Backpropagation is one of the most widely used algorithms for training feedforward deep neural networks. The algorithm requires a differentiable activation function and it performs computations of the gradient proceeding backwards through the feedforward deep neural network from the last layer through to the first layer. In order to calculate the gradient at a specific layer, the gradients of all layers are combined via the chain rule of calculus. One of the biggest disadvantages of the backpropagation is that it requires a large amount of training time. To overcome this issue, this paper proposes a modified backpropagation algorithm with multiplicative calculus. Multiplicative calculus provides an alternative to the classical calculus and it defines new kinds of derivative and integral forms in multiplicative form rather than addition and subtraction forms. The performance analyzes are discussed in various case studies and the results are given comparatively with classical backpropagation algorithm. It is found that the proposed modified backpropagation algorithm converges in less time to the solution and thus provides fast training in the given case studies. It is also shown that the proposed algorithm avoids the local minima problem.

Due to the rapid increase for novel antenna designs to satisfy the modern wireless technologies, different techniques are proposed to improve their performance. For this purpose, this paper presents a novel technique to design a miniaturized microstrip antenna structure based on metamaterial (MTM) transmission line defected patch. The proposed antenna is introduced as part of wearable systems with minimum back radiation toward the human body. Therefore, the antenna is constructed of a closed loop linked to an interdigital capacitor to magnify the electric field fringing at the patch core. The proposed antenna when printed on a FR-4 substrate at 0.6GHz in the first mode shows S11 impedance matching less than -10dB. The proposed antenna performance is evaluated numerically based on CST MWS in terms of radiation pattern. A neural network is invoked to optimize the proposed antenna performance at the required frequency band. Finally, the proposed antenna performance is validated using another software package based on HFSS simulator to provide good agreement with percentage of error less than 4.4%.

In this paper, a universal sign languages recognition system is proposed using two different approaches. Although a variety of sign languages exist in use, the proposed system recognizes three widely used sign languages as the American Sign Language, British Sign Language and Turkish Sign Language to make the recognition universal. One of the proposed approach is primarily based on Hausdorff distance and Hu invariants as the feature vectors and the system first focuses on how to process the hand movements and then it recognizes the different letters by the help of Hausdorff distance and Hu invariants measurements. The second approach is implemented by a feed forward neural network structure and recognition is provided with training data as three different sign language alphabets. It is shown that both approaches operate successfully while experimental results demonstrate that neural network based method provides superior performance.

Trafik işaretleri, karayolunda seyahat eden sürücülere yol kısıtlamaları açısından uyarıda bulunmak amacıyla karayollarına yerleştirilmektedir. Bu işaretlerin doğru bir şekilde algılanması ve trafik işaretinin gerektirdiği kısıtlamaya uyulması, sürüş güvenliği açısından önemlidir. Son yıllarda, derin öğrenme algoritmalarının nesne sınıflandırılmasında ve tespitinde başarılı olduğu birçok çalışma ile gösterilmiştir. Bu çalışmada, derin öğrenme tabanlı “You Only Look Once” (YOLO) algoritmaları trafik işaretleri tespiti açısından karşılaştırılmıştır. İlk olarak 877 görüntüden oluşan dört sınıflı trafik işaretleri veri seti elde edilmiştir. Daha sonra YOLOv5, YOLOv8 ve YOLOv9 algoritmaları trafik işareti tanıması açısından incelenmiştir. Deneysel çalışmalarda, tespit algoritmalarının performanslarını değerlendirmek amacıyla duyarlılık, kesinlik, f1 skor ve mAP performans değerlendirme kriterleri açısından incelenmiştir. Elde edilen deneysel sonuçlara göre YOLOv9’un Duyarlılık metriği %90.8, mAP@0.5 metriği %93.1 ve mAP@0.5:0.95 metriği %77.7 olarak hesaplanmıştır. Bu sonuçlar YOLOv9 algoritmasının en iyi trafik işareti tespit algoritması olduğunu doğrulamaktadır.

Spectrum sensing task is known to be one of the most important functionalities of a cognitive radio for dynamic spectrum access networks to be aware of the radio environment. Licensed user (primary user) detection has gained a great deal of interest recently on spectrum sensing strategies whereas unlicensed user (secondary user) detection must be a part of sensing with equal proportion of interest. This paper proposes a novel scheme that enables an efficient and robust spectrum sensing for both primary and secondary users detection. The proposed scheme is based on intentionally adding known noise at the sensing side. Then the sensing detector becomes insensitive to signal and noise types, and thus noise uncertainty problem is diminished. After sensing, the added noise is removed without affecting existing transmission on the channel. Although there have been several spectrum sensing methods, the proposed scheme is the optimal solution for spectrum sensing, to the best of our knowledge. DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13769