Medical imaging is an important field of research used for the diagnosis and prediction of diseases. Melanoma is considered as one of the hazardous types of cancers and if detected in early stages, it can be cured easily using simple methods. By using clinical examination, it is difficult to predict melanoma at early stages with high accuracy. This paper proposes a novel strategy for the detection of melanoma by skin malignant growth and also proposes a method for early prediction. The proposed system is based on Deep learning algorithm for the prediction of the affected area and type of melanoma using the metrics precision, accuracy, recall and F1 score. The pre-processing methods are utilized for enhancing the image. The Active contour segmentation process differentiates the infected regions from the healthy skin regions. SOM and CNN classifiers are used for the process of classification of melanoma. A randomly chosen sample of 500 images are taken, 350 images are used as the training dataset and 150 images are used as a testing dataset, for which the proposed system showed high efficiency in the detection of melanoma with a greater accuracy of 90%.
Emerging 5G technology is largely supported by the base station signal processing techniques such as precoding. Precoding combines the input signals in a predefined way and deliver them in a right proportion to the multiple antenna elements. Such precoding algorithms are crucial for the design of the emerging 5G technology; massive MIMO system. In this paper, the performance of various linear precoding algorithms such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Conjugate Gradient (CG) based precoding are analyzed in terms of Bit Error Rate (BER) and achievable sum-rate. The results are analyzed under various channel conditions such as rural, sub urban and urban. The above algorithms are tested for MIMO-OFDM system. OFDM system simulated here uses 52 subcarriers and a Base Station equipped with more than 100 antennas serving multiple users simultaneously. The channel models are stochastic channels simulated using WINNER II modeling.
A large population depends on many industries for its livelihood like electrical, embedded, software product sectors. An IoT environment includes feedback phones that use smart devices, such as MCU (microcontroller unit), sensors and different sensors, collect, distribute, and act on messages they collect from their environment. IoT systems share the sensory data they receive when data has been either sent to the cloud for global applications or analysis by linking to an IoT gateway or other edge device. For the electrical maintenance occur in the industries will need a specific alert signal. The loss of manufacturing workers/workers may be controlled by a specific, minor accident. Therefore, it is essential to provide an emergency device able to detect the non-safety state of coal and produce an alarm to reduce the possibility of risks in the system. We are proposing an IoT-based alert system via mobile phones, as smartphones’ general usage is very common in the industries rather than using a walkie-talkie.
In this paper, a novel 2 × 2 multiple-input multiple-output (MIMO) antenna array with four patch elements is designed. The proposed antenna is the first dual band, operating at two prominent working frequencies: 24 (24.286-25.111) GHz and 77 GHz (75.348–79.688), of automotive radars. This structure is composed of two antenna modules colocated on a single substrate, whereas each module is made up of a corporate fed planar array of two elements. This attractive feature enables us to utilize the antenna in two different ways; either both modules serve as the transmitting/receiving antenna of a monostatic radar or one module serves as a transmitter and the other one as the receiver of a bistatic radar. Most of the existing autonomous radar applications operating at 24 GHz are going to become obsolete, and all countries have plans of shifting towards the 77 GHz band. Hence, our design is very attractive as it operates with the required performance in both the bands with another added feature of the MIMO structure. The placement of antenna elements is also optimized in terms of inter- and intraelement separation of greater than λ/2 so as to ensure high diversity gain of 9.6 dBi. Moreover, the proposed antenna structure with only two antenna elements is able to achieve a high gain of around 11.8 dBi and 11.3 dBi at the dual operating modes of 24 GHz and 77 GHz, respectively. In addition to the above-mentioned benefits, this design also addresses mutual coupling reduction that is a common problem in MIMO structures by using complementary split ring resonator (CSRR) structures. State-of-the-art comparison with the recent literature shows that the proposed antenna has less number of antenna elements, an adequate gain, an excellent VSWR value, and high isolation.
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