Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.

The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging.

Species living in extremely cold environments resist the freezing conditions through antifreeze proteins (AFPs). Apart from being essential proteins for various organisms living in sub-zero temperatures, AFPs have numerous applications in different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST and PSI-BLAST. Diverse AFPs found in fishes (Type I, II, III, IV and antifreeze glycoproteins (AFGPs)), are sub-types and show low sequence and structural similarity, making their accurate prediction challenging. Although several machine-learning methods have been proposed for the classification of AFPs, prediction methods that have greater reliability are required. In this paper, we propose a novel machine-learning-based approach for the prediction of AFP sequences using latent space learning through a deep auto-encoder method. For latent space pruning, we use the output of the auto-encoder with a deep neural network classifier to learn the non-linear mapping of the protein sequence descriptor and class label. The proposed method outperformed the existing methods, yielding excellent results in comparison. A comprehensive ablation study is performed, and the proposed method is evaluated in terms of widely used performance measures. In particular, the proposed method demonstrated a high Matthews correlation coefficient of 0.52, F-score of 0.49, and Youden’s index of 0.81 on an independent test dataset, thereby outperforming the existing methods for AFP prediction.

In recent years, approximate computing (AC) has attracted attention owing to its tradeoff between the exactness of computations and performance gains [1, 2]. AC has also been probed for the technique of Triple modular redundancy (TMR). TMR is a well-known fault masking methodology, with associated overheads, widely used in systems of different nature and at different levels. E.g.: layout-level, gate-level, HW-module level, software [3-5]. At hardware level, through exploitation of AC the 200% area overhead problem due to triplication of the original modules in TMR can be reduced. By approximating the modules of TMR while ensuring that at least two of the approximate modules do not differ from the original module for every input vector, the facilitation of fault masking can lead to overhead reduction. Hence, approximate TMR (ATMR) aims to achieve cost-effective reliability. Nevertheless, due to the extensive search space, computational complexity, and principal fault masking function of ATMR, designing an ATMR is a challenging task. An ATMR technique must be scalable so that it can be easily adopted by circuits having large number of inputs and the extraction of ATMR modules remains computationally inexpensive. Compared with TMR, due to the inclusion of approximations, ATMR is more vulnerable to errors, and hence, the design technique must ensure awareness of input-criticality. To the best of the authors’ knowledge, none of the existing survey articles on AC has reported on ATMR. Therefore, in this work, ATMR design techniques are thoroughly surveyed and qualitatively compared. Moreover, design considerations and challenges for designing ATMR are discussed.

Approximate Triple Modular Redundancy has been proposed in the literature to overcome the area overhead issue of Triple Modular Redundancy (TMR). The outcome of TMR/Approximate TMR modules serves as the voter input to produce the final output of a system. Because the working principle of Approximate TMR conditionally allows one of the approximate modules to differ from the original circuit, it is critical for Approximate TMR that a voter not only be tolerant toward its internal faults but also toward faults that occur at the voter inputs. Herein, we present a novel compact voter for Approximate TMR using pass transistors and quadded transistor level redundancy to achieve a higher fault masking. The design also targets a better Quality of Circuit (QoC), a new metric which we have proposed for highlighting the ability of a circuit to fully mask all possible internal faults for an input vector. Comparing the fault masking features with those of existing works, the proposed voter delivered upto 45.1%, 62.5%, 26.6% improvement in Fault Masking Ratio (FMR), QoC, and reliability, respectively. With respect to the electrical characteristics, our proposed voter can achieve an improvement of up to 50% and 56% in terms of the transistor count and power delay product, respectively.