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Background: The need for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG (SMBG) have driven the development of non-invasive approaches. Methods: This prospective study aimed to investigate the accuracy of glucose level calculation using the near-infrared spectroscopy (NIRS) technology Glucube® system. People with Type 1 diabetes, Type 2 diabetes, prediabetes, and normal glucose metabolism were included. Over one week, individuals performed glucose measurements with the Glucube® system and capillary blood fingersticks with a standard glucometer (Ascensia Contour® Next). To assess the impact of the improvement in dexterity, the accuracy variables were compared with the point-of-care (POC) glucometer Accu-Chek® Inform II in a one-week sub-study. Results: Overall, 105 subjects (mean age 53.8 ± 13.8 years, 50.5% female) participated, resulting in 1914 paired glucose measurements between 49 and 331 mg/dL. Total mean absolute relative difference (MARD) was 20.3%, MARD for values >100 mg/dL was 18.3%, and mean absolute deviation (MAD) for values p = 0.068). The percentages within Zone A were 51.6 vs. 61.2%, Zone B 46.8 vs. 33.9%, and Zone C 1.6 vs. 4.8%, and the sum of Parkes Zones A+B was 98.4 vs. 95.2% (p = 0.311) for day 1 and day 8, respectively. Conclusions: Glucube® is a novel non-invasive system based on NIRS technology for monitoring blood glucose levels. Its promising capabilities support further research.

Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human–computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain–computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software.

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

A new digital countermeasure against attacks related to the clock frequency is presented. This countermeasure, known as frequency sensor, consists of a local oscillator, a transition detector, a measurement element and an output block. The countermeasure has been designed using a full-custom technique implemented in an Application-Specific Integrated Circuit (ASIC), and the implementation has been verified and characterized with an integrated design using a 0.35 mm standard Complementary Metal Oxide Semiconductor (CMOS) technology (Very Large Scale Implementation—VLSI implementation). The proposed solution is configurable in resolution time and allowed range of period, achieving a minimum resolution time of only 1.91 ns and an initialization time of 5.84 ns. The proposed VLSI implementation shows better results than other solutions, such as digital ones based on semi-custom techniques and analog ones based on band pass filters, all design parameters considered. Finally, a counter has been used to verify the good performance of the countermeasure in avoiding the success of an attack.

This paper presents the design of a wireless flood sensor to detect the presence of water on home floors, providing early warning of water leaks. A wireless sensor network has been deployed to gather the measurements from the sensor nodes. A control central coordinates the network and processes the data. Users can remotely inquire for the presence of water, status of the batteries for a specific node, the type of liquid and information about its functionality and alarms, thanks to a proprietary software application. The alerts are also communicated to the user within the home through an audible siren. The designed device is optimized in terms of costs, ease of deployment and maintenance, thus making it widely acceptable to end users.

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.

This paper presents the design of a wireless sensor network particularly designed for remote monitoring and control of industrial parameters. The article describes the network components, protocol and sensor deployment, aimed to accomplish industrial constraint and to assure reliability and low power consumption. A particular case of study is presented. The system consists of a base station, gas sensing nodes, a tree-based routing scheme for the wireless sensor nodes and a real-time monitoring application that operates from a remote computer and a mobile phone. The system assures that the industrial safety quality and the measurement and monitoring system achieves an efficient industrial monitoring operations. The robustness of the developed system and the security in the communications have been guaranteed both in hardware and software level. The system is flexible and can be adapted to different environments. The testing of the system confirms the feasibility of the proposed implementation and validates the functional requirements of the developed devices, the networking solution and the power consumption management.

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.