FPGA-based Implementation of IIR Filter for Real-Time Noise Reduction in Signal

Filtering of unwanted frequencies represents the main aspect of digital signal processing (DSP) in any modern communication system. The main role of the filter is to perform attenuation of certain frequencies and pass only frequencies of interest. In a DSP system, sampled or discrete-time signals are processed by digital filters using different mathematical operations. Digital filters are commonly categorized as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This research focuses on the full VHDL implementation of digital second-order lowpass IIR filter for reducing the noisy frequencies on the FPGA board. The initial step is to determine, from continuous time domain function, the transfer function in the complex {s} domain, then map transfer function in complex {z} domain and finally calculate the difference equation in discrete-time domain of the system with adequate coefficients. Prior to the FPGA implementation, the IIR filter is tested in MATLAB using a signal with mixed frequencies and signal with randomly generated noise. The digital implementation is completed by using fixed-point binary vectors and clocked processes.

"Quantitative estimation of cooling load capabilities of residential buildings using machine learning"

Based on previous research on energy efficiency of the buildings, particularly their cooling load capabilities we will develop a collection of machine learning methods for detecting buildings with best cooling load capabilities. This collection will study the influence of 8 input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) on one output parameter, that is cooling load of buildings. The results of this study support the practicability of using machine-learning software to estimate building parameters as a convenient and accurate approach, as long as the methods chosen are well suited for the type of data in question.

"Understanding Forms and Models of Cloud Computing Technologies Adopted in the Selected Institutions in Southwestern Nigeria "

The study examined the forms and models of cloud computing technology adopted in the selected institutions from four states in Southwestern Nigeria. The three purposively selected institutions were Federal, State and Private owned making twelve institutions. However, the administered questionnaire was filled in by the ten (10) IT personnel, ten (10) lecturers and five (5) students from each of the selected institutions making 300 respondents. The questionnaire elicited information on the forms and models of cloud computing technology adopted and the extent of use of the adopted cloud computing technologies in the selected institutions. Secondary data were obtained from relevant literature. Data collected were analysed with descriptive and inferential statistics. The study concludes that the forms of cloud computing technology adopted by the selected institutions in Southwestern Nigeria are infrastructure-as-a-service (IaaS), software-as-a-service (SaaS) and platform-as-a-service (PaaS) while software-as-a-service (SaaS) is often used by the institutions. Also, the models of adopted cloud computing technology are private, public, hybrid and community cloud computing by the selected institutions in Southwestern Nigeria. The adopted forms and models of cloud computing technology are used for different business functions such as payroll, procurement, human resources, accounting and finance, CRM, application development, and project management.

"Leveraging Raspberry Pi as a server for the integration of the NETCONF protocol within IoT systems based on YANG"

Herein the idea of leveraging Raspberry Pi as a server for the integration of an incipient network management protocol, the Network Configuration Protocol (NETCONF), within IoT systems based on YANG is presented. The practical realization of this idea requires the implementation of the NETCONF protocol together with REpresentational State Transfer web services (RESTful). Such an interesting and innovative practical realization like this opens new additional possibilities in domotics systems and these possibilities will be discussed in this paper.

Sentiment Analysis on Twitter Data using Big Data

With the increasing number of users and data on the Internet, especially social media sites, sentiment analysis topic became one of the important and essential fields for most. Collection of people's feelings and sentiment and classifying the data attracted most businesses and companies. Recently, twitter sentiment analysis has attracted much attention, because of Twitter's growth and popularity. The solution for handling enormous amounts of data from social media is a new term called Big data. Big data is not just for having a large amount of data, but also the importance of processing and the usage of the data.

Overview of Human Lineage Genetic Marker Studies in Bosnia and Herzegovina: Y chromosome story

– Modern Bosnia and Herzegovina is a state consisting of multiple ethnicities and regions located in the Western Balkan, with a very complex history. The earliest historical findings show that its area was inhabited since the Paleolithic. From that time, this part of Europe, especially the region of the Modern Bosnia and Herzegovina, could be recognized as the crossroad for the different human migration and the meeting point for different cultures, religions and gene pools. Mitochondrial DNA is being used for maternal lineage testing, while the Y chromosome is being used for paternal lineage testing. Therefore, these markers are being referred to as lineage markers. Lineage markers are often used for parental lineage monitoring in population genetics, human genetics, as well as in forensic genetics. The main intention of this paper is to construct a short overview of the Y chromosome studies performed in Bosnia and Herzegovina within the last two decades.

Effect of metals on antibiotic sensitivity, growth, and biofilm-forming capacity of B. subtilis subsp. spizizenii

B. subtilis is normally considered a soil organism, it can be also found in the animal and human gastrointestinal tract. Bacillus subtilis subsp. spizizenii is a type of Bacillus subtilis complex. It shares up to 99% of homology with B. subtilis CU1, which can be represented as a probiotic strain. Metal compounds found in soil or used in agriculture can easily enter the food chain and end up in our gut. Gram-positive bacteria (e.g. Bacillus spp.) have good adsorptive capacity for metals due to high peptidoglycan and teichoic acid content in cell walls. There is some evidence that certain metals inside the intestine play an important role in influencing growth and functionality of specific probiotic strains. Some of them have inhibitory, while others have an activating effect on bacteria. This study revealed that metal compounds increased antibiotic susceptibility of B. subtilis subsp. spizizenii. Higher concentrations of metal solutions inhibited growth of tested bacteria. Culture did not show affinity to form biofilms before or after addition of metal solutions.

INFLUENCE OF THE DISTRIBUTED GENERATION ON THE POWER QUALITY IN DISTRIBUTION NETWORK

The aim of this paper is to present and discuss the influence of distributed generation on power quality. Nowadays, interest in power quality has increased since it has become a very important issue in power system delivery. One of the major problems of ensuring a certain level of power quality are harmonics. The aim of this project is to investigate an impact of photovoltaic (PV) on harmonic voltage distortion (HD) in real MV distribution network. Different scenarios will be implemented where solar power plant is going to be modelled with high variability of load and generation to see their effects on the systems power quality (PQ). Those scenarios are when PV is disconnected from the grid and PVs are connected with 2 different powers. Results presented below showed that PV improves power quality of the system, because their inverters are source of harmonics and they increase HD. However, that impact is not very significant and harmonic limits are not violated. A load flow analysis is done for the model of test system 110/35/10kV in which a distributed generator is added, that is on-grid or off-grid. The network modelling and simulation is done in DIgSILENT PowerFactory software.

Contemporary housing trends in Sarajevo

– Within the last 20 years, there has been witnessed a significant increase of the urban population of Sarajevo, as a result of economic and social migrations. Consequently, this has caused an increasing demand for new housing which is mainly profit-oriented without any beneficial social, environmental or cultural implication. Primary objective of this research is to analyze the current situation and to assess the quality of the buildings not only as a housing solution, but as a complex that unites the community who inhabits it. This research will be conducted in a qualitative manner in analysis and statistical approach over the data related to the urbanization, building standards and positive effects of the building. Newly built parts of settlements Otoka and Stup will be used as case studies, since these parts of the city are most influenced by the mass production of the new housing solutions. This paper stresses out the correlation between high demand for the new housing and decreased quality of the housing without respecting minimum spatial and environmental standards, without basic amenities, social infrastructure and recreational and cultural activities. There is a need for improvements in contemporary housing design that will reflect with positive impacts on social, environmental, economic and cultural aspects of urban living.

Using Exploratory Data Analysis and Big Data Analytics for Detecting Anomalies in Cloud Computing

While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection, which is a necessary part of the management of modern applications. As the record of user behavior, weblogs surely become the research item related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where anomalous behavior needs to be detected in understanding phases prior to modeling a system for such use. Big Data Analytics often ignores anomalous point due to high volume of data. To address this problem, we propose a complemented methodology for Big Data Analytics – the Exploratory Data Analysis, which assists in gaining insight into data relationships without the classical hypothesis modeling. In that way, we can gain better understanding of the patterns and spot anomalies. Results show that Exploratory Data Analysis facilitates anomaly detection and the CRISP-DM Business Understanding phase, making it one of the key steps in the Data Understanding phase.