The main aim of this paper is to provide electric power generation using a renewable and portable method in rural and geographically problematic areas where energy transmission is not possible. Electric power generation from wind with low-cost, efficient and portable small-scale wind turbine during natural disasters (earthquake, fire, flood, etc.) leading to catastrophic consequences (long electrical black-outs) and for individual low power applications is targeted. The control of gearless Permanent Magnet Synchronous Generator (PMSG) along with load side inverter control in this project are performed using a full-rating, bi-directional, two-level, back-to-back voltage source rectifier/inverter with total of 8 switches for the variable speed wind turbine. Therefore, one of the major advantages and novelty of using MRAS rotor flux linkage and stator resistance estimator in this system is to correct changes in the flux and stator resistance values in the control system. Due to its simplicity and effectiveness, the load side control is achieved by using four switch grid side inverter. To observe the total system performance, a three-phase passive load is used at the inverter output. The low-pass LCL filter is designed and used in the load side to reduce current and voltage harmonics and increase stability and efficiency of the power network. The results of the overall system are validated with the real-time DSP system. It is proved with this paper that the inclusion of a simple, effective and economical position sensorless control in the generator side show that renewable wind energy system can be a viable distributed green energy solution for rural areas.

This paper presents the simulation performance of control of a grid-connected photovoltaic maximum power point tracking system with highly efficient and reliable inverter concept transformerless inverter. Perturb and Observe technique based on variation of power and voltage is used to extract maximum power from photovoltaic power system. A dc-dc boost converter is applied with the technique by changing duty ratio of the PWM signal of the converter switch. A single-phase highly efficient and reliable inverter concept transformerless inverter provides a power flow into grid. The control of the inverter is based on the grid voltage and current and also dc-dc boost converter output voltage values. The system performance is evaluated in terms of PV module and dc-dc boost converter output voltages and currents, grid current and voltage, leakage current, active and reactive powers and power efficiency characteristics. The simulation results show 97.96% maximum efficiency, 96.84% European efficiency and 97.60% Californian efficiency with 20 kHz switching frequency for the HERIC inverter on the proposed system.

In this study, a multi-functional probe was designed and implemented to determine P-N electrical conductivity type of thermoelectric (TE) semiconductors according to Hot-Probe method based on the Seebeck effect. The type determination operation is performed according to the positive or negative amplitude of the Seebeck voltage (thermoemf) produced by forming the temperature difference (ΔT) between the surfaces of the TE semiconductors. Using the developed probe, it is possible to provide both necessary heating and measurement of the thermoemf voltage and temperature. As a result of experimental studies; it has been shown that the thermoemf measurements can be made in ± 0,1 mV accuracy in 0-30 mV range and the temperature measurements can be ± 1 °C in 0-75 °C range. In conclusion, with the developed multifunctional probe, electrical conductivity type of TE semiconductors can be determined as P or N practically, quickly and reliably.

Pitching a fast product to the market as well as with the same standard along with decreasing the total cost is one of the main objects of companies engaging with serial production. Though, not only production, whole process that from production to the marketing (development, manufacturing, logistic) is required to be conducted within a way that faster, effective, flexible and manageable. This study deals with the improvement of the efficiency and speed of the storage line in a storage facility in which logistics activities include storage of products, shipments of products to customers, sorting of incoming products by product and seize based operations. The storage plant is divided to fields according to operational specifications in order to investigate the process of storage as modular. The proposed improvement contains five conveyor lines which are a part of the storage system. In these part of the storage system, where the first product to be prepared comes in common with the final product, the control structure that operates depending on the counter has been improved by replacing it with the controller based on the density states of incoming / outgoing lines. According to system's previous state, a considerable increase has been observed in the the product storage capacity under the same operating conditions.

In recent years, significant improvements have been in fast digital filters and filter banks. QMF (Quadrature Mirror Filter) bank, known as an application of multi-speed filter banks are widely used sub-band coding, image compression, word coding, biomedical signal processing and communication systems. In this study; QMF bank design is realized via a windowing method with prototype FIR (Finite Impulse Response) filter design and based on cascade bounded Kaiser and Hamming windows for eliminating undesired oscillations in QMF bank design. It has been investigated how the performance changed of the filter degree in designing filter with different window functions and at the result it is shown that Kaiser-Hamming window with lower stopband attenuation could be used in QMF bank design.

In this study, it is aimed to detect and count wild animals based on image processing in wildlife. From the images obtained from a fixed camera in the protected areas, an image processing based system has been developed for detecting and counting wild animals which are added value with hunting tourism to the country's economy. Through this developed system, it is aimed to both determine and count the wild animals species with high performance. For this, firstly, using gaussian mixed models (GMM) technique, the background images were extracted from the image scenes coming from real-time photocapture videotapes. In a real-time complex image scene that is instantaneous in nature where there is a lot of mobility, developed field test, attribute test and color test criteria are used to determine the targeted wild animal. In the experimental studies, 4 species of wild animals including deer, fox, wolf and wild horses were detected with 100% accuracy. The software's processing time per video frame is 0.242 seconds. With the developed methods, it has been seen that species determination for wild animal inventory can be done with less cost camera systems and computer software without human power with 100% success rate. The difference from the wild animal classification studies in the literature is the catching of the 100% recognition rate targeted by wild animal identification process with simpler mathematical operations and color factor developed without using ready-made algorithms on object recognition. The wild animal recognition algorithms we use in our work are obviously to be the infrastructure for dynamic object detection studies in computer vision applications and all other object recognition will increase the performance ratio in the studies

