The latest advances in the different Industry 4.0 technologies have enabled the automation and optimization of complex tasks of production processes thanks to their ability to monitor and track the state of physical elements like machinery, environmental sensors/actuators or industrial operators. This paper focuses on the latter and presents the design and evaluation of a system for monitoring industrial workers that provides a near real-time decentralized response system aimed at reacting and tracing events that affect operator personal safety and health. Such a monitoring system is based on the information collected from sensors encapsulated in IoT wearables that are used to measure both personal and environmental data. The communications architecture relies on LoRaWAN, an LPWAN (Low-Power Wide-Area Network) technology that offers good reliability in harsh communications environments and that provides relatively long-distance communications with low-energy consumption.

Specifically, each wearable sends the collected information (e.g., heart rate, height, external temperature, gas concentration, location) from the sensors to the nearest LoRaWAN gateway, which is to a pool of nodes where information is stored in a distributed manner. Such a decentralized system allows for providing information redundancy and guarantees its availability as long as there is an operative node. In addition, the proposed system is able to store and to process the collected data through smart contracts in a blockchain, which eliminate the need for a central backend and ensure the traceability and immutability of such data in order to share them with third parties (e.g., insurance companies or medical services).

Public places like hospitals, and industries are required to maintain standards of hygiene and cleanliness. Traditionally, the cleaning task has been performed by people. However, due to various factors like shortage of workers, unavailability of 24-hour service, or health concerns of working with toxic chemicals used in cleaning, autonomous robots have been looked upon as alternatives. In recent years, cleaning robots like Roomba have gained popularity. These cleaning robots have limited battery power, and therefore efficient cleaning is important. Efforts are being undertaken to improve the efficiency of cleaning robots.

The most rudimentary type of cleaning robot is the one with bump sensors and encoders, which simply keep cleaning the room until the battery is available. Some researchers have tried to attach sensors like Lidar and cameras on the robot, and use sensory information for intelligent cleaning. Some approaches first build a map of the environment, and then plan systematic paths to clean the floor. Other approaches uses dirt sensors attached to the robot to clean only the untidy portions of the floor. Researchers have also proposed to attach cameras on the robot to detect dirt and then clean. However, a critical limitation of all the previous works is that robots cannot know if the floor is clean or not unless they actually visit that place. Hence, a timely information is not available if the room should be cleaned or not, which is a major limitation to achieve efficiency.

To overcome such limitations, we propose a novel approach that uses external cameras, which can communicate with the robots. The external cameras are fixed in the room, and detect if the floor is untidy or not through image processing. The external camera detects if the floor is untidy, along with the exact areas, and coordinates of the portions of the floor, which must be cleaned. This information is communicated to the cleaning robot through wireless network. The robot then intelligently plans the shortest path through the untidy areas minimizing battery usage. The camera node and robot work in a master-slave architecture, in which camera is the master instructing the robot about areas to clean. The camera node comprises of a Raspberry-Pi embedded computer, and robot is programmed on ROS (Robot Operating System). The ROS Master acts as a name-service in the ROS computation graph storing topics and services registration information for ROS master and slave nodes. The communication protocol is TCPROS, which uses standard TCP/IP sockets. Unlike previous works that use on-board robot sensors, the novel contribution of the proposed work lies in using external cameras, and intelligent communication between the camera node and robot.

The proposed method enables cleaning robots to have an access to a ‘birds-eye view’ of the environment for efficient cleaning. We demonstrate how normal web-cameras can be used for dirt detection. The dirt detection algorithm uses a combination of ‘sum of absolute differences’ and histogram algorithms in RGB and HSV domains. We test the algorithm with different types, sizes, and colors of dirt. The proposed cleaning algorithm is targeted for home, factories, hospitals, airports, universities, and other public places. The scope of our current work is limited to indoor environments; however, an extension to external environments is straightforward. In this paper, we demonstrate the current algorithm through actual sensors in real-world scenarios.

Currently, the mineral industry makes iron ore beneficiation processes in humid or natural moisture. Excessive moisture in iron ore can affect the beneficiation process, causing loss of productivity and transport issues, as well as reducing the efficiency of dewatering subprocesses and safety. The traditional technic for measuring iron ore moisture is the standard oven method, which is very accurate, but not very representative. Furthermore, it has a high time response: up to 24 hours for each mineral sample. Consequently, corrective and preventive actions to the process become inefficient. Alternative technics, as microwave method, performs online moisture measurements, but with low accuracy. Recently, we developed a high accuracy capacitive sensor for measuring ore moisture, but not online (bench device). This paper refers to the development of a capacitive, non-invasive, coplanar-electrode transducer for iron ore moisture measurement, designed for online applications. To achieve this, we constructed a signal conditioning system, based on an 8-bit microcontroller, and a driven shield for the sensor element. The system transmits the processed data via radio frequency to a computer. Also, it applies a statistical filter to the measurements, based on standard deviation and moving average, as a way for minimizing electromagnetic interference. The statical calibration results reached a coefficient of determination of 98.41%. The coplanar, non-invasive approach of the transducer offers the advantage of preserving the physical integrity of the sensor electrodes as well as a future online application.

Robotic mapping and exploration is basic to many robotic applications such as search and rescue operations in disaster scenarios. Multi-robot systems can speed up exploration tasks in such critical situations by making use of distributed sensors to increase the range of exploration and mapping. In this case, every robot explores and maps different areas that are finally merged and connected. To build a map of an unknown environment, a robot must perform SLAM or Simultaneous Localization and Mapping and based on the perceived data metric maps such as feature maps or occupancy grid maps are constructed. The maps are then used for localizing the robots and path planning. Although metric maps allow precise robot localization and estimation, they suffer from high memory requirements needed to store all the information in occupied cells. Moreover, merging maps from other robots is an intensive process. On the other hand, topological features map representation can be used to store information into nodes and edges and does not have any large memory requirements. In this paper, we present a combined metric-topological mapping approach to multi-robot SLAM. This method maintains a graph with sensor information stored in nodes and edges that can be optimized globally. By combining local metric and topological maps build by individual robots, the graph structure can be merged and extended to map large areas effectively. To robustly merge local maps into a global one, we used visual features from each robot that are matched in a distributed system. The graph node-edge structure is used for path planning and information sharing between robots resulting in optimized task distribution.

Monitoring of the marine environment results in large amounts of data that must be processed and transmitted effectively for efficient resource management. In particular, given its high sampling rate, underwater noise signal acquisition technologies deserve special attention. In this article, a comparative study of the efficiency of different information processing and compression techniques is carried out, depending on the characteristics that want to be transmitted from the original signal. The applications and experiments carried out are focused on responding to the Marine Strategies, a marine environment planning instrument created under Directive 2008/56 / EC, of June 17, 2008, and more specifically to the D1 of the MSFD as regards at noise levels (both continuous and impulsive), as well as part of the D11 focused on the detection and abundance of cetaceans.

An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by the inertial navigation system to calculate the navigation solution. Since the sensor measurements contain noise, the navigation solution drifts over time.

When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results of this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using our MIMU hardware and software tool we examined the possibility of using MIMUs instead of a single IMU for: 1) navigation solution 2) outlier rejection 3) calibration performance 4) coarse alignment accuracy and 5) the effect of different MIMUs locations in the architecture.

Our results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.

