Search results for: microorganisms detection

Authors: C. Makwana, S. R. Dave

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Arsenic is toxic to almost all living cells. Its contamination in natural sources affects the growth of microorganisms. The presence of arsenic is associated with various human disorders also. The attempt of this sort of study provides information regarding the performance of our isolated microorganisms in the presence of Arsenic, which have ample scope for bioremediation. Six isolates were selected from the polluted sample of industrial zone Vatva, Ahmedabad, Gujarat, India, out of which two were Thermophilic organisms. The thermophilic exopolysaccharide (EPS) producing Bacillus was used for microbial enhance oil recovery (MEOR) and in the bio beneficiation. Inorganic arsenic primarily exists in the form of arsenate or arsenite. This arsenic resistance isolate was capable of transforming As +3 to As+5. This isolate would be useful for arsenic remediation standpoint from aquatic systems. The study revealed that the thermophilic microorganism was growing at 55 degree centigrade showed considerable remediation property. The results on the growth and enzyme catalysis would be discussed in response to Arsenic remediation.

Keywords: aquatic systems, thermophilic, exopolysacchride, arsenic

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Authors: Mozhgan Farzami Sepehr, Farhad Nourozi

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In a greenhouse study, decontamination capacity of the species Polypogon monspoliensis, for detoxification of petroleum-polluted soils caused by sewage and waste materials of Tehran Petroleum Refinery. For this purpose, the amount of total oil and grease before and 45 days after transplanting one-month-old seedlings in the soils of five different treatments in which pollution-free agricultural soil and contaminated soil were mixed together with the weight ratio of respectively 1 to 9 (% 10), 2 to 8 (%20), 3 to 7 (%30) , 4 to 6 (%40), and 5 to 5 (%50) were evaluated and compared with the amounts obtained from control treatment without vegetation, but with the same concentration of pollution. Findings demonstrated that the maximum reduction in the petroleum rate ,as much as 84.85 percent, is related to the treatment 10% containing the plant. Increasing the shoot height in treatments 10% and 20% as well as the root dry and fresh weight in treatments 10% , 20% , and 30% shows that probably activity of more rhizosphere microorganisms of the plant in these treatments has led to the improvement in growth of plant organs comparing to the treatments without pollution.

Keywords: phytoremediation, total oil and grease, rhizosphere, microorganisms, petroleum-contaminated soil

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Authors: Isao Tomita

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The detection of environmental gases, 12CO_2, 13CO_2, and CH_4, using near-infrared semiconductor lasers with a short laser path length is studied by means of wavelength-modulation spectroscopy. The developed system is compact and has high sensitivity enough to detect the absorption peaks of isotopic 13CO_2 of a 3-% CO_2 gas at 2 um with a path length of 2.4 m, where its peak size is two orders of magnitude smaller than that of the ordinary 12CO_2 peaks. In addition, the detection of 12CO_2 peaks of a 385-ppm (0.0385-%) CO_2 gas in the air is made at 2 um with a path length of 1.4 m. Furthermore, in pursuing the detection of an ancient environmental CH_4 gas confined to a bubble in ice at the polar regions, measurements of the absorption spectrum for a trace gas of CH_4 in a small area are attempted. For a 100-% CH_4 gas trapped in a 1 mm^3 glass container, the absorption peaks of CH_4 are obtained at 1.65 um with a path length of 3 mm, and also the gas pressure is extrapolated from the measured data.

Keywords: environmental gases, Near-Infrared Laser Detection, Wavelength-Modulation Spectroscopy, gas pressure

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Authors: Tim Farrelly

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In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time.

Keywords: deep learning, object detection, machine vision applications, sport, network design

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

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Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Alireza Pour Ebrahimi, Vahid Abasi

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Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach.

