Another network under Stacked Hourglass architecture is constructed to detect specific keypoints of each fish. For ecology monitoring, detected keypoints are used in the estimation of the fishes' posture and length. Unlike other size estimation methods which also apply a binocular camera, our method naturally bypasses the pixel-wise matching difficulty in global stereo matching algorithms. Experiment shows that our system is applicable for online fish ecology monitoring, with efficient and accurate estimation performance.
Formalization of Interstate Traffic Rules in Temporal Logic

To allow autonomous vehicles to safely participate in traffic and to avoid liability claims for car manufacturers, autonomous vehicles must obey traffic rules. However, current traffic rules are not formulated in a precise and mathematical way, so that they cannot be directly applied to autonomous vehicles. Additionally, several legal sources other than national traffic laws must be considered to infer detailed traffic rules. Thus, we formalize traffic rules for interstates based on the German Road Traffic Regulation, the Vienna Convention on Road Traffic, and legal decisions from courts. This makes it possible to automatically and unambiguously check whether traffic rules are being met by autonomous vehicles. Temporal logic is used to express the obtained rules mathematically. Our formalized traffic rules are evaluated for recorded data on more than 2,500 vehicles.
High Precision Vehicle Localization based on Tightly-coupled Visual Odometry and Vector HD Map

Matching low-cost camera and vector HD map is proven to be a practical and effective way of estimating the location and orientation of intelligent vehicles. However, map-based approach is viable only when the landmark observation is adequate and precise. In some areas with sparse and noisy observation, or even non-existent map matching features, the localization results may be unstable. In this paper, we introduce a novel algorithm by fusing visual odometry and vector HD map in a tightly-coupled optimization framework to tackle these problems. Our algorithm exploits the observation of visual feature points and vector HD map landmarks in the sliding window manner and optimize their residuals in a tightly-coupled approach. In this way, the system is more robust against the noisy HD map landmark observations. In addition, our method is able to accurately estimate vehicle pose even when landmarks are sparse. Experiments under two challenging scenarios with noisy and sparse landmark observations show that our method can achieve the Mean Absolute Error (MAE) at 0.1473m and 0.2496m respectively.
Effect of Electromagnetic Stimulation on Selected Fabaceae Plants

The effects of short exposure of seeds of lupine (Lupinus L.) and lucerne (Medicago sativa L.) to a lowfrequency magnetic field (f=50 Hz, B= 30 mT), laser-light radiation (λ = 632.8 nm), and their combination on germination parameters and the content of photosynthetic pigments were examined. Independent studies were done on Petri dishes and in pots. As shown by data, short-term pre-sowing treatment with the physical factors mentioned above in most cases neither influences the germination parameters of the examined plants (no statistical differences between most of the samples) nor produces any negative effects. Generally, an increase in the content of photosynthetic pigments was observed for lupine while no statistically relevant effect of pre-sowing stimulation on lucerne was observed in the laboratory experiment. The pot experiment on lucerne showed statistically relevant differences. Comparing our data and the results obtained by other groups, we conclude that a short-term stimulation of selected Fabaceae with electromagnetic factors does not significantly influence the germination parameters or the content of their photosynthetic pigments. No statistical differences were found between coarse-grained and small-seed plants.
A Novel Genetic Model for Drone Positioning in Wireless Sensor Networks

Society is investing in Wireless Sensor Networks (WSN) in rural areas to protect the environment and agriculture while increasing security and enhance urban mobility in urban areas. In this context, Unmanned Aerial Vehicles (UAV) can be used for the collection of data recorded by sensors. However, the use of UAVs has introduced new challenges to be mitigated. In particular, in the WSN field, UAVs must follow strategies to increase the lifetime of the network, while also decrease the energy spent to realize the data transfer. Thus, in this work, we propose and evaluate a novel genetic algorithm considering the most important requirements listed in the literature. Experimental evaluations with different WSN setups show that the proposed genetic model is more effective in optimizing the network lifetime than classical strategies presented in the literature.
To Embody or Not: A Cross Human-Robot and Human-Computer Interaction (HRI/HCI) Study on the Efficacy of Physical Embodiment

A plethora of commercial social robots and social robotics startups have risen over the last few years. At a cursory glance, most such robots are merely conversational agents, essentially offering a similar or subset of the capabilities of a smart communication device embodied in a mobile/semi-mobile robotic platform. This raises the question of the efficacy of such an approach. In this paper, we explore embodiment using a social, in-the-wild interaction scenario, comparing a Human-Computer and Human-Robot context. A public site has been deliberately chosen to highlight the importance of conducting such user studies in unconstrained social settings as opposed to in controlled lab settings. Increasing evidence suggest the lack of generalizability of lab-based results in the wild, which we argue as a reason for misguided commercialization of social robots and their eventual commercial failures. The results have implications for the longterm commercial viability of such social robots.
Tracking The Development Of Six Wheat Varieties Using Infrared Imaging And Image Processing Algorithms

