Davchev, Dancho
Preferred name
Davchev, Dancho
Official Name
Davchev, Dancho
Main Affiliation
Email
danco.davcev@finki.ukim.mk
45 results
Now showing 1 - 10 of 45
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation issues of different cryptography algorithms in wireless sensor networks(NATO-ARW, 2006-09-04); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Project based learning of embedded systems(2016-06-23); ; ; Traditional teaching, usually based on lectures and tutorials fosters the idea of instruction-driven learning model where students are passive listeners. Besides this approach, Project Based Learning (PBL) as a different learning paradigm is standing behind constructivism learning theory, where learning from real-world situations is put on the first place. The purpose of this paper is to present our approach in learning embedded systems at our University. It is based on combination of traditional (face-to-face) learning and PBL. Our PBL represents an interdisciplinary project based on wireless sensor monitoring of real-world environment (greenhouse). The students use UML that was shown as an excellent tool for developing such a projects. From the student perspective, we found that this high level of interdisciplinary is very valuable from the point of view of facing the students with reallife problems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Architecture for wireless sensor and actor networks control and data acquisition(IEEE, 2011-06-27); ; ; Wireless Sensor and Actor Networks (WSANs) have received increased attention from the research community. This is mainly because as an extension to Wireless Sensor Networks(WSN), they have the ability to actively participate in the environment trough the actors. This however introduces new challenges as to how to transfer commands between nodes, actors and central station who may be from different manufacturers and use different communication protocols. Another important aspect is the ability of the WSAN to present the data to the interested party or to receive the command from the operator, and do this with in the simplest and most user friendly way as possible. In this paper we propose architecture for interconnection between different layers of WSANs and the central stations that would allow building a simple interface that would ease the operation with WSANs in view of Control and Data Acquisition. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Variable step size LMS algorithm for data prediction in wireless sensor networks(International Frequency Sensor Association, 46 Thorny Vineway Toronto ON M 2 J 4 J 2 Canada, 2012-02) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Information System for Trust Food Supply Management(2017); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NOISE POLLUTION MODELLING AND VISUALISATION–THE CASE STUDY FOR THE CITY OF SKOPJE(Scientific Technical Union of Mechanical Engineering" Industry 4.0", 2016); ; Noise pollution modelling can be used to provide information about growing noise pollution from the urban traffic. Various methods have been developed that aim at minimizing the noise pollution and improving the environment. Geographic Information System (GIS) can be adapted to gather, analyse and present noise information. The results in this paper demonstrated that most of regions surrounding the main streets are suffering from the noise pollution. As a main contribution of this paper, the first this-kind of study for the city of Skopje, practicing GIS capabilities for presenting noise information, we have produced a general picture of the traffic-induced noise pollution on annual level. The assessment showed that the used method in visualization can provide reliable information about noise pollution in any city or urban region. In this paper, we were focused, as a case study, for the city of Skopje. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Tracking of unusual events in wireless sensor networks based on artificial neural-networks algorithms(IEEE, 2005-04-04); Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and will meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage and data robustness. As a result of the dimensionality reduction obtained simply from the outputs of the neural-networks clustering algorithms, lower communication costs and energy savings can also be obtained. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several motes, equipped with several sensors each. Results from simulations of purposefully faulty sensors show the data robustness of these architectures. The proposed neural-networks classifiers have distributed short and long-term memory of the sensory inputs and can function as security alert when unusual sensor inputs are detected. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent wireless sensor networks using fuzzyart neural-networks(IEEE, 2007-07-01); An adaptation of one popular model of neuralnetworks algorithm (ART model) in the field of wireless sensor networks is demonstrated in this paper. The important advantages of the ART class algorithms such as simple parallel distributed computation, distributed storage, data robustness and autoclassification of sensor readings are confirmed within the proposed architecture consisting of one clusterhead which collects only classified input data from the other units. This architecture provides a high dimensionality reduction and additional communication savings, since only identification numbers of the classified input data are passed to the clusterhead instead of the whole input samples. We have adapted and implemented the FuzzyART neural-network algorithm and used it for initial clustering of the sensor data as a sort of pattern recognition. This adaptation was made specifically for MicaZ sensor motes by solving mainly problems concerning the small memory capacity ofthe motes. At the final clusterhead - server, the data are stored in a database and the results of the data processing are continuously presented in a classification graph. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated Structural Classification of Proteins by Using Decision Trees and Structural Protein Features(Springer, Berlin, Heidelberg, 2009-09-28); ; ; ; The protein function is tightly related to classification of proteins in hierarchical levels where proteins share same or similar functions. One of the most relevant protein classification schemes is the structural classification of proteins (SCOP). The SCOP scheme has one negative drawback; due to its manual classification methods, the dynamic of classification of new proteins is much slower than the dynamic of discovering novel protein structures in the protein data bank (PDB). In this work, we propose two approaches for automated protein classification. We extract protein descriptors from the structural coordinates stored in the PDB files. Then we apply C4.5 algorithm to select the most appropriate descriptor features for protein classification based on the SCOP hierarchy. We propose novel classification approach by introducing a bottom-up classification flow, and a multi-level classification approach. The results show that these approaches are much faster than other similar algorithms with comparable accuracy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Probabilistic predictions of ensemble of classifiers combined with dynamically weighted majority vote(IASTED, Acta Press, 2011-02); ; ; This paper presents a new method for dynamic calculation of weights that can be used in the process of aggregation of classifications by weighted majority vote. The proposed method can be used for all binary classification problems for classifiers that produce probabilistic classifications. Most aggregation functions produce an output which only represents the aggregated classification of an ensemble of classifiers and sometimes this isn't enough. This paper also proposes a method for estimation of the probability of an aggregated classification. The estimated probability of the aggregated classification is essential if the performance of the ensemble of classifiers needs to be expressed in terms of Area Under the Receiver Operating Curve or some other performance measures that classifications’ probability. The experimental results demonstrate the performance improvements obtained by applying the proposed methods to an ensemble of classifiers compared to individual classifiers.