Compressively Sensed Image Recognition

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201

Selection of antagonistic actinomycete isolates as biocontrol agents against root-rot fungi

In this study, actinomycetes isolates, isolated from rhizosphere of wheat (Triticum aestivum L.), were screened for antagonistic activities on certain root rot fungi (Fusarium culmorum, Fusarium graminearum, Fusarium verticilloides and Bipolaris sorokiniana). The  in vitro antagonistic effects of actinomycetes isolates were determined on solid media against fungal pathogens. The inhibition mechanism, effect of application time and pH on inhibition was investigated. The actinomycete isolate 129.01 exhibited a high inhibition ratio of more than 60 % against all fungi. The activity of the isolate 129.01 against root rot fungi was tested under greenhouse conditions. The root rot score (1-10), mean plant height (cm) and mean weight of green part of plant (g) were determined after an incubation period. The root rot score of the infected plants was decreased significantly by this isolate, even if the plants were inoculated with all of the pathogen fungi together (P<0.05). The results indicate that isolate 129.01 could be useful as a biocontrol agent. The assignment of the isolate 129.01 to the genus Streptomyces was supported by 16S rRNA analysis.Fil: Erginbas, Gul. Centro Internacional de Mejoramiento de Maíz y Trigo; TurquíaFil: Yamac, Mustafa. Eskisehir Osmangazi University; TurquíaFil: Amoroso, Maria Julia del R.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Planta Piloto de Procesos Industriales Microbiológicos; ArgentinaFil: Cuozzo, Sergio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Planta Piloto de Procesos Industriales Microbiológicos; Argentin

Detecting the point of release of virtual projectiles in AR/VR

Our aim is to detect the point of release of a thrown virtual projectile in VR/AR. We capture the full-body motion of 18 participants throwing virtual projectiles and extract motion features, such as position, velocity, rotation and rotational velocity for arm joints. Frame-level binary classifiers that estimate the point of release are trained and evaluated using a metric that prioritizes detection timing to obtain a ranking of joints and motion features. We find that wrist joint and rotation motion feature are most accurate, which can can help when placing simple motion tracking sensors for real-time throw detection

Die Nutzung von Oracle Integration Cloud als iPaaS - Lösung für eine hybride Integration

Cloud Computing hat sich hinsichtlich der Digitalisierung zu einer Basistechnologie entwickelt. Das Marktvolumen der Cloud-Technologie ist in den vergangenen Jahren kontinuierlich gestiegen. Hybride IT-Umgebungen mit Cloud- und On-Premise Anwendungen werden von den Unternehmen zunehmend bevorzugt. Eines der größten Hürden von hybriden Architekturen ist derzeit die Integration von heterogenen Umgebungen, die immer mehr und mehr an Bedeutung gewinnt. Zudem wird mit dem vermehrten Einsatz von Cloud-Services die IT-Infrastruktur der Unternehmen immer komplexer. Mithilfe von hybriden Integrationsplattformen kann diese Herausforderung erfolgreich bewältigt werden. Die vorliegende Ausarbeitung gibt den Unternehmen einen Leitfaden, welche die hybride Integration mithilfe von cloudbasierten Integrationsplattformen meistern können

Operational Support Estimator Networks

In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in a sparse signal. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support estimators rely on computationally expensive iterative signal recovery techniques to achieve such non-linearity. Contrary to the convolution layers, the proposed OSEN approach consists of operational layers that can learn such complex non-linearities without the need for deep networks. In this way, the performance of the non-iterative support estimation is greatly improved. Moreover, the operational layers comprise so-called generative \textit{super neurons} with non-local kernels. The kernel location for each neuron/feature map is optimized jointly for the SE task during the training. We evaluate the OSENs in three different applications: i. support estimation from Compressive Sensing (CS) measurements, ii. representation-based classification, and iii. learning-aided CS reconstruction where the output of OSENs is used as prior knowledge to the CS algorithm for an enhanced reconstruction. Experimental results show that the proposed approach achieves computational efficiency and outperforms competing methods, especially at low measurement rates by a significant margin. The software implementation is publicly shared at https://github.com/meteahishali/OSEN

Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing

Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing with SE problem are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e.g., Compressively Sensed Measurements). This study proposes a novel approach for learning such a mapping from a training set. To accomplish this objective, the Convolutional Support Estimator Networks (CSENs), each with a compact configuration, are designed. The proposed CSEN can be a crucial tool for the following scenarios: (i) Real-time and low-cost support estimation can be applied in any mobile and low-power edge device for anomaly localization, simultaneous face recognition, etc. (ii) CSEN's output can directly be used as "prior information" which improves the performance of sparse signal recovery algorithms. The results over the benchmark datasets show that state-of-the-art performance levels can be achieved by the proposed approach with a significantly reduced computational complexity

Parameter estimation validity and relationship robustness: A comparison of telephone and internet survey techniques

With the expansion of telecommunication and online technologies for the purpose of survey administration, the issue of measurement validity has come to the fore. The proliferation of automated audio services and computer-based survey techniques has been matched by a corresponding denigration of the quality of traditional phone survey data, most notably as an outcome of falling response rates. This trend, combined with the introduction of screening technologies and answering machines, represents a barrier to the proper execution of survey research. Whereas the question was once, “can technology-assisted surveys achieve the same level of validity as traditional phone surveys?”, the question now becomes, “what are the relative advantages and disadvantages of technology-assisted and phone surveys?” Each has its own challenges and opportunities, and this paper begins to explore these. The present study provides further insight into the validity of telephone and Internet survey data, and explores whether or not the robustness of relationships between variables varies by survey mode. Study data were provided by two surveys, the first of which was conducted in a metropolitan area of the Midwestern US, with interviews of 505 adults using a computer-aided telephone-interviewing (CATI) system. The second was a national survey of 2172 respondents conducted over the Internet by a commercial research firm that sends requests to a diverse set of potential respondents, who logged onto the survey site to participate. Results suggest that weighting in an attempt to achieve parametric matching does seem to increase robustness of relationships and, in this age of poor response rates, this seems to demand an increased use of parametric weightings. Implications of study findings for telematic survey practitioners are discussed