Deep Learning Based Detection on RIS Assisted RSM and RSSK Techniques

The reconfigurable intelligent surface (RIS) is considered a crucial technology for the future of wireless communication. Recently, there has been significant interest in combining RIS with spatial modulation (SM) or space shift keying (SSK) to achieve a balance between spectral and energy efficiency. In this paper, we have investigated the use of deep learning techniques for detection in RIS-aided received SM (RSM)/received-SSK (RSSK) systems over Weibull fading channels, specifically by extending the RIS-aided SM/SSK system to a specific case of the conventional SM system. By employing the concept of neural networks, the study focuses on model-driven deep learning detection namely block deep neural networks (B-DNN) for RIS-aided SM systems and compares its performance against maximum likelihood (ML) and greedy detectors. Finally, it has been demonstrated by Monte Carlo simulation that while B-DNN achieved a bit error rate (BER) performance close to that of ML, it gave better results than the Greedy detector.Comment: Accepted to be published in GlobeComm 2023 Worksho

THE FAULT DIAGNOSIS MODEL BASED ON ARTIFICIAL IMMUNE SYSTEM USING GENETIC ALGORITHM

Bu çalışmada asenkron motor arızalarını tespit etmek için yapay bağışık sistem tabanlı arıza teşhis metodu önerilmiştir. Önerilen metot kırık rotor çubuğu arızlarını tespit etmek için negatif seçim algoritmasını kullanır. Arıza ile ilgili özellikler motor akımın bir fazının ilk fark filtrelemesi ve hilbert dönüşümü kullanılarak elde edilir. Bu özellik sinyallerinin faz uzayı nonlineer zaman serileri analizi yöntemi ile elde edilerek negatif seçimin giriş verisi oluşturulur. Hilbert tabanlı dönüşüm olarak adlandırılan yeni özellik sinyali faz uzayında kırık rotor çubuğu arızalarını ayırt etmek için kullanışlıdır. Orjinal negatif seçim algoritmasında detektörler rastgele üretilir. Fakat rasgele üretilen detektörler iki probleme sahiptir. Birincisi öz olmayan uzay kapsanmayabilir. İkincisi benzer detektörlerin üretimini engellemek için herhangi bir sınırlama yoktur. Genetik algoritma negatif seçimin detektörlerini optimize etmek ve üretmek için kullanılmıştır. Minimum detektör sayısı ile öz olmayan uzayın maksimum kapsanması sağlanmıştır. Önerilen yöntemin doğruluğu zaman adımlı birleştirilmiş sonlu elaman durum uzayı ile elde edilen simülasyon verileri kullanılarak doğrulanmıştır. In this study, artificial immune system based fault diagnosis method has been proposed to detect the induction motor faults. The proposed method uses negative selection algorithm to detect broken rotor bar faults. Fault related features are obtained using Hilbert transform and first difference filtering of one phase motor current.The phase space of these feature signals is obtained using a nonlinear time series analysis and they constitute the input data of the negative selection. The new feature signal called Hilbert based transform is quite useful to separate broken rotor bar faults in the phase space. In the original negative selection algorithm detectors are randomly generated. But randomly generated detectors have two problems. The first is that the non-self space may not be covered, completely. The second problem is that there is not any restriction to deny generation of similar detectors. The genetic algorithm is used to generate and optimize the detectors of the negative selection. The maximum coverage of non-self space with minimum detector numbers is ensured. The accuracy of method has been verified using simulation data that obtained by time-stepping coupled finite element state space method

A Deep Learning-Based Hybrid Approach to Detect Fastener Defects in Real-Time

A fastener is an important component used to fix the rail in railways. Defects in this component cause the rail and ballast to remain unstable. If the defective fasteners are not replaced in time, it is inevitable that the train will derail, and serious accidents will occur. Therefore, this component should be inspected periodically. Conventional image processing-based control systems are affected by noise and different lighting conditions in the real environment. In this study, it is aimed to determine the defects of fasteners with a deep learning-based hybrid approach. The YOLOv4-Tiny method is used for fastener detection and localization. This method gives accurate results, especially for the detection of small objects. After the fastener position is determined, a new lightweight convolutional neural network model is used for defect classification. The proposed convolutional neural network for classification has a small network structure because it uses depth-wise and pointwise convolution layers. When the experimental results are compared with other known transfer learning methods, better results were obtained in terms of training/test time and accuracy

Mejora de stocks en el sudeste del Mar Negro, mediante la liberación de rodaballos, Psetta maxima, criados en cautividad: análisis de la captura, migración, crecimiento y dieta

