This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.

In this paper, we propose a novel microstrip-ridge gap waveguide (MS-RGW) filter configuration, which is based on cavity resonators with mushroom inclusions. The resonators are realized as defects in surrounding mushroom-based perfect magnetic conductor (PMC), and thus, the filter configuration does not require additional conductive layers nor rearrangement of the PMC elements. The hosting MS-RGW is fed through transition from SMA to microstrip ridge, enabling simple fabrication and excellent impedance matching in a wide frequency range. To demonstrate the potential of the proposed structure, four narrowband filters have been designed, fabricated, and measured. The filters exhibit excellent in-band characteristics and small dimensions.

Due to the global water crisis there is a strong need for real-time water quality monitoring with high temporal and spatial resolution. This paper presents an economical multiparameter water quality monitoring system for continuous monitoring of fresh waters. It is based on a sensor node that integrates turbidity, temperature, and conductivity sensors, a miniature eighteen-channel spectrophotometer, and a sensor for the detection of thermotolerant coliforms, which is a major novelty of the system. Due to the influence of water impurities on the measurement of thermotolerant coliforms, a heuristic method has been developed to mitigate this effect. Moreover, the sensor is low power and with an integrated Long Range Wide Area Network module, it comprises a system that is wireless sensor network ready and can send data to a dedicated server. In addition, the system is submersible, capable of long-term field operation, and significantly cheaper in comparison to existing solutions. The purpose of the system is to give early warning of incidental pollution situations, thus enabling authorities to take action regarding further prevention of such occasions.

The connection of macrosystems with microsystems for in-line measurements is important in different biotechnological processes as it enables precise and accurate monitoring of process parameters at a small scale, which can provide valuable insights into the process, and ultimately lead to improved process control and optimization. Additionally, it allows continuous monitoring without the need for manual sampling and analysis, leading to more efficient and cost-effective production. In this paper, a 3D printed microfluidic (MF) chip for glucose (Glc) sensing in a liquid analyte is proposed. The chip made in Poly(methyl methacrylate) (PMMA) contains integrated serpentine-based micromixers realized via stereolithography with a slot for USB-like integration of commercial DropSens electrodes. After adjusting the sample’s pH in the first micromixer, small volumes of the sample and enzyme are mixed in the second micromixer and lead to a sensing chamber where the Glc concentration is measured via chronoamperometry. The sensing potential was examined for Glc concentrations in acetate buffer in the range of 0.1–100 mg/mL and afterward tested for Glc sensing in a cell culturing medium. The proposed chip showed great potential for connection with macrosystems, such as bioreactors, for direct in-line monitoring of a quality parameter in a liquid sample.