In this work, a biomimetic approach to provide human-robot interaction by mimicking the motion of human arm and fingers is presented. The movement of an industrial robot is performed by human arm movement in same direction and the control of gripper is also performed by hand movements. For the movement of robot, as a first step, a kinematic model is obtained to give the position of the human hand to the point determined as the origin point in the waist line. In the modelling, the human arm is considered as three limp that are forearm, biceps and shoulder. The rotational angles are obtained from sensors placed in the shoulder, biceps, and forearm, are used in the mathematical model with limb lengths. Rotation kinematics and kinematics matrices are used in these calculations. For the gripper control, a MYO armband with EMG sensors is used. With this EMG sensor on the armband, finger movements are detected from the arm muscles and the pneumatic gripper was controlled in the direction of these movements. A 6-axis robot arm is used in the applications. The calculated position data and the gripper information are transferred to the robot controller via the TCP / IP protocol over Ethernet. A code that provides reaching of robot to calculated position and control the gripper is created and transferred to robot. In the tests, it has been observed that the industrial robot has been successfully controlled by human arm and hand movements

Nowadays, in many recent applications; the data size become too large to be managed within the limited storage environments. In such applications, the data is a streaming data and has a continuously flowing data format. Queries on streaming data are performed using declarative queries, as opposed to queries made on classical databases. Declarative queries provide the ability to filter on a continuous data stream. In addition, they provide the ability to follow the data that match the conditions in the queries. Systems with a data structure in a continuous data flow format can be considered Event-based Systems, in which unit data can be considered as a real event. In such systems; real-time detection of complex events that will trigger business processes is becoming an important need. Within the scope of this research, a Complex Event Monitoring Infrastructure is proposed to address this deficiency. The proposed infrastructure provides the detection of complex events, which form the preconditions for initiating business processes in Event-based Systems. In order to demonstrate the usability of the proposed infrastructure, a prototype application has been developed for e-commerce web sites, where the click-stream data is generated in a streaming data format. Prototype application continuously monitors customer click-stream data collected from the Web site, where the trade takes place (e-commerce environment). Each click-stream data, labeled with a timestamp, is considered as an event data. By combining the basic events, occurring within a certain time period, complex events are identified and the appropriate business processes for corresponding commercial activities are triggered. The developed application was evaluated from the perspective of performance and scalability, and positive results were obtained indicating the usability of the proposed infrastructure.

In a Wireless Sensor Network (WSN), system resources, such as the total node energy, need to be utilized effectively while executing the task of interest. Since a number of sensor nodes may be needed in various type of applications, it is also important to develop each sensor node in a cost effective manner. The purpose of this paper is to develop a custom made, low power, cost-effective wireless sensor node based on a MSP430G2553 microprocessor and nRf24l01+ wireless transceiver module. Then, using these nodes, we form a simple WSN testbed, and locate the position of a light emitting source.

Identification of protein domain-domain interactions (DDIs) is an essential step in understanding proteins’ functional and structural roles. MirrorTree is a DDI prediction method that is based on the principle of interacting proteins’ co-evolution. However, this method is sensitive to taxonomic diversity and evolutionary span within the two protein homolog sets compared to predict DDI.
In this work, we propose a new MirrorTree-based DDI prediction method, namely Taxonomic Diversity-based Domain Interaction Prediction (TAXDIP). TAXDIP improves the MirrorTree method by adding a sampling step that favors representation of higher-level taxonomic ranks (e.g. family over species) in two protein homolog sets prior to their comparison. This additional step ensures increased evolutionary span within protein homolog sets.
TAXDIP is first assessed using a set containing 6,514 positive (interacting) domain pairs and a negative (non-interacting) set of equal size containing randomly generated domain pairs with no known interactions. TAXDIP achieved 71.0% sensitivity and 63.0% specificity on this set. Next, a benchmark-set containing 500 interacting and 500 non-interacting domain pairs is used to compare the performance of TAXDIP against DDI prediction methods ME and RDFF. TAXDIP showed better sensitivity and specificity than RDFF. While TAXDIP’s sensitivity is better than ME, its specificity remained below ME.
In conclusion, TAXDIP, with its performance, is a viable alternative to existing prediction methods. Furthermore, given TAXDIP’s true predictions are overlapping with, and furthermore, complementing other DDI prediction methods, TAXDIP has a strong position in becoming part of a meta-DDI prediction method that combines multiple methods to build a consensus prediction.

In recent years, the research on activity recognition has gained speed especially with the development of smart phones and wearable devices. Activities could be categorized into two main groups. simple activities such as walking, running and complex activities such as eating, sleeping, brushing teeth. In this survey paper, articles about activity recognition are examined thoroughly. Sensors and devices used in activity recognition, types of daily activities, application areas, data collection process, training methods, classification algorithms and resource consumption are mentioned in details. The state of the art is elaborated and the existing methods are compared to each other. Later, open data sets are mentioned and studies offering innovative solutions using latest approaches such as deep learning methods are introduced. Finally, still open issues on this area are presented and future work has been discussed.

Software development processes require continuous improvement in line with emerging new technologies and the possibilities it provides. A new generation of software development models based on product demands of software customers with marketable functions aims to increase the intermediate product production speed and thus the number of interim versions. In the light of these needs, software companies need to oversee their development processes to meet their customers' needs. But more importantly, companies are forced to change their processes in line with innovative practices in order not to cut back on the software production line. In this article, the software development methods of the three companies that develop software are examined in detail by the case study method, and the process change activities are systematically detailed. In the light of the information obtained, the experiences of the three firms in the software development methods are questioned and the effects of these acquisitions on the processes are discussed. As a result of the study, it has been observed that the software development success has a significant impact on the well-being of the process, and the software development teams are trying to design their own processes in the light of the gains they acquire.