Keywords: network, intrusion detection system, peer to peer, internal and external network

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Authors: M. A. El-Khateeb

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The growth of the olive oil production in Saudi Arabia peculiarly in Al Jouf region in recent years has been accompanied by an increase in the discharge of associated processing waste. Olive mill waste is produced throughout the extraction of oil from the olive fruit using the traditional mill and press process. Deterioration of the environment due to olive mill disposal wastes is a serious problem. When olive mill waste disposed into the soil, it affects soil quality, soil micro flora, and also toxic to plants. The aim of this work is to isolate microorganism (bacterial or fungal strains) from OMW capable of degrading phenols. Olive mill wastewater, olive mill waste and soil (beside oil production mill) contaminated with olive waste were used for isolation of phenol tolerant microorganisms. Four strains (two fungal and two bacterial) were isolated from olive mill waste. The isolated strains were Candida tropicalis and Phanerochaete chrysosporium (fungal strains) and Bacillus sp. and Rhodococcus sp. (bacterial strains). These strains were able to degrade phenols and could be used for bioremediation of olive mill waste.

Keywords: bioremediation, bacteria, fungi, Sakaka

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Authors: V. Verma, Syed Riyaz-ul-Hassan

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Staphylococcus aureus is one of the most commonly found pathogenic bacteria and is hard to eliminate from the human environment. It is responsible for many nosocomial infections, besides being the main causative agent of food intoxication by virtue of its variety of enterotoxins. Routine detection of S. aureus in food is usually carried out by traditional methods based on morphological and biochemical characterization. These methods are time-consuming and tedious. In addition, misclassifications with automated susceptibility testing systems or commercially available latex agglutination kits have been reported by several workers. Consequently, there is a need for methods to specifically discriminate S. aureus from other staphylococci as quickly as possible. Data on protocols developed using molecular means like PCR technology will be presented for rapid and specific detection of this pathogen in food, clinical and environmental samples, especially milk.

Keywords: food Pathogens, PCR technology, rapid and specific detection, staphylococcus aureus

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Authors: Li Li, Li-Ru Xia, He-Ye Wang, Xiao-Dong Bi

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In this paper, we presented a highly sensitive immune-affinity monolithic array for detection of α-fetoprotein (AFP) and carcinoembryonic antigen (CEA). Firstly, the epoxy functionalized monolith arrays were fabricated using UV initiated copolymerization method. Scanning electron microscopy (SEM) image showed that the poly(BABEA-co-GMA) monolith exhibited a well-controlled skeletal and well-distributed porous structure. Then, AFP and CEA immune-affinity monolithic arrays were prepared by immobilization of AFP and CEA antibodies on epoxy functionalized monolith arrays. With a non-competitive immune response format, the presented AFP and CEA immune-affinity arrays were demonstrated as an inexpensive, flexible, homogeneous and stable array for detection of AFP and CEA.

Keywords: chemiluminescent detection, immune-affinity, monolithic copolymer array, UV-initiated copolymerization

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Authors: Yin Zhang, Kai Qiao, Xiyang Zhi, Jinnan Gong, Jianming Hu

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In order to effectively detect aerial targets over long distances, an optimal matching design method of space-based optical payload is proposed. Firstly, main factors affecting optical detectability of small targets under complex environment are analyzed based on the full link of a detection system, including band center, band width and spatial resolution. Then a performance characterization model representing the relationship between image signal-to-noise ratio (SCR) and the above influencing factors is established to describe a detection system. Finally, an optimal matching design example is demonstrated for a typical aerial target by simulating and analyzing its SCR under different scene clutter coupling with multi-scale characteristics, and the optimized detection band and spatial resolution are presented. The method can provide theoretical basis and scientific guidance for space-based detection system design, payload specification demonstration and information processing algorithm optimization.

Keywords: space-based detection, aerial targets, optical system design, detectability characterization

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Weihao Wang, Zhulin Zong

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Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate.

Keywords: two-dimensional, ordered statistical, constant false alarm, detection, weak target signals

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Authors: Tomáš Bublík, Miroslav Virius

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This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and sub graph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm.

