Vibration of circumferentially stepped-thickness piezoelectric cylindrical shells

In this work, thickness variations are introduced around the circumference of a piezoelectric cylindrical shell. The aim is to investigate the vibration characteristics of the shells and the effect of these step-thickness variations on the mode shape of vibration. These thickness variations require stress distribution analysis as well to avoid failure of the cylindrical shell. To this aim, two configurations of stepped-thickness shells with two and three circumferential thickness variations are investigated using FEA software, ANSYS. The results show that these steps assist in localizing vibration in the thin sections and excite mode shapes having the same circumferential wave number as the number of thickness variations. This can be a suitable approach to control and forcibly excite certain vibration mode shapes, which might be required for some applications

Probabilistic Design of Multi-Dimensional Spatially-Coupled Codes

Because of their excellent asymptotic and finite-length performance, spatially-coupled (SC) codes are a class of low-density parity-check codes that is gaining increasing attention. Multi-dimensional (MD) SC codes are constructed by connecting copies of an SC code via relocations in order to mitigate various sources of non-uniformity and improve performance in many data storage and data transmission systems. As the number of degrees of freedom in the MD-SC code design increases, appropriately exploiting them becomes more difficult because of the complexity growth of the design process. In this paper, we propose a probabilistic framework for the MD-SC code design, which is based on the gradient-descent (GD) algorithm, to design better MD codes and address this challenge. In particular, we express the expected number of short cycles, which we seek to minimize, in the graph representation of the code in terms of entries of a probability-distribution matrix that characterizes the MD-SC code design. We then find a locally-optimal probability distribution, which serves as the starting point of a finite-length algorithmic optimizer that produces the final MD-SC code. We offer the theoretical analysis as well as the algorithms, and we present experimental results demonstrating that our MD codes, conveniently called GD-MD codes, have notably lower short cycle numbers compared with the available state-of-the-art. Moreover, our algorithms converge on solutions in few iterations, which confirms the complexity reduction as a result of limiting the search space via the locally-optimal GD-MD distributions

Impacts of land use change on streamflows in the Damansara watershed, Malaysia.

Land-use change has significant impacts on hydrologic processes at the watershed level. In this study, hydrologic models and spatial data were used to assess the effects of land-use changes and predict the effects of two future land-use scenarios on the flood regime of the Damansara Watershed. Due to urban growth, the Damansara Watershed has seen increasing streamflows and experienced occasional flooding. The hydrology was modeled using the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model, and land-use changes were quantified with land-use maps. Actual storms were used to calibrate and validate HEC-HMS rainfall-runoff model. The calibrated HEC-HMS model was used to simulate future streamflows and to forecast the impact of land-use changes on downstream peak streamflow. The model also estimated the contribution of individual sub-basins to downstream peak streamflows of the entire watershed. The model predicts that changes in land-use will increase the peak streamflow, and the increase is directly proportional to the rate of urbanization. It was found that the sensitivity of the hydrologic response to land-use change increases as the recurrence interval of rainfall events decreases, and that those impacts are more pronounced in different sub-basins. The results of this study provide support for land-use planning and the management of watersheds

Investigation of THz Frequency Shaped Anode Planar Gunn Diodes Operating in Delayed Mode

Funding Information: This work was supported by Saudi Arabia’s Ministry of Higher Education. ACKNOWLEDGMENT The authors thank the University of Aberdeen for providing the necessary support. Publisher Copyright: © 2013 IEEE.Peer reviewedPublisher PD

n point dct vlsi architecture for emerging hevc standard

This work presents a flexible VLSI architecture to compute the -point DCT. Since HEVC supports different block sizes for the computation of the DCT, that is, up to , the design of a flexible architecture to support them helps reducing the area overhead of hardware implementations. The hardware proposed in this work is partially folded to save area and to get speed for large video sequences sizes. The proposed architecture relies on the decomposition of the DCT matrices into sparse submatrices in order to reduce the multiplications. Finally, multiplications are completely eliminated using the lifting scheme. The proposed architecture sustains real-time processing of 1080P HD video codec running at 150 MHz

