Attia Irum¹, Abdul Muiz Fayyaz¹, Sara Ayub², Mudassar Raza³, Majed Alhaisoni⁴, Muhammad Attique Khan⁵, Abdullah Alqahtani⁶, Heebum Kim⁷, Byeong-Gwon Kang^7,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1265-1276, 2023, DOI:10.32604/csse.2023.032861

Abstract An effective communication application necessitates the cancellation of Impulsive Noise (IN) from Orthogonal Frequency Division Multiplexing (OFDM), which is widely used for wireless applications due to its higher data rate and greater spectral efficiency. The OFDM system is typically corrupted by Impulsive Noise, which is an unwanted short-duration pulse with random amplitude and duration. Impulsive noise is created by humans and has non-Gaussian characteristics, causing problems in communication systems such as high capacity loss and poor error rate performance. Several techniques have been introduced in the literature to solve this type of problem, but they still have many issues that… More >

Ayesha Heena^1,*, Nagashettappa Biradar¹, Najmuddin M. Maroof², Surbhi Bhatia³, Arwa Mashat⁴, Shakila Basheer⁵

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1409-1422, 2023, DOI:10.32604/csse.2023.029611

Abstract Image enhancement is an important preprocessing task as the contrast is low in most of the medical images, Therefore, enhancement becomes the mandatory process before actual image processing should start. This research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images, the proposed operators are based on Grunwald-Letnikov (G-L), Riemann-Liouville (R-L) and Caputo (Li & Xie), which are the definitions of fractional order calculus. In this fractional-order, differentiation is well focused on the enhancement of echocardiographic images. This provoked for developing a non-linear filter mask for image enhancement. The designed filter is… More >

A. K. S. Saranya^1,*, T. Jaya²

Computer Systems Science and Engineering, Vol.45, No.1, pp. 545-562, 2023, DOI:10.32604/csse.2023.029975

Abstract Heartbeat detection stays central to cardiovascular an electrocardiogram (ECG) is used to help with disease diagnosis and management. Existing Convolutional Neural Network (CNN)-based methods suffer from the less generalization problem thus; the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed. In contrast, this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders (KDNN-SAE) that computes the disease before the exact heart rate by combining features from multiple ECG Signals. Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.… More >

Bilal Shoaib¹, Yasir Javed², Muhammad Adnan Khan^3,*, Fahad Ahmad⁴, Rizwan Majeed⁵, Muhammad Saqib Nawaz¹, Muhammad Adeel Ashraf⁶, Abid Iqbal², Muhammad Idrees⁷

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1413-1427, 2021, DOI:10.32604/cmc.2021.015099

Abstract For the unforced dynamical non-linear statespace model, a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article. The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems. With the help of an ortho-normal triangularization method, which relies on numerically stable givens rotation, matrix inversion causes a computational burden, is reduced. Matrix computation possesses many excellent numerical properties such as singularity, symmetry, skew symmetry, and triangularity is achieved by using this algorithm. The proposed method is validated for the prediction of stationary and non-stationary MackeyGlass Time Series, along… More >

Xizheng Zhang^1,2,*, Zhangyu Lu¹, Chongzhuo Tan¹, Zeyu Wang¹

Computer Systems Science and Engineering, Vol.36, No.1, pp. 117-130, 2021, DOI:10.32604/csse.2021.014081

Abstract Regarding the problem of the short driving distance of pure electric vehicles, a battery, super-capacitor, and DC/DC converter are combined to form a hybrid energy storage system (HESS). A fuzzy adaptive filtering-based energy management strategy (FAFBEMS) is proposed to allocate the required power of the vehicle. Firstly, the state of charge (SOC) of the super-capacitor is limited according to the driving/braking mode of the vehicle to ensure that it is in a suitable working state, and fuzzy rules are designed to adaptively adjust the filtering time constant, to realize reasonable power allocation. Then, the positive and negative power are determined,… More >