The majority of IoT nodes work within specific scenarios and can be configured in different ways. This paper seeks to design and implement a low-cost Internet of Things node for research applications to make it suitable for a wider variety of scenarios. The design is divided into hardware board and mobile application. With Bluetooth, the mobile application can connect to the node, and the node can collect data, store this data in the ThingSpeak database, control some connected devices, and check if the connected devices are on or off. The node was designed and tested for research purposes using smart agriculture as the case study. The system detects temperature, humidity, and soil moisture using node sensors, enabling data collection and interpretation by smartphone and web application. There are many challenges associated with the collected data preparation, analysis, visualization, and prediction using the Softmax function for optimal future management. Python was utilized to apply necessary data analysis techniques. The system saves time and makes farming more convenient as it uses few resources in terms of hardware and cost.

One of the elementary operations in computing systems is multiplication. Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems. In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity. This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost. The Vedic multiplier is considered fast as it generates all partial product and their sum in one step. This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output. First, the unsigned Vedic multiplier is designed based on the Urdhava Tiryakbhyam (UT) Sutra. This multiplier consists of bitwise multiplication and adder compressors. Compared with Vedic multipliers in the literature, the proposed design has a quantum cost of 111 with a reduction of 94% compared to the previous design. It has a garbage output of 30 with optimization of the best-compared design. Second, the proposed unsigned multiplier is expanded to allow the multiplication of signed numbers as well as unsigned numbers. Two signed Vedic multipliers are presented with the aim of obtaining more optimization in performance parameters. DesignI has separate binary two's complement (B2C) and MUX circuits, while DesignII combines binary two's complement and MUX circuits in one circuit. DesignI shows the lowest quantum cost, 231, regarding state-ofthe-art. DesignII has a quantum cost of 199, reducing to 86.14% of DesignI. The functionality of the proposed multiplier is simulated and verified using XILINX ISE 14.2.

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Optimum usage of the existing frequency spectrum is a major requirement due to the large increase in the number of subscribers at the same frequency. The cognitive radio scheme has become an important application used to optimize spectrum utilization, detects spectrum bands that are not occupied by primary users to create communication links between secondary users in the same band. These non-occupied bands are described as white space. In this paper, a spectrum sensing method that is an important stage in cognitive radio communications is discussed. Various spectrum sensing methods such as energy detection (ED) and cyclostationary feature detection (CFD) are used to sense the spectrum in the FM broadcasting band. A comparison of energy detection and cyclostationary feature detection spectrum sensing methods is made to determine the empty bands in the FM broadcasting band. This enables secondary users to transfer information in these empty bands without affecting the primary users of the FM broadcasting system.

Estimating the number of mispredictions is critically important for estimating the Worst-Case Execution Time for real-time systems. This paper generalizes and improves over previous attempts to provide a safe and tight mispredication count estimate for dynamic branch predictors. The paper gives closed formulas to compute mispredictions in case of simple and nested loops applicable to all variations of two-level adaptive branch predictors in addition to the gshare and gselect predictors. The given formulas are general enough to accommodate predictors with any counter size.