Keywords: AST, Java, tree matching, scripthon source code recognition

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Authors: M. Okeke, A. Blyth

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Industrial Control Systems (ICS) such as Supervisory Control And Data Acquisition (SCADA) can be seen in many different critical infrastructures, from nuclear management to utility, medical equipment, power, waste and engine management on ships and planes. The role SCADA plays in critical infrastructure has resulted in a call to secure them. Many lives depend on it for daily activities and the attack vectors are becoming more sophisticated. Hence, the security of ICS is vital as malfunction of it might result in huge risk. This paper describes how the application of Prey Predator (PP) approach in flocks of birds could enhance the detection of malicious activities on ICS. The PP approach explains how these animals in groups or flocks detect predators by following some simple rules. They are not necessarily very intelligent animals but their approach in solving complex issues such as detection through corporation, coordination and communication worth emulating. This paper will emulate flocking behavior seen in birds in detecting predators. The PP approach will adopt six nearest bird approach in detecting any predator. Their local and global bests are based on the individual detection as well as group detection. The PP algorithm was designed following MapReduce methodology that follows a Split Detection Convergence (SDC) approach.

Keywords: artificial life, industrial control system (ICS), IDS, prey predator (PP), SCADA, SDC

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Muhammad Hassan Khalil, Xu Jiadong

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Detection of malignant tumor inside the breast of women is a challenging field for the researchers. MWI (Microwave imaging) for breast cancer diagnosis has been of interest for last two decades, newly it suggested for finding cancerous tissues of women breast. A simple and basic idea of the mathematical modeling is used throughout this paper for imaging of malignant tumor. In this paper, the authors explained inverse scattering method in the microwave imaging and also present some simulation results.

Keywords: breast cancer detection, microwave imaging, tomography, tumor

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Authors: Nassima Behidj-Benyounes, Thoraya Dahmene, Nadjiba Chebout

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This work examines the study of the antimicrobrial effect of alkaloids extracted from the seeds of Peganum harmala L (Zygophyllaceae). This natural substance is extracted by using different solvents (aqueous, ethanolic, and hexane). The evaluation of the antimicrobial activity has only dealt with alkaloids. The antimicrobial effect of alkaloids is evaluated on several microorganisms. It has been tested on eight bacterial strains. The extract has been studied by using two yeasts. Finally, three molds have been studied. It should be noted that these agents are characterized by a high frequency of contamination and pathogenicity. Through this study, we note that Staphylococcus aureus, Saccharomyces cerievisae and E. coli are very sensitive in respect of the ethanol extract. Pseudomonas aerogenosa and Penicillium sp. are resistant to this extract. The other microorganisms are moderately sensitive. The study of the antimicrobial activity of different extracts of the Harmel has shown an optimal activity with the ethanol extract.

Keywords: Peganum harmala L., seeds, alkaloids, bacteria, fungi, yeast, antimicrobial activity

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Authors: Mohammad Esmaeilpour

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One of the important open problems in the field of medical image processing is detection and quantification of small changes. In this poster, we try to investigate that, how the algebraic decomposition techniques can be used for semiautomatically detecting and quantifying subtle changes in Magnetic Resonance (MR) neuroimaging volumes. We mostly focus on the low-rank values of the matrices achieved from decomposing MR image pairs during a period of time. Besides, a skillful neuroradiologist will help the algorithm to distinguish between noises and small changes.

Keywords: magnetic resonance neuroimaging, subtle change detection and quantification, algebraic decomposition, basis functions

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Authors: Prikhil Agrawal, N. Priyanka

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With the increase of millions of users on Internet day by day, it is very essential to maintain highly reliable and secured data communication between various corporations. Although there are various traditional security imparting techniques such as antivirus software, password protection, data encryption, biometrics and firewall etc. But still network security has become the main issue in various leading companies. So IDSs have become an essential component in terms of security, as it can detect various network attacks and respond quickly to such occurrences. IDSs are used to detect unauthorized access to a computer system. This paper describes various intrusion detection techniques using GA approach. The intrusion detection problem has become a challenging task due to the conception of miscellaneous computer networks under various vulnerabilities. Thus the damage caused to various organizations by malicious intrusions can be mitigated and even be deterred by using this powerful tool.