In this study, the capture, growth, migration and diet of hatchery-released turbots (Psetta maxima) were examined in the southeastern Black Sea region for six years (2009-2014). A total of 9933 turbots were marked with individual serial-numbered T-bar anchor tags and released at Trabzon, Turkey. The mean TL and weight of the released turbots were 12.91 cm (±1.25) and 35.41 g (±12.38) and the same measurements for the captured turbots were 31.17±0.86 cm and 878.08±69.47 g, respectively. A total of 2.7% (270 fishes) of the tagged individuals were captured during the study period and the age of the captured tagged fishes was between 0+ and 5+ years. Growth of the captured turbots was analytically examined. Movements of the tagged turbots were expressed as “resident” and “migratory”. Three prey groups showed the majority of forage organisms; teleost fishes, crustaceans and mollusks in the stomach of the captured turbots. The hatchery-released turbots might be used for stock enhancement due to their high growth rate and commercial value, and their relatively limited migration range.En este estudio se examinaron datos de captura, crecimiento, migración y dieta, de rodaballos (Psetta maxima) criados en cautividad y liberados en zonas del sudeste del Mar Negro, durante seis años (2009-2014). Se marcaron individualmente un total de 9933 rodaballos, utilizando marcas numeradas en serie (marcas de plástico tipo T) y se liberaron en Trabzon (Turquía). Las medias de longitud total (TL) y peso total de los rodaballos liberados fueron de 12.91 cm (±1.25) y 35.41 g (±12.38), y las mismas medidas para los rodaballos capturados fueron de 31.17±0.86 cm y 878.08±69.47 g, respectivamente. Un 2.7% (270 peces) de los individuos marcados fue capturado durante el periodo de estudio y la edad de estos individuos osciló entre 0+ y 5+ años de edad. El crecimiento de los rodaballos capturados se examinó analíticamente. Los movimientos de los rodaballos marcados fueron expresados como “residentes” y “migratorios”. La mayoría de los organismos presentes en los estómagos de los rodaballos capturados pertenecían a tres grupos de presas: peces teleósteos, crustáceos y moluscos. Los rodaballos criados en cautividad y liberados podrían ser usados en la mejora de stocks debido a su elevada tasa de crecimiento, valor comercial, y su relativamente limitado rango de migración

Reproductive Characteristics and Egg Development in Flounder (Pleuronectes flesus luscus) in the Southern Black Sea

Abstract Spawning time, total fecundity, egg size, fertilization and hatching rates, and egg development of the flounder, Pleuronectes flesus luscus, were investigated in six wild female broodstock (mean wt 394.4±226.8 g). Spawning lasted 33 days from December 29 to January 30. Mean total fecundity was 171.4±109 x 10 3 eggs per female. Newly ovulated eggs were spherical and buoyant, with a diameter of 1.075-1.213 mm (avg 1.156±0.025 mm), a colorless transparent chorion, a slightly yellowish unsegmented yolk, and a narrow perivitelline space, without an oil globule. Fertilization and hatching rates were 17.2±15.7% and 51.5±27.6%, respectively. Hatching occurred after 117 h of incubation at 9.8-11ºC. There were variations in egg size between batches, with the size tending to decrease during the spawning season (p<0.05)

Black sea aquaculture: Legacy, challenges & future opportunities

Responsible aquaculture, the farming of aquatic organisms, is a sustainable strategic sector for land and coastal communities. It significantly contributes to food security and enhancement of economic development; it provides employment opportunities and often contributes to the ecological services provided by the environment. According to the Food and Agriculture Organization of the United Nations (FAO), the contribution of aquaculture to the global food security is widely demonstrated by an astounding industry growth of 7.5% per year since 1970. In 2018, aquaculture reached the all-time highest production of 114.5 million tonnes in live weight with a total farm gate sale value of USD 263.6 billion. This makes aquaculture a key player within the Blue Growth concept and a strong contributor to some of its key Sustainable Development Goals (SDG). This is particularly true in geographical areas where dependence of local economies on fishery products is high, and yet access to sustainable landings is hampered by ecological barriers. One such area is represented by the Black Sea basin. Whilst the Black Sea annual capture fishery production has varied considerably since 1990 and its current landings are significant, growing attention is currently given to boost aquaculture development along the Black Sea bordering countries, with marine aquaculture being considered as an important contributor to the total fisheries production. Nonetheless, aquaculture development in this region is not homogenous and its development has, so far, been limited by environmental, economic, social, and more generally governance issues. This paper, for the first time, attempts to provide a comprehensive fresh outlook of the aquaculture sector in the Black Sea, stressing the importance of regional cooperation as an essential pillar to support the sustainable development of the industry. The paper addresses aquaculture in the Black Sea from different perspectives: it outlines the key characteristics of the Black Sea environment; it discusses the most common farmed aquatic species and the potential for new ones; it frames the national approaches to aquaculture development, sharing information about success stories, while shedding light on the main challenges and priorities ahead. This collective endeavour will represent a helpful contribution to Black Sea riparian countries to answer the many questions they have, and expectations they hold from the aquaculture sector.Additional co-authors: Dilek Fidan, Linda Fourdain, Marco Frederiksen, Archil Guchmanidze, Housam Hamza, Jessica Harvey, Magda Nenciu, Galin Nikolov, Victor Niţă, Muhammed Doğan Özdemir, Elitsa Petrova-Pavlova, Gabriel Popescu, Ferit Rad, Şafak Seyhaneyildiz Can, John A. Theodorou, Behnan Thomas, Nicolò Tonachella, Ekaterina Tribilustova, Irina Yakhontova, Ahmet Faruk Yesilsu, Güzel Yücel-Gie

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic