Land use land cover (LULC) has altered dramatically because of anthropogenic activities, particularly in places where climate change and population growth are severe. The geographic information system (GIS) and remote sensing are widely used techniques for monitoring LULC changes. This study aimed to assess the LULC changes and predict future trends in Selangor, Malaysia. The satellite images from 1991–2021 were classified to develop LULC maps using support vector machine (SVM) classification in ArcGIS. The image classification was based on six different LULC classes, i.e., (i) water, (ii) developed, (iii) barren, (iv) forest, (v) agriculture, and (vi) wetlands. The resulting LULC maps illustrated the area changes from 1991 to 2021 in different classes, where developed, barren, and water lands increased by 15.54%, 1.95%, and 0.53%, respectively. However, agricultural, forest, and wetlands decreased by 3.07%, 14.01%, and 0.94%, respectively. The cellular automata-artificial neural network (CA-ANN) technique was used to predict the LULC changes from 2031–2051. The percentage of correctness for the simulation was 82.43%, and overall kappa value was 0.72. The prediction maps from 2031–2051 illustrated decreasing trends in (i) agricultural by 3.73%, (ii) forest by 1.09%, (iii) barren by 0.21%, (iv) wetlands by 0.06%, and (v) water by 0.04% and increasing trends in (vi) developed by 5.12%. The outcomes of this study provide crucial knowledge that may help in developing future sustainable planning and management, as well as assist authorities in making informed decisions to improve environmental and ecological conditions.

Advances in wireless communication are forging new possibilities for sensors. New sensors are equipping major systems around us with unparalleled intelligence as in the case of smart grids, smart homes, and driverless vehicles. Considering the current developments in the field of sensor networks, one feels that it has reached an interesting stage, where the role of the sensors becoming crucial in numerous applications. This all speaks volumes of the fact that sensors are going to be at the front and center of most of future technologies, needless to say the Internet of Things. Considering their vital role from futuristic perspective this survey reports variety of sensors along with their characteristics and applications, which impact human life and well-being. In addition, this survey considers recent prototypes, leading sensor manufacturers as well as major projects that have made use of sensors since the last decade. Moreover, significance of this effort is that integration possibilities of sensors with other networks and major technologies are discussed, while possible challenges and key benefits are highlighted. This research effort focuses the latest developments in the area of sensors and sensor networks as research gears up to meet the challenges of the emerging technologies and their applications particularly those that emphasize smart sensors.

Governments rely on collected data to analyze their citizens’ health requirements and needs. Such data is usually of scattered manner gathered by several agencies and entities that fall under different business models and goals. Hence, the scattered nature of this data creates a burden on governments in precisely planning adequate health bills that guarantee the wellbeing of their citizens. Furthermore, the issue of synchronizing and having a real-time access to such data exacerbates the problem even more. Therefore, a system that guarantees the integrity, accessibility, correctness and security of medical data in a highly synchronized environment is crucial for governments to accurately project their future needs. In this paper, we present Smart-Health (SHealth), a blockchain-based health management system. In its crux, SHealth is a private multi-layered blockchain integrated with a multi-tiered addressing scheme that defines the privileges and permissions of entities in the system. Blockchain-based systems assure security, reliability, availability, resistance against tampering and malicious attacks, seamless integration, and easy data management. SHealth proposes a complete autonomy to its users. Through a user-friendly graphical interface it capitalizes on smart contracts to initiate various requests and inquiries pertaining to patients such as appointments, medical tests, medications, medical procedures, or history. SHealth is simple, robust, efficient, secured, and completely automated. All the stakeholders in the system can access the health related data stored in a distributed database without compromising its authenticity. SHealth covers all the possible scenarios in health systems from which some of them are explained in this work.

High Peak to Average Power Ratio (PAPR) is one of the major drawbacks in Orthogonal Frequency Division Multiplexing (OFDM). The high PAPR increases the complexity of Analogue to Digital (A/D) and Digital to Analogue (D/A) converters and also reduces the efficiency of RF High Power Amplifier (HPA). In this paper, we propose two novel PAPR reduction techniques for OFDM systems, Zadoff-Chu matrix Transform (ZCT) precoding based PAPR reduction technique and ZCT postcoding based PAPR reduction technique. Simulation results show that, at clip rate of 10^-1, the PAPR of our both proposed systems reduced to 0dB, 3dB, 4.1dB and 4.3dB for M-QAM (where M = 4, 16, 64, 256). Both ZCT precoding and postcoding based OFDM systems are shown better PAPR gain and Bit Error Rate (BER) performance than Walsh Hadamard Transform precoded OFDM (WHT-OFDM) and OFDM conventional.

High Peak to Average Power Ratio (PAPR) is still a most important challenge in Orthogonal Frequency Division Multiplexing (OFDM) system. In this paper, we propose a Discrete Cosine Transform (DCT) precoding based SLM technique for PAPR reduction in OFDM systems. This technique is based on precoding the constellation symbols with DCT precoder after the multiplication of phase rotation factor and before the Inverse Fast Fourier Transform (IFFT) in SLM-OFDM System. Simulation results show that our proposed technique can reduce the PAPR to about 5.5dB for N=64 and V=16 at clipping probability of 10^-3.