For the equipment connected to the three-phase or single-phase grid, the power factor represents an efficiency measure for the usage of electrical energy. The power factor improvement through correction methods reduces the load on the transformers and power conductors, leading to a reduction of losses in the mains power supply and a sustainable grid system. The implications at the financial level are also important. An example of load that generates a small power factor is represented by a motor without mechanical load or having a small mechanical load. Given the power factor correction (PFC), the costs are reduced through the elimination of penalties, applying only in the common coupling point (CCP). The advantages of using equipment for the power factor correction are related also to their long operation duration and the easiness of their installation. The device presented in this article takes advantage of the advances in information and communication technology (ICT) to create a new approach for telemetry and remote configuration of a PFC. This approach has flexibility and versatility, such that it can be adapted to many loads, easily changing the capacitance steps and settings of the power factor correction device.

Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.

The new global pandemic determines people to change their ordinary habits, so personal transportation will play a very important role for a long period of time from now on. However, even though conventional vehicles offer enough range for replacing some trips that normally would be covered by planes, they have a harmful impact on the environment. By comparison, Electric Vehicles (EVs) suffer from this shortcoming (insufficient range), being more expensive, as well. Therefore, this article presents a very simple, unique and inexpensive methodology for designing the powertrain of a plug-in series-hybrid vehicle, based on measurements (regarding the engine torque requirements, but not limited to) performed on a compact passenger car, during real driving conditions, as well as based on mathematical calculations and computer-aided simulations (using MATLAB Simulink). The parameters of the proposed series-hybrid driving system which have been determined refer to the specifications of the Internal Combustion Engine (ICE) which is used to drive a generator, the rated output power of the electric generator, the required capabilities of the electric motor which is used to drive the wheels, the capacity of the high voltage battery pack (which supplies the electric motor), together with its other attributes, taking into consideration the vehicle’s overall performance and characteristics. Besides this, it has been developed a very simple algorithm that manages the power generated by the engine and which can reduce the fossil fuel consumption with almost 38%, on average, compared to a conventional drivetrain.

Sensor-Cloud Systems (SCS) are designed to link sensor networks and cloud applications, in order to gather and process information. While this is a hot topic for both academia and industry, with a large number of implementations, not much effort was put towards analysing the reliability of these systems. This article presents an experimental implementation of a system in the field of intelligent products and explores reliability improving techniques in five main areas of the SCS: network communication performance, auto recovery, local backup, automated software testing and system security. They all play an important role in determining the level of reliability of the novel Internet of Things (IoT) architecture framework for intelligent products presented in the article. A new formula is proposed for assessing the reliability of a SCS based on metrics from each of the five areas. Metrics used to assess the system reliability are presented, along with comparisons between operating with improvement techniques and without them. The results show that the reliability of the implemented SCS is improved considerably by implementing a deliberate reliability policy using an original five level tiered approach.

Batteries have become the main power source in today’s automotive systems. This paper proposes the design of a fast-balancing passive battery management system (BMS) with remote monitoring for the automotive domain. This system is designed for four series-connected lithium iron phosphate (LiFePO4) cells, which are the preferred choice in the automotive industry. The results show that the proposed BMS can monitor the cell voltages with an error lower than 0.12%, and it can perform the balancing operation successfully with maximum currents of 750 mA during both charging and discharging cycles, not only for LiFePO4 cells, but also for lithium-ion (Li-ion) cells. Furthermore, the cell voltages are sent over the controller area network (CAN) interface for remote monitoring.

The aim of this paper is to present a new original software platform, called LabConcept, that is designed for monitoring health activity in order to establish a more efficient treatment plan and provide a better lifestyle via telemedicine. It is fully designed by the authors and embeds all the patient’s healthcare information into a mobile software application, compatible with smartphones, laptops or other mobile devices. The novelty consists in standardizing the laboratory and the point-of-care results into a Quick Response (QR) code that is printed on every result bulletin issued and given to the patient after his routine blood assays are performed. Compared to the existing telemedicine software (VSee, Teladoc, TheraPlatform, Chiron Health), which have the basic videoconferencing role, LabConcept has a set of new important features that start from the concept of telemedicine, designed to load and create databases for all the assayed results in order to be sent to the doctor’s LabConcept application for professional interpretation. The software also analyzes the clinical evolution of an assay on a user-predefined period of time, automatically pre-diagnoses and alerts the user about a potential disorder from the stored results and records comments regarding the efficiency of the patient’s treatment plan. LabConcept is an easy and safe end-to-end real-time patient–doctor communication channel, conducive to improved monitoring and treatment procedures for the patient.