Keywords: genetic algorithm (GA), intrusion detection system (IDS), dataset, network security

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Authors: Ahlem Abid, Farah Jemili

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Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

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Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

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In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

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Authors: Gabriela Kalcikova, Igor Bosevski, Ula Rozman, Andreja Zgajnar Gotvajn

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Antibiotics are extensively used in human medicine and also in animal husbandry to prevent or control infections. Recently, a lot of attention has been put on veterinary antibiotics, because their global consumption is increasing and it is expected to be 106.600 tons in 2030. Most of veterinary antibiotics are introduced into the environment via animal manure, which is used as fertilizer. One of such veterinary antibiotics is tiamulin. It is used the form of fumarate for treatment of pig and poultry. It is used against prophylaxis of dysentery, pneumonia and mycroplasmal infections, but its environmental impact is practically unknown. Tiamulin has been found very persistent in animal manure and thus it is expected that can be, during rainfalls, transported into the aquatic environment and affect various organisms. For assessment of its environmental impact, it is necessary to evaluate its biodegradability and toxicity to various organisms from different levels of a food chain. Therefore, the aim of our study was to evaluate ready biodegradability and toxicity of tiamulin fumarate to various organisms. Bioassay used included luminescent bacterium Vibrio fischeri heterotrophic and nitrifying microorganisms of activated sludge, water flea Daphnia magna and duckweed Lemna minor. For each species, EC₅₀ values were calculated. Biodegradability test was used for determination of ready biodegradability and it provides information about biodegradability of tiamulin under the most common environmental conditions. Results of our study showed that tiamulin differently affects selected organisms. The most sensitive organisms were water fleas with 48hEC₅₀ = 14.2 ± 4.8 mg/L and duckweed with 168hEC₅₀ = 22.6 ± 0.8 mg/L. Higher concentrations of tiamulin (from 10 mg/L) significantly affected photosynthetic pigments content in duckweed and concentrations above 80 mg/L cause visible chlorosis. It is in agreement with previous studies showing significant effect of tiamulin on green algae and cyanobacteria. Tiamuline has a low effect on microorganisms. The lower toxicity was observed for heterotrophic microorganisms (30minEC₅₀ = 1656 ± 296 mg/L), than Vibrio fisheri (30minEC₅₀ = 492 ± 21) and the most sensitive organisms were nitrifying microorganisms (30minEC₅₀ = 183 ± 127 mg/L). The reason is most probably the mode of action of tiamulin being effective to gram-positive bacteria while gram-negative (e.g., Vibrio fisheri) are more tolerant to tiamulin. Biodegradation of tiamulin was very slow with a long lag-phase being 20 days. The maximal degradation reached 40 ± 2 % in 43 days of the test and tiamulin as other antibiotics (e.g. ciprofloxacin) are not easily biodegradable. Tiamulin is widely used antibiotic in veterinary medicine and thus present in the environment. According to our results, tiamulin can have negative effect on water fleas and duckweeds, but the concentrations are several magnitudes higher than that found in any environmental compartment. Tiamulin is low toxic to tested microorganisms, but it is very low biodegradable and thus possibly persistent in the environment.

Keywords: antibiotics, biodegradability, tiamulin, toxicity

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Authors: Amer Dawoud, Jesy Motchaalangaram, Arati Biswakarma, Wujan Mio, Karl Wallace

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The development of an embedded miniaturized drone-based system for remote detection of Chemical Warfare Agents (CWA) is proposed. The paper focuses on the software/hardware system design of the electrochemical Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) signal processing for future deployment on drones. The paper summarizes the progress made towards hardware and electrochemical signal processing for signature detection of CWA. Also, the miniature potentiostat signal is validated by comparing it with the high-end lab potentiostat signal.

