Structural Design and Testing of Mass Timber Concrete Composite Floor Solutions with Acoustic Interlayers

Timber-concrete composite (TCC) solutions originated in the early twentieth century. There has been a renewed interest in TCC solutions in modern mass timber buildings, especially for floor systems. However due to lack of data, design standards, and code acceptance, TCC solutions require that new mass timber solutions be tested, and design methods validated or adjusted based on new findings. This thesis characterizes the interface shear and flexural properties of different TCC solutions through an experimental testing program that combined mass-ply panels (MPP), reinforced concrete topping, and various interface layers and connection systems to create novel composite floor systems. Fifteen TCC solutions are presented. These consist of 102 mm thick MPP, 75 mm thick reinforced concrete topping, two interface shear connection solutions (screws and steel mesh), and three levels of acoustic insulation layer (no insulation, 5mm layer, 19mm layer). Structural tests performed included: (1) Pushout tests, (2) Comparative one-way bending tests, and (3) Orthotropic stiffness tests. Within the experimental program design, pushout and comparative one-way bending test results indicate that higher stiffness and strength can be achieved with interface shear solutions that included steel mesh versus those that included screws. In addition, inclusion of insulation layers results in statistically significant reduction of strength and stiffness in floor solutions that include screws, while increased stiffness and negligible reductions in strength were observed in solutions that include the steel mesh

The 2022 SIM IT Issues and Trends Study

The Society for Information Management’s 42nd Annual IT Issues and Trends Study received responses from 796 IT executives, including 334 CIOs and 540 unique organizations. The average revenue of participating organizations was $6.1 billion (median$400 million). IT spending as a percentage of revenue was 5.9%, up slightly from 2021 but close to the 10-year average of 5.7%. However, 74.6% of organizations reported increasing IT headcount, a 10-year high and up from 63.6% in 2021. Similarly, 94.8% reported increases in average IT salaries. The top five IT management issues for organizations in 2022 were Cybersecurity, Alignment, Analytics, Compliance and Digital Transformation; the top five largest IT investments were Analytics, Cybersecurity, Cloud, Application Development and ERP; while the five most difficult to find soft skills were Critical Thinking, Teamwork, Business Acumen, Leadership and Problem Solving. The most common criteria for assessing CIO performance were Value of IT to the Business, Internal Customer Satisfaction, Cybersecurity, Strategic Contribution of IT and IT Availability. The average tenure of CIOs was 5.9 years (median 4 years) with 48% reporting to the CEO. CIOs continue to come from outside organizations at record levels (82%), and 24.6 came from prior non-IT positions

PAPR REDUCTION IN ACO-OFDM FOR VISIBLE LIGHT COMMUNICATION SYSTEM

Visible Light Communication (VLC) is gaining popularity in optical wireless. In conventional OFDM, bi-polar signals having both positive and negative values are considered. However, in optical OFDM uni-polar signals, which have only positive values, are used. Therefore, suitable changes have to be done in conventional OFDM to make it compatible with O-OFDM. These modifications lead to the generation of asymmetrically clipped optical Orthogonal Frequency Division Multiplexing (ACO-OFDM) technology. In ACO-OFDM systems Peak to Average Power Ratio (PAPR) is a detrimental effect and should be suppressed. In ACO-OFDM, the estimation of probability density function (pdf) is not straightforward; therefore, a very limited literature is available. In this paper, an attempt is made to estimate pdf and Complimentary Cumulative Distribution Function (CCDF) expression for an ACO-OFDM with intensity modulation and direct detection (IM/DD), and its validity is checked by using simulation results. For ACO-OFDM scheme PAPR reduction methodology is used by applying various clipping strategies along with non-linear µ-law companding scheme. The results presented in the paper are obtained through computer simulation using MATLAB software. As clipping increases Bit Error Rate (BER), therefore, at various clipping mechanism BER are also obtained. It has been found, that by choosing suitable clipping along with non-linear companding scheme, PAPR can be reduced significantly while maintaining reasonable good BER performance. It is found, that with the proposed technique, PAPR is reduced by 76.10% as compared to raw ACO-OFDM

A Hybrid Machine Learning Technique For Feature Optimization In Object-Based Classification of Debris-Covered Glaciers

Object-based features like spectral, topographic, and textural are supportive to determine debris-covered glacier classes. The original feature space includes relevant and irrelevant features. The inclusion of all these features increases the complexity and renders the classifier’s performance. Therefore, feature space optimization is requisite for the classification process. Previous studies have shown a rigorous exercise in manually selecting the best combination of features to define the target class and proven to be a time consuming task. The present study proposed a hybrid feature selection technique to automate the selection of the best suitable features. This study aimed to reduce the classifier’s complexity and enhance the performance of the classification model. Relief-F and Pearson Correlation filter-based feature selection methods ranked features according to the relevance and filtered out irrelevant or less important features based on the defined condition. Later, the hybrid model selected the common features to get an optimal feature set. The proposed hybrid model was tested on Landsat 8 images of debris-covered glaciers in Central Karakoram Range and validated with present glacier inventories. The results showed that the classification accuracy of the proposed hybrid feature selection model with a Decision Tree classifier is 99.82%, which is better than the classification results obtained using other mapping techniques. In addition, the hybrid feature selection technique has sped up the process of classification by reducing the number of features by 77% without compromising the classification accuracy

Genetic Algorithm for Combined Speaker and Speech Recognition using Deep Neural Networks, Journal of Telecommunications and Information Technology, 2018, nr 2

Huge growth is observed in the speech and speaker recognition field due to many artificial intelligence algorithms being applied. Speech is used to convey messages via the language being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algorithm (GA) for combined speaker and speech recognition, relying on optimized Mel Frequency Cepstral Coefficient (MFCC) speech features, and classification is performed using a Deep Neural Network (DNN). In the first phase, feature extraction using MFCC is executed. Then, feature optimization is performed using GA. In the second phase training is conducted using DNN. Evaluation and validation of the proposed work model is done by setting a real environment, and efficiency is calculated on the basis of such parameters as accuracy, precision rate, recall rate, sensitivity, and specificity. Also, this paper presents an evaluation of such feature extraction methods as linear predictive coding coefficient (LPCC), perceptual linear prediction (PLP), mel frequency cepstral coefficients (MFCC) and relative spectra filtering (RASTA), with all of them used for combined speaker and speech recognition systems. A comparison of different methods based on existing techniques for both clean and noisy environments is made as well

Speech-Based Vehicle Movement Control Solution, Journal of Telecommunications and Information Technology, 2021, nr 3

The article describes a speech-based robotic prototype designed to aid the movement of elderly or handicapped individuals. Mel frequency cepstral coefficients (MFCC) are used for the extraction of speech features and a deep belief network (DBN) is trained for the recognition of commands. The prototype was tested in a real-world environment and achieved an accuracy rate of 87.4