Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.

The dynamic performance of a microgrid is governed by the decentralized primary control strategy that is embedded in each of its hosted Distributed Energy Resource (DER) units. The primary control computes the voltage synthesized by the interfacing voltage-sourced converter such that the DER unit contributes active and reactive powers in support of the microgrid voltage amplitude and frequency. These operational requirements must be satisfied with respect to the converter safety and physical limitations, such as the limited magnitude of the converter output current and terminal voltage. In this paper, all the control requirements mentioned above are taken into account in a single constrained optimization problem using the framework of Model-based Predictive Control (MPC). Solving the primary control problem in this way allows the microgrid to respond to major disturbances such short-circuit faults and transitions between modes of operation, while the electronically-interfaced DER units operate within operational and safety limits.

Measurement While Drilling (MWD) is a technology for assessing rock mass conditions by collecting and analyzing data of mechanical drilling variables while the system operates. Nowadays, typical MWD systems rely on physical sensors directly installed on the drill rig. Sensors used in this context must be designed and conditioned for operating in harsh conditions, imposing trade-offs between the complexity, cost, and reliability of the measurement system. This paper presents a methodology for integrating physics-based observers into an MWD system as an alternative to complement or replace traditional physical sensors. The proposed observers leverage mathematical models of the drill’s electrical motor and its interaction with dynamic loads to estimate the bit speed and torque in a Down-the-Hole rig using current and voltage measurements taken from the motor power line. Experiments using data collected from four test samples with different rock strengths show a consistent correlation between the rate of penetration and specific energy derived from the observed drilling variables with the ones obtained from standardized tests of uniaxial compressive strength. The simplicity of the setup and results validate the feasibility of the proposed approach to be evaluated as an alternative to reduce the complexity and increase the reliability of MWD systems.

Extracting discriminative k-mers is an important and challenging problem in DNA sequence analysis with applications in metagenomics and motif discovery. Despite the availability of multiple computational tools designed for this purpose, most discriminative k-mer discovery methods suffer from long execution times and high memory usage when processing large datasets. This paper presents a novel approach for discriminative k-mer discovery in DNA sequences, which leverages streaming and sketch algorithms to reduce space complexity and expose data parallelism, enabling the use of parallel platforms for accelerating the execution of computationally-intensive operations. To assess the performance of our method, we designed and implemented two versions of the algorithm that leverage parallelization at different levels: (i) a software version tailored for multithreading and vector instructions in commodity CPUs, and (ii) a custom architecture implemented on a Field-Programmable Gate Array (FPGA) accelerator that exploits fine-grain parallelism and deep pipelining on reconfigurable logic. Experimental results show that, when mining discriminative k-mers from a set of well-known ChIP-seq sequences, our parallel software implementation executes at least 15% faster than exact-counting tools, and requires at least five times less memory when processing large datasets. More importantly, we designed a custom FPGA-based accelerator for our algorithm on a Xilinx KCU1500 board, which achieves speedups above 78x with the largest datasets, compared to our parallel software implementation. The accelerator uses less than 3% of the logic resources available on the on-board XCKU115 Kintex-7 Ultrascale FPGA, and between 12% and 70% of the memory resources, depending on the size of the dataset.