Search results for "Mach"
showing 10 items of 3360 documents
Assessment of microalgae species, biomass and distribution from spectral images using a convolution neural network
2021
Artikkeliin "Assessment of microalgae species, biomass and distribution from spectral images using a convolution neural network" liittyvä aineisto koostuu seuraavista osista: 1.Transmittanssi-hyperspektrikuvat levänäytteistä kuvattuina 24-kuoppalevyllä 2.Biomassamääritykset elektronisella solulaskurilla 3.Opetus- ja validointiaineisto konvoluutioneuroverkolle 4.Testiaineisto konvoluutioneuroverkolle 5.Opetus-, validointi- ja testiaineiston käsittelyyn käytetty Python koodi 6.Seitsemään eri malliin käytetty Python koodi ja mallit itsessään The data and code related to the article "Assessment of microalgae species, biomass and distribution from spectral images using a convolution neural netwo…
Anomaly detection in wireless sensor networks
2016
Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, WSNs are being used in almost every part of life. The cost effective nature of WSNs is beneficial for environmental monitoring, production facilities and security monitoring. At the same time WSNs are vulnerable to security breaches, attacks and information leakage. Anomaly detection techniques are used to detect such activities over the network that do not conform to the normal behavior of the network communication. Supervised Machine learning approach is one way to detect…
Samsung and Volkswagen Crisis Communication in Facebook and Twitter : A Comparative Study
2017
Since September 2015 at least two major crises have emerged where major industrial companies producing consumer products have been involved. In September 2015 diesel cars manufactured by Volkswagen turned out to be equipped with cheating software that caused NO2 and other emission values to be reduced to acceptable levels while tested from the real, unacceptable values in normal use. In August 2016 reports began to appear that the battery of a new smart phone produced by Samsung, Galaxy Note7, could begin to burn, or even explode, while the device was on. In Nov. 2016 also 34 washing machine models were reported to have caused damages due to disintegration. In all cases, the companies have …
Is There Any Hope for Developing Automated Translation Technology for Sign Languages?
2021
This article discusses the prerequisites for the machine translation of sign languages. The topic is complex, including questions relating to technology, interaction design, linguistics and culture. At the moment, despite the affordances provided by the technology, automated translation between signed and spoken languages – or between sign languages – is not possible. The very need of such translation and its associated technology can also be questioned. Yet, we believe that contributing to the improvement of sign language detection, processing and even sign language translation to spoken languages in the future is a matter that should not be abandoned. However, we argue that this work shou…
ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING
2020
Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potentia…
Minimal learning machine in anomaly detection from hyperspectral images
2020
Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.
All-Organic Waveguide Sensor for Volatile Solvent Sensing
2019
This work was supported by ERDF 1.1.1.1 Activity Project Nr. 1.1.1.1/16/A/046 “Application assessment of novel organic materials by prototyping of photonic devices”. We acknowledge Igors MIHAILOVS for valuable discussions.
DIGITAL PHOTOGRAMMETRY, TLS SURVEY and 3D MODELLING for VR and AR APPLICATIONS in CH
2020
Abstract. The world of valorization of Cultural Heritage is even more focused on the virtual representation and reconstructions of digital 3D models of monuments and archaeological sites. In this scenario the quality and the performances offered by the virtual reality (VR) and augmented reality (AR) navigation take primary importance, improving the accessibility of cultural sites where the real access is not allowed for natural conditions or human possibilities. The creation of a virtual environment useful for these purposes requires a specific workflow to follow, combining different strategies in the fields of survey, 3D modelling and virtual navigation. In this work a specific case of stu…
Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs
2019
Abstract The T cell repertoire is composed of T cell receptors (TCR) selected by their cognate MHC-peptides and naive TCR that do not bind known peptides. While the task of distinguishing a peptide-binding TCR from a naive TCR unlikely to bind any peptide can be performed using sequence motifs, distinguishing between TCRs binding different peptides requires more advanced methods. Such a prediction is the key for using TCR repertoires as disease-specific biomarkers. We here used large scale TCR-peptide dictionaries with state-of-the-art natural language processing (NLP) methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific classifier to predict which TCR binds to which…
Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space
2019
Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this "convergence" of adaptive immunity among different individuals remain poorly understood. To quantitatively describe epitope immunogenicity, here we propose a supervised machine learning framework generating probabilistic estimates of immunogenicity, termed "immunogenicity scores," based on the numerical features computed through sequence-based simulation approximating the molecular scanning process of peptides presented onto major histocompatibility complex (MHC) by the human T cell receptor (T…