Keywords: drone-based, remote detection chemical warfare agents, miniaturized, potentiostat

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Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

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Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Tomáš Dyk, Stanislav Procházka, Martin Drahanský

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The article proposes a new algorithm for detecting damaged areas of the tank barrel based on the image of the inner surface of the tank barrel. Damage position is calculated using image processing techniques such as edge detection, discrete wavelet transformation and image segmentation for accurate contour detection. The algorithm can detect surface damage in smoothbore and even in rifled tank barrels. The algorithm also calculates the volume of the detected damage from the depth map generated, for example, from the distance measurement unit. The proposed method was tested on data obtained by a tank barrel scanning device, which generates both surface image data and depth map. The article also discusses tank barrel scanning devices and how damaged surface impacts material resistance.

Keywords: barrel, barrel diagnostic, image processing, surface damage detection, tank

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Authors: Eva Slaninova, Diana Cernayova, Zuzana Sedrlova, Katerina Mrazova, Petr Sedlacek, Jana Nebesarova, Stanislav Obruca

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Cyanobacteria are gram-negative prokaryotes belonging to a group of photosynthetic bacteria. In comparison with heterotrophic microorganisms, cyanobacteria utilize atmospheric nitrogen and carbon dioxide without any additional substrates. This ability of these microorganisms could be employed in biotechnology for the production of bioplastics, concretely polyhydroxyalkanoates (PHAs) which are primarily accumulated as a storage material in cells in the form of intracellular granules. In this study, there two cyanobacterial cultures from genera Synechocystis were used, namely Synechocystic sp. PCC 6803 and Synechocystis salina CCALA 192. There were optimized and used several various approaches, including microscopic techniques such as cryo-scanning electron microscopy (Cryo-SEM) and transmission electron microscopy (TEM), and fluorescence lifetime imaging microscopy using Nile red as a fluorescent probe (FLIM). Due to these instrumental techniques, the morphology of intracellular space and surface of cells were characterized. The next group of methods which were employed was spectroscopic techniques such as UV-Vis spectroscopy measured in two modes (turbidimetry and integration sphere) and Fourier transform infrared spectroscopy (FTIR). All these diverse techniques were used for the detection and characterization of pigments (chlorophylls, carotenoids, phycocyanin, etc.) and PHAs, in our case poly (3-hydroxybutyrate) (P3HB). To verify results, gas chromatography (GC) was employed concretely for the determination of the amount of P3HB in biomass. Cyanobacteria were also characterized as polyhydroxybutyrate producers by flow cytometer, which could count cells and at the same time distinguish cells including P3HB and without due to fluorescent probe called BODIPY and live/dead fluorescent probe SYTO Blue. Based on results, P3HB content in cyanobacteria cells was determined, as also the overall fitness of the cells. Acknowledgment: Funding: This study was partly funded by the projectGA19-29651L of the Czech Science Foundation (GACR) and partly funded by the Austrian Science Fund (FWF), project I 4082-B25.

Keywords: cyanobacteria, fluorescent probe, microscopic techniques, poly(3hydroxybutyrate), spectroscopy, chromatography

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Authors: Jianrong Wu, Xuan Fu, Akihiro Higashi, Zhiming Tan

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CMOS image sensors with a rolling shutter are used broadly in the digital cameras embedded in mobile devices. The rolling shutter suffers the flicker artifacts from the fluorescent lamp, and it could be observed easily. In this paper, the characteristics of illumination flicker in motion case were analyzed, and two efficient detection methods based on matching fragment selection were proposed. According to the experimental results, our methods could achieve as high as 100% accuracy in static scene, and at least 97% in motion scene.

Keywords: illumination flicker, embedded camera, rolling shutter, detection

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Authors: R. Rezaeipour, A. R. Mehrabi

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Recent increase of the DGs and microgrids in distribution systems, disturbs the tradition structure of the system. Coordination between protection devices in such a system becomes the concern of the network operators. This paper presents a new method for fault location detection in the active distribution networks, independent of the fault type or its resistance. The method uses synchronized voltage and current measurements at the interconnection of DG units and is able to adapt to changes in the topology of the system. The method has been tested on a 38-bus distribution system, with very encouraging results.

Keywords: fault location detection, active distribution system, micro grids, network operators

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