- Eklemek veya çıkarmak istediğiniz kriterleriniz için 'Dahil' / 'Hariç' seçeneğini kullanabilirsiniz. Sorgu satırları birbirine 'VE' bağlacı ile bağlıdır. - İptal tuşuna basarak normal aramaya dönebilirsiniz.
Following recent advances in digital technologies, many data in various domains have been transformed into digital world and shared with millions of users via social media and web technologies. As a result, big amount of data has presented many challenging problems in different fields, e.g internet of things, artificial intelligence. One of application areas is in food domain. Recognition of food category from images, automatic recipe retrieval from internet and analysis and matching of food images with recipes, ingredients, nutrition values bring cooperation of multi disciplines and technologies. In this work, for the first time, s . . .emantical analysis of Turkish Cuisine is held and various information related to food in Turkish Cuisine is structured in a hierarchical ontology model. A new database containing 50 different food categories and related images is constructed and linked with data such as food properties, recipes, etc.. As a result, multimodal information retrieval can be achieved faster in a more semantic way. At the same time, food image classification with deep learning methods is performed and faster connection of recognized food category to related semantic data is provided
Daha fazlası
Daha az
Automatic extraction of bare-Earth LiDAR points to generate Digital Terrain Model (DTM) is still an ongoing problem. Even though there are several methods for ground filtering, automatic and adaptive methods are still a need due to the complexity of the environment. In this study, we address the ground filtering problem by applying Empirical Mode Decomposition (EMD) to the airborne LiDAR data. EMD is a data-driven method that adapts to the local characteristics of the signal. We benefit from EMD to extract the local trend of the LiDAR height data. This way, can extract a local adaptive threshold to filter ground and non-ground objec . . .ts. We tested our method using the ISPRS LiDAR reference dataset and obtained promising results. We also compared the filtering results with the ones in the literature to show the improvements obtained
Daha fazlası
Daha az
Epilepsy is a chronic neurological disorder in which the normal pattern of neuronal activity in the brain becomes disturbed. Identification of the brain region that is abnormally active during an epileptic seizure is vital for epilepsy surgery. One way of achieving so is to collect electroencephalography ( EEG) signals from epileptic people and then to identify the active region as a seizure occurs. In this work, we present a Bayesian change point model that detects when seizures occur. We applied our method to a data set that contains 48 EEG and electrocardiography (EKG) record pairs collected from epileptic people and observed tha . . .t the model is able to detect all seizures
Daha fazlası
Daha az
In this paper, we first form a sensor using low power and low cost components, that is, MSP430G2553 microcontroller, nRF24L01+ communication unit, and a Light Dependent Resistor (LDR). Then, by communicating such sensors with a Fusion Center (FC), we form a Wireless Sensor Network (WSN). Sensors measure the light intensities at their locations and send their measurements to the FC. Then, FC forwards the sensor measurements to the computer which performs field estimation using ordinary kriging. Our test results show that the predicted field measurement using ordinary kriging at a specific location is quite close to the actual sensor . . .measurement at that location
Daha fazlası
Daha az
Publish-Subscribe(P/S) based sensor monitoring systems provide different quality-of-service according to client-server policies. Using adaptive timing and data freshness models may increase the flexibility of services in these systems. In this paper, we propose two timing models for sensor network based emergency detection P/S Systems in which the timing constraints and data freshness can be dynamically adjusted for user needs. The performance of these models is tested in a real testbed setup. The experimental results reveal that the proposed models can meet the timing and data freshness demands of the clients for applications which . . . acquire physical data from scalar sensors with 2-10s sampling rates
Daha fazlası
Daha az
Ship detection in satellite images is used for monitoring illegal fishing and violation of coastal waters or general maritime management. SAR images has been extensively used in this manner. Recently, researchers have started using optical satellite images for ship detection. These studies can be divided into two categories as inshore and offshore ship detection. Offshore ship detection is a relatively easy problem. But, inshore ship detection is a challenging problem in which ships are located closely. In this study, we handle this problem and propose a novel method assuming we have the harbor information beforehand. In the method, . . . the mask is morphologically thickened in single steps and adjacent ships are detected with a contour following method. Moreover, a shape model for the ships is used to eliminate false alarms. We tested the proposed method on high resolution satellite images and achieved promising results
Daha fazlası
Daha az
Smith Waterman Algorithm is a widely used tool in bioinformatics to align reads from aligning to a reference in whole genome sequencing. Mapping millions of sequences read from sequencing is a computationally expensive operation. Accuracy and performance are two important aspects of this process. FPGA based solutions are widely studied. In this paper we tried to achieve a better mapping accuracy for optimum alignment using quality scores of the bases of read sequences while keeping the performance high. We are offering a new Smith Waterman processing unit and systolic array based on quality scores.
Human activity monitoring enables detecting instances when people need help during daily routines. They may have forgotten taking medication or they can experience more severe situations such as falling. Detecting their activities yield their context information revealing occurrences of such cases. We designed and implemented a solution to activity detection proposing a Support Vector Machine (SVM) based method. We gathered data through accelerometer to come up with a noninvasive solution. Our method is the combination of a feature extractor and classifier. Presented activity recognition suit eliminates the need for experimenting wi . . .th multiple features to determine the best classifying features contrary to some approaches utilizing SVM. With our SVM based activity recognizer, we classified sit, stand, lie and walk actions with 100 % accuracy
Daha fazlası
Daha az
Scanning EMG is a method developed for examining the electro-physiological cross section and the size of the motor unit of a human muscle. Electrical specifications of the motor unit can be obtained as well as anatomical distribution of muscle fibers and pathological changes between different muscles can be examined by the help of this method. In this paper; an automation system which is designed for the execution of scanning EMG method, whether manually or automatically, and a user-interface are described. Parameters like step count and step size which are about the movement of an electrode. moved by a linear actuator, through musc . . .le fibers can be defined as reference by user via designed interface. As a result, acquired signals are digitalized by data-acquisition card (DAQ) and saved as text file for the future signal process tasks
Daha fazlası
Daha az
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.