A local scour prediction method for pile caps in complex piers

The outcomes of an experimental study on local scour at a pile cap are presented. Experiments were conducted with the approaching flow having an undisturbed flow depth and a threshold flow velocity. The main variables investigated were pile cap dimensions and location relative to the streambed. According to the rate of change in scour depth, the scour at a pile cap for different cap levels was divided into four cases. Equations for a correction factor for these four cases are derived. The correction factor Kc has the effect of reducing the scour depth from a corresponding full-depth pier of the same width as the pile cap. A new methodology is presented to estimate local scour depth at a pile cap as a component of a complex pier. The proposed method was evaluated with the results from this experimental study and historical measurements. The proposed method, which corresponds closely to the observations, can be used to predict local scour at a pile cap as a component of a complex pier in the superposition method. It is also applicable to the prediction of local scour due to a caisson being sunk onto a mobile bed in a current

Investigation of contact edge effects in the channel of planar Gunn diodes

The effect of the edge of the channel on the operation of Planar Gunn diodes has been examined using Monte Carlo simulations. High fields at the corner of the anode contact are known to cause impact ionization and consequent electroluminescence, but our simulations show that the Gunn domains are attracted to these corners, perturbing the formation of the domains which can lead to chaotic dynamics within the rest of the channel leading to uneven heating and reduced RF output power. We show how novel shaping of the electrical contacts at the ends of the channel reduces the attraction and restores the domain wave-fronts for good device operation

Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition

Crafting an effective Automatic Speech Recognition (ASR) solution for dialects demands innovative approaches that not only address the data scarcity issue but also navigate the intricacies of linguistic diversity. In this paper, we address the aforementioned ASR challenge, focusing on the Tunisian dialect. First, textual and audio data is collected and in some cases annotated. Second, we explore self-supervision, semi-supervision and few-shot code-switching approaches to push the state-of-the-art on different Tunisian test sets; covering different acoustic, linguistic and prosodic conditions. Finally, and given the absence of conventional spelling, we produce a human evaluation of our transcripts to avoid the noise coming from spelling inadequacies in our testing references. Our models, allowing to transcribe audio samples in a linguistic mix involving Tunisian Arabic, English and French, and all the data used during training and testing are released for public use and further improvements.Comment: 6 pages, submitted to ICASSP 202

Experimental work of effect of openings on the post-tensioned flat slab

This study aims to evaluate the effect of various parameters on the behavior of the reinforced concrete flat slabs and the contribution of each design element in the punching shear strength. This research presents experimental results of tested post-tensioned flat slabs with opening under concentric compressive load. The developed post-tensioned flat slabs are to ensure adequate punching shear strength capacity. The experimental work consisted of eight specimens of post-tensioned reinforced concrete flat slabs which classified into groups. All slabs had the same dimension and reinforcement. The slabs had dimensions with a 1750 mm length and 1750 mm width, to study the behavior of post-tensioned flat slab with/out openings under the concentrated load and punching influence

Accurate metaheuristic deep convolutional structure for a robust human gait recognition

Gait recognition has become a developing technology in various security, industrial, medical, and military applications. This paper proposed a deep convolutional neural network (CNN) model to authenticate humans via their walking style. The proposed model has been applied to two commonly used standardized datasets, Chinese Academy of Sciences (CASIA) and Osaka University-Institute of Scientific and Industrial Research (OU-ISIR). After the silhouette images have been isolated from the gait image datasets, their features have been extracted using the proposed deep CNN and the traditional ones, including AlexNet, Inception (GoogleNet), VGGNet, ResNet50, and Xception. The best features were selected using genetic, grey wolf optimizer (GWO), particle swarm optimizer (PSO), and chi-square algorithms. Finally, recognize the selected features using the proposed deep neural network (DNN). Several performance evaluation parameters have been estimated to evaluate the model’s quality, including accuracy, specificity, sensitivity, false negative rate (FNR), and training time. Experiments have demonstrated that the suggested framework with a genetic feature selector outperforms previous selectors and recent research, scoring accuracy values of 99.46% and 99.09% for evaluating the CASIA and OU-ISIR datasets, respectively, in low time (19 seconds for training)