The current paper presents a study on the growth of six varieties of wheat. The complete growth cycle -from germination to maturation is tracked. The observations are performed on a dedicated experimental field, where the different wheat varieties are sowed based on a specialized plan. The monitoring technique used to track the growth is presented in details. It includes the application of infrared imaging and specialized image processing that allows to record and estimate vegetation indexes. Using those indexes evaluation of the growth and development of the wheat is made and presented.
Towards harnessing based learning algorithms for tweets sentiment analysis

In the digital era of information, the number of web users and the volume of data being generated and exchanged over the internet has drastically increased. Sentiment analysis and opinion mining are open research fields having various everyday life applications, such as Twitter. Twitter is flooded with opinions, emotions, views and discussions among different communities across the world. Therefore, the tweet is related to various domains, such as stock market, product opinion and political elections. In this study, a classification problem is considered having a given set of tweets with various features and diverse opinions. The goal is to obtain expressions of opinion representing a target feature and to categorize these tweets as positive, neutral, or negative sentiment using different machine learning techniques. The dataset sentiment140 (STSTest) is preprocessed and very commonly used for research purposes. The studied machine learning classifiers are random Forest, libSVM, IBk (kNN), decision table, and AdaBoostMl. Several evaluation measures are used for evaluating the performance of studied classifier on the selected dataset. These measures include accuracy, precision, recall, and F-measure. The confusion matrix is also obtained as a result of each classifier. The obtained results revealed that the RF classifier obtained the best accuracy results whereas the AdaBoostMl classifier obtained the worst accuracy results when tested for STS-Test dataset.
Word2Vec Duplicate Bug Records Identification Prediction Using Tensorflow

Bug duplication reporting is one of the most widespread software problems that cause inconvenience for the internal software stakeholders. It is useful for developers to eliminate redundant bug records where the fewer bugs duplicated records in bug reports documentation the more efficiently allocated resources are set to fix and enhance the software features. In this paper, the word embedding (Word2Vec) approach is used on four different software components from the Mozilla Core dataset with different sentence types through the duplicated bug category records to compare whether two given bug record descriptions are categorized as related bugs records. Besides, this paper proposes three different similarity measures and explores the accuracy of each measure. The study results show that the approach’s accuracy is proportional to the existence of similar words within any of the two given two bug records descriptions. Additionally, we found that percentage of similarity accuracy is improved by finding the closest word using the Euclidean distance method than traversing for more index adjacent values within the trained word vector array.
Speech Enhancement via Attention Masking Network (SEAMNET): An End-to-End System for Joint Suppression of Noise and Reverberation

This paper proposes the Speech Enhancement via Attention Masking Network (SEAMNET), a neural network-based end-to-end single-channel speech enhancement system designed for joint suppression of noise and reverberation. It formalizes an end-to-end network architecture, referred to as b-Net, which accomplishes noise suppression through attention masking in a learned embedding space. A key contribution of SEAMNET is that the b-Net architecture contains both an enhancement and an autoencoder path. This paper proposes a novel loss function which simultaneously trains both the enhancement and the autoencoder paths, so that disabling the masking mechanism during inference causes SEAMNET to reconstruct the input speech signal. This allows dynamic control of the level of suppression applied by SEAMNET via a minimum gain level, which is not possible in other state-of-the-art approaches to end-to-end speech enhancement. This paper also proposes a perceptually-motivated waveform distance measure. In addition to the b-Net architecture, this paper proposes a novel method for designing target waveforms for network training, so that joint suppression of additive noise and reverberation can be performed by an end-to-end enhancement system, which has not been previously possible. Experimental results show the SEAMNET system to outperform a variety of state-of-the-art baselines systems, both in terms of objective speech quality measures and subjective listening tests. Finally, this paper draws parallels between SEAMNET and conventional statistical model-based enhancement approaches, offering interpretability of many network components.
Further research on optimal interval observer design for switched systems

This paper focuses on the research on interval estimate method for discrete-time linear time-invariant switched systems. When real-time state of the system is estimated, it is necessary to eliminate the influence of external interference on the estimation results and consider the problem of switching modes. For these reasons, the $H_{\infty}$ observer design is combined with reachability analysis. First, we need to design a $H_{\infty}$ observer so as to reduce the impact of external disturbances. Then, the observers based on reachability analysis technology are applied in it to obtain the interval of the states. In the end, a simulation for one example is completed to test the quality of the interval observer.