This paper address the implementation of a mixed-domain simulator for first-order band-pass sampling receivers, which is based on an initial frequency-domain signal treatment followed by a time-domain simulation scheme. One of the proposed applications for this type of receivers is to perform the spectrum sensing feature, which is required in actual and future cognitive radio approaches. Some details about the multi-stage modelling strategy will be given focusing in each specific component of the receiver, wherein it is considered a mixed frequency-time signal treatment. Moreover, it will be summarized the main features of the implemented simulator, as well as potential improvements. Finally, several simulation examples obtained with the implemented simulator will be shown, in which are included the impact of a CW signal excitation in a received modulated signal and a multi-carrier signal reception scenario.

The search for materials for hydrogen storage that are able to adsorb and desorb hydrogen molecules has received attention. Nanofibers have been studied because it has some interesting characteristics like high surface to volume ratio and the ease of producing by means of a low-cost setup. Polyacrylonitrile (PAN) has allowed the possibility of obtaining carbon nanofibers from the electrospinning process followed by a thermal treatment; moreover Pd has the capability of store hydrogen and can form a complex with dimethylformamide (DMF) so that, during the preparation of the solution of PAN and PdCl2, both present high solubility in the solvent.  Thus, the results demonstrated that as Pd can form complex with DMF the preparation method of the PAN+PdCl2 solutions influences the interaction between Pd and PAN. The decrease in dispersion viscosity and an unexpected increase in the diameter of the fibers confirm the hypothesis. The FTIRS and XPS analysis confirm that the Pd complexes with DMF by means of bonds to N.

In text classification based on the bag-of-words (BoW) or similar representations, we usually have a large number of features, many of which are irrelevant (or even detrimental) for classification tasks. Recent results show that compressed learning (CL), i.e., learning in a domain of reduced dimensionality obtained by random projections (RP), is possible, and theoretical bounds on the test set error rate have been shown. In this work, we assess the performance of CL, based on RP of BoW representations for text classification. Our experimental results show that CL significantly reduces the number of features and the training time, while simultaneously improving the classification accuracy. Rather than the mild decrease in accuracy upper bounded by the theory, we actually find an increase of accuracy. Our approach is further compared against two techniques, namely the unsupervised random subspaces method and the supervised Fisher index. The CL approach is suited for unsupervised or semi-supervised learning, without any modification, since it does not use the class labels.

Physical devices with different sensors and sampling rates distributed over several unrelated locations need to store their values over time. Applications that need the result of a set of sensors must access their data. A common and simple interface within the physical devices, monitoring stations, to store data on a database is needed; as also a simple and common retrieval interface for any application that only shows the data as it is or processes it into higher levels of significance. BlueLab IoT is a platform with libraries and an interface application to aid that development; a working implementation is provided.

This article reports on a monolithic 10 cm × 10 cm area PV module integrating an array of 72 a-Si:H n-i-p cells on a thin polyethylene-naphtalate substrate. The design optimization and device performance analysis are performed using a two-dimensional distributed circuit model of the photovoltaic cell. Experimental results show that the shunt leakage is one of the factors reducing the device performance. Using the LBIC technique, the multiple micro-shunts in the n‑i‑p cell were detected. The mechanism of electrical shunts formation is proposed and discussed.

In this work, an in-depth analysis concerning the transmission performance of IEEE802.11g/n (Wi-Fi) signals in a WDM-PON system is presented. It is considered that the optical/electrical transceivers are based on low-cost 850 nm VCSELs and PIN photodiodes. System modelling includes the impact of noise generated in the optical path, such as relative intensity noise (RIN), shot noise, photodetector thermal noise, clipping and intermodulation distortion. An analytic analysis based on Volterra series is conducted and mathematical expressions for both the EVM and SNR are derived. The theoretical analysis is also compared with experimental results. Among several conclusions, it is observed that the laser intermodulation distortion, clipping and RIN are the most relevant factors.

This paper presents the manufacturing of amicroelectrode array, on printed circuit boards (PCB) and onsilicon substrate, composed of 60 to 64 gold microelectrodes(between 4 µm and 70 µm each in diameter). The sensor has ameasurement total area of 0.5 cm in radius, one referenceelectrode (1 mm2 in area) and 28.4 mm wide and 28.4 mm long.These microelectrodes were used for checking and logging ofextracelullar local field potential of cell culture and nitritemeasurement in a phosphate buffered saline solution(electrolytical aqueous medium). In addition, an apparatus toshield from electromagnetic interference for connecting thearrays was designed to allow the capture of electrochemicalreactions or electronic signals by the microelectrodes, forexample: nitrite or cardiac potential measurement, respectively.Finally, biocompatibility tests of the array structures wereperformed. The preliminary electrical and biocompatibilitytesting, along with the collected data, has shown promisingresults pointing to the development of an accurate sensor afterthe completion of this study. The sensor has potentially abroader range of applications with only a few adaptations anddue its good accuracy it can be a very useful resource for manychemical and biological applications.

This paper analyses the Alamouti scheme for different antenna configurations and different modulation types,namely BPSK, QPSK and QAM. All configurations were modeled and simulated in MATLAB. A MIMOreceiver for a 21 antenna configuration and BPSK modulation was implemented in a FPGA. The FPGAresults indicate that the Alamouti scheme is a good design option for hardware implementation of a MIMOreceiver. The receiver uses only about 10% of the resources of a medium-sized FPGA and achieves almost300 Msymbols per second.

The emerging concept of smart cities demands for a large number of electronic devices, like sensors and actuators, distributed over several public spaces and buildings. The Internet of Things (IoT) has a key role in connecting devices to the Internet. However, the significant number of devices makes the maintenance task of the entire network difficult and expensive. To mitigate this problem, considerable research efforts have been made to develop energy-aware devices capable of self-sustainable operation, by harvesting their energy from various sources. In this paper, we study the possibility of harvesting energy from the light flowing in the Gigabit Passive Optics Network (GPON) to supply low-power devices. Since most cities already have a working GPON installation, using this installation to interconnect and power IoT devices can be a viable and less expensive solution, instead of installing new dedicated networks. This is also an interesting solution to convey communications and energy to low-power applications where access to the power grid is unfeasible. This study is focused in the 1550 nm wavelength, whose available optical power, in residential premises, is between -7 dBm and +2 dBm. With this range of optical power, and with a 30% efficiency photodiode, we show, for the worst-case scenario of the GPON, how it is possible to harvest 62 µW of energy at the Maximum Power Point (MPP).

This paper presents a new single cell multi-objectiveoptimization algorithm. The objective is to optimize areas oflow coverage and high interference simultaneously, through theadjustment of the antenna tilts and/or antenna orientation. Theprocess is achieved using a specific implementