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The purpose of this paper is to find out the use of compressive sensing (CS) algorithm for wireless sensor networks (WSNs). As energy-efficient algorithms are required for WSNs, CS is very much useful as less than 25 per cent of the entire input data alone is required to be transmitted, and reconstruction at the receiver with this reduced data set is of good quality. But, the usefulness of the algorithm with suitable modulation schemes is not analyzed so far in the literature. Hence, this work concentrated on the algorithm performance with different modulation schemes and different channel conditions.

Compressive sensing encoding is performed by using suitable transform on the input signal. Here, DCT and DWT are used to generate the sparse signal. Random measurement matrix is used to generate the compressed output, which is reconstructed using the Basis Pursuit (BP) method. Also, an analysis for the energy-efficient modulation scheme is performed by modulating the compressed output using QPSK/BPSK/QAM and transmitted by considering the Gaussian and Rayleigh Channels. Energy required per bit transmission is modeled and computed for different schemes.

Simulation result shows that the use of CS algorithm for data compression tremendously reduces the number of transmission bits and, hence, enhances the transmission and bandwidth efficiency in WSN. Results show that DWT is a much suitable transform to be used for sparse measurement generation. In comparison with DCT, DWT is computationally simple and takes very less time, which is expected in real-time application. The reconstruction result shows that about 25 per cent of the data sample is sufficient to recover the original image, perhaps which is the most surprising result. An extensive analysis of various modulation schemes based on the energy model shows that QPSK is in the AWGN channel, and QAM modulation in the Rayleigh channel is a much suitable modulation scheme to be used in WSN for further reduction of energy consumption.

Compressive sensing is recently gaining importance for quantization, compression and noise removal in images. In this paper, this technique was used along with modulation schemes to analyze the suitability of the algorithm for WSN.

Sensor networks have found wide applications in the monitoring of environmental events such as temperature, earthquakes, fire and pollution. A major challenge with sensor network hardware is their limited available energy resource, which makes the low power design of these sensors important. This paper aims to present a low power sensor which can detect sound waveform signatures.

A novel mixed signal hardware is presented to correlate the received sound signal with a specific sound signal template. The architecture uses pulse width modulation and a single bit digital delay line to propagate the input signal over time and analog current multiplier units to perform template matching with low power usage.

The proposed method is evaluated for a chainsaw signature detection application in forest environments, under different supply voltage values, input signal quantization levels and also different template sample points. It is observed that an appropriate combination of these parameters can optimize the power and accuracy of the presented method.

The proposed mixed signal architecture allows voltage and power reduction compared with conventional methods. A network of these sensors can be used to detect sound signatures in energy limited environments. Such applications can be found in the detection of chainsaw and gunshot sounds in forests to prevent illegal logging and hunting activities.

Out-of-pocket (OOP) payments continue to be a major method of financing healthcare in many low- and middle-income countries including Malaysia. Although macro-level data show that this is a substantial percentage of national health expenditure, at the grassroots level, the amount spent on health by households remains unknown in Malaysia. The purpose of this paper is to assess the validity and reliability of an adapted-for-purpose questionnaire designed to capture urban household health expenditures (HHEs) among Malaysian households.

This two-part study assessed content validity of the questionnaire using three experts and the reliability of the questionnaire through a test-retest study among 50 OOP-paying patients followed up at one private primary care clinic in Kuala Lumpur. This study was approved by the Malaysian Research Ethics Committee (NMRR-16-172-29311-IIR).

The validity of the 83-item questionnaire was high, with an item content validity index of 1.00 and a scale content validity index average score of 1.0 agreed to among the evaluating experts. In the test-retest reliability study, the majority of the categorical questionnaire items had perfect agreement values (k=0.81-1.00). Continuous questionnaire items were also found to be highly reliable with no significant differences between the test-retest segments and high correlation coefficient values (intra-class correlation coefficient>0.7).

The HHE questionnaire had excellent content validity and very high test-retest reliability. The results of this study suggest that this questionnaire could be used in Malaysian studies to determine actual urban HHE which is a first step toward developing universal health coverage for all.

The outbreak of COVID-19 has projected prominent threats to the learning and teaching environment. The context of pandemic has delivered numerous advices, which are relevant in dealing with the pandemic situation, to the educational institution administrators, educators and other officials.

The response of an educational body addresses the needs as well as the concerns of learners and their parents. Educational body incorporates asynchronous learning methodologies that work pre-eminent in digital media, to enhance their ability to teach distantly. To make remote teaching and learning efficient, artificial intelligence (AI) approaches are incorporated into the traditional system of education.

Educational body have to encompass a diversified tools and system that places COVID-19 in a worldwide, in addition to the general disciplines of classroom. AI and other technological advancement has introduced numerous tools and applications for handling the pandemic situation.

This research discussed the impact of COVID and influence of AI on education and also the significance and applications of AI in education system in Saudi Arabia. In addition, this study examined the experience of Saudi’s students Universities with AI applications, (316) form the sample of this study to response it’s the Likert scale tool. The results of the study indicated that in the midst of the COVID-19 outbreak, the Government switched to online education, and positive responses were found from learners with taking all benefits of AI application. However, the lack of experience played a critical role in preventing full utilization of AI applications, which will motivate the decision maker to train the learner and the teacher to take advantage of these applications to face any pandemic in future.