In Inverse Synthetic Aperture Radar (ISAR) imaging, the Range Instantaneous Doppler (RID) method is used to compensate for the non-uniform rotational motion of the target that degrades the Doppler resolution of the ISAR image. The Instantaneous Range Instantaneous Doppler method (IRID) is proposed in this paper as a tool that compensates for higher order phase terms that may degrade both, the slant-range resolution and the Doppler resolution of the ISAR image. In IRID, adaptive S-distribution was applied on both the slant-range and Doppler dimensions of the image, thus, producing a better focused ISAR image. The IRID method was applied to simulated and measured data sets, and the ISAR images show that the IRID results offer better visual ISAR images than the RID results.
This chapter addresses a prominent problem that is ubiquitous in scientific and engineering applications including the usually challenging task of cryptanalysis. This problem is the problem of integer factorization, i.e., the decomposition of a composite integer into a product of smaller integers, restricted herein to be prime integers. This is an intractable problem that might admit real-time hardware solutions for small bit sizes. This chapter suggests manual and automated scalable solutions for integer factorization based on equation solving over big Boolean algebras. The manual solution is illustrated over a form of 8-variable Karnaugh maps that is highly regular and modular. This solution covers the problem of 6 bits, which includes the problems of 5, 4, and 3 bits as special cases. Additionally, an automated solution is used, and the findings are then shown and briefly explained. These findings demonstrate the well-known evolution of temporal and spatial complexity with increasing input bit count. In future work, the largest possible hardware circuit that could be obtained via the automated solution is to be constructed, verified and tested. The complexity in our solution comes from two sources. One involves the task of finding the Boolean expressions for the solution. This task is slow, but it has to be done once, and only once, for a problem of a given size. The hardware implementation (e.g., an FPGA implementation) could serve as a ready real-time look-up solution not only of the pertinent problem but also of all smaller problems.
Speech emotion recognition (SER) is a challenging task due to the complex and subtle nature of emotions. This study proposes a novel approach for emotion modeling using speech signals by combining discrete wavelet transform (DWT) with linear prediction coding (LPC). The performance of various classifiers, including support vector machine (SVM), K‐Nearest Neighbors (KNN), Efficient Logistic Regression, Naive Bayes, Ensemble, and Neural Network, was evaluated for emotion classification using the EMO‐DB dataset. Evaluation metrics such as area under the curve (AUC), average prediction accuracy, and cross‐validation techniques were employed. The results indicate that KNN and SVM classifiers exhibited high accuracy in distinguishing sadness from other emotions. Ensemble methods and Neural Networks also demonstrated strong performance in sadness classification. While Efficient Logistic Regression and Naive Bayes classifiers showed competitive performance, they were slightly less accurate compared to other classifiers. Furthermore, the proposed feature extraction method yielded the highest average accuracy, and its combination with formants or wavelet entropy further improved classification accuracy. On the other hand, Efficient Logistic Regression exhibited the lowest accuracies among the classifiers. The uniqueness of this study was that it investigated a combined feature extraction method and integrated them to compare with various forms of combinations. However, the purposes of the investigation include improved performance of the classifiers, high effectiveness of the system, and the potential for emotion classification tasks. These findings can guide the selection of appropriate classifiers and feature extraction methods in future research and real‐world applications. Further investigations can focus on refining classifiers and exploring additional feature extraction techniques to enhance emotion classification accuracy.
The presented paper investigated focusing on 'why' statement in engineering education. For studying the effect of utilizing ‘why’ statement in teaching comparing with other statements such as ‘what’, ‘how’ and determination (state), five aspects were investigated to spot the light on the influence of the ‘why’ statement in teaching over several angles. So, passing, captivating the attention, review the taught idea, motivation and concluding the taught idea were used for testing the hypothesis. Five questionnaires were used for these aspects. Two manners of the statements’ order in the questionnaires were used. The first manner, the ‘why’ statement is always the first, but in the second manner can have any order. The results showed the high effect of ‘why’ and ‘what’ statements in persuasion the students studying the engineering subjects. The second manner had a better effect on enhancing the results of 'why' statement.
Abstract Spread spectrum code‐division multiple‐access (CDMA) systems are currently considered as very attractive alternatives to the more familiar FDMA and TDMA systems, especially in the presence of multipath fading and external interference. The problem of code selection in a CDMA system with a finite number of users is addressed in this paper. A simple and efficient method for the selection of finite code sets with relatively high processing gain from Gold and Kasami code families of relatively large sizes is described in detail. Selected code sets of period 63 are presented along with their overall average performance parameters for different number of users.
Gamma radiography is used to monitor the corrosion of pipelines in remote locations; usually high radioactivity (1011–1012 Bq) is used. The technique is also not useful for imaging pipes with thick walls or large vessel walls. In this work, Compton backscattered radiation was used for the wall-thickness determination and corrosion imaging of pipe and flat materials using extremely-low-activity sources with radioactivities on the order of 104–105 Bq. A two-dimensional scanning system was designed to scan object surfaces, and the signals from a NaI(Tl) scintillation detector were fed into a computer for image construction using the LabView program. Thicknesses greater than 1 cm and 1.5 cm could be measured for Fe and Al and for polyvinyl chloride (PVC) and poly methyl methacrylate (PMMA), respectively. It was also possible to detect changes of less than 1 mm in depression depth for depressions measuring 3 mm in diameter. One- and two-dimensional images artificial defects on a pipe surface were successfully constructed.
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