The KM3NeT deep-sea neutrino telescope will use thousands of Digital Optical Modules (DOMs) forming a 3D array to detect the Cherenkov’s light produced by the particles generated after a neutrino interaction in the medium. The DOMs are arranged in Detection Units (DUs), structures anchored and maintained vertical by buoyancy each one containing 18 DOMs at different height. The DOMs are, thus, subject to movements due to sea currents. For a correct reconstruction of events detected by the telescope, it is necessary to monitor the position of each DOM with ~10 cm accuracy. For this, an Acoustic Positioning System (APS) with a piezo-ceramic transducer installed in each DOM and a long baseline of acoustic transmitters and receivers on the seabed is used. Besides, there is a system of compass/accelerometers in the DOMs to determine their orientation. Then, a mechanical model is used to reconstruct the shape of the DU taking as input the information from the positioning sensors and using the sea current velocity as free parameter of the DU Line Fit method. The mechanical equations consider the buoyancy and the drag force of any item in the DU line. This work describes the data process of the different sensors and systems to obtain the fit shape of DUs, the situation for the first DUs installed as an example and to study the viability and define the full process to apply in KM3NeT.

This work presents the design of a portable device for the recording and transmission of an ECG signal using the AD8232 microchip as the analog front end (AFE). Starting with the manufacturer’s evaluation board for this chip, then a SMD-to-DIP adapter with breadboard‐friendly Arduino microcontroller and breakout boards, until reaching a custom PCB. The analog ECG signal from the AFE output was digitized using one channel of the 10-bit ADC of the ATmega328 microcontroller contained on the Arduino Nano board 3.0. Besides this hardwired development, a simulation was also used in the analog circuit design with the SPICE Multisim software and related macromodel library to investigate system stability. The digitized ECG signal can be transmitted by serial cable, and recorded with a microSD card adapter along with the date and time stamp data of the sample capture (real-time clock provided); for wireless transmission the shield HC-06 was used. Transmission via bluetooth to a PC us-ing LABVIEW software or to a smartphone with Android system can be achieved. Besides the hardware filters in the AFE stage, digital filtering by means of simple difference equations was included as an option. A menu was incorporated to choose from the several modes of operation of the device. The ECG test signals were obtained from a patient simulator (SimCube) and real pa-tients. The result obtained from this work was a portable ECG system prototype with serial transmission, SD recording, and bluetooth transmission, for monitor-ing applications, and complies with electrical safety regulations and medical equipment design.

The recent advances in sensor technologies coupled with the development of machine/deep learning strategies is opening new frontiers in Structural Health Monitoring (SHM). Dealing with the structural vibrations recorded by pervasive sensor networks, the goal of SHM is to extract meaningful damage-sensitive features from the data, shaped as multivariate time series, and to make in real-time decisions connected to the safety level of the structures. Within this context, we discuss an approach able to detect and localize a structural damage avoiding any pre-processing of the acquired data. The method takes advantage of the capability of Deep Learning of Fully Convolutional Neural Networks, trained during an initial offline phase. A hybrid model- and data-based solution is looked for: for the former aspect, Reduced Order Models (ROMs) of the structure are built in the aforementioned initial offline phase of SHM, to strongly reduce the computational burden of the subsequent online monitoring phase. Through some numerical benchmarks, we show how the proposed method can recognize and localize damage, even when data are corrupted by measurement noise and environmental variability.

The use of fossil fuels will not be able to meet the humanity's growing long-term energy demand. In this scenario, the use of hydrogen as an intermediate energy source has become an interesting alternative as a method of energy production. In addition, the use of fossil fuels can lead to harmful consequences, such as the emission of greenhouse gases. This paper presents the development of a low-cost instrumentation system for monitoring the temperature, current, voltage and gas flowrate of a dry electrolytic cell. Through the electrolysis process, the cell generates a hydrogen-rich gas, which is used as an additive in internal combustion engines to reduce pollutant gas emissions and primary fuel consumption. The measured variables are presented as graphs as a function of time to analyze the behavior of the electrolyzer. The main advance reported in this work is related to the use of a low-cost sensor for a hydrogen-rich gas flow measurement, which calibration was performed indirectly using a rotameter as a reference. The calibration curve adjusted to the experimental data by linear regression presented a coefficient of determination of 0.9241. Thus, the use of the low-cost sensor is a viable alternative for measuring the electrolysis gas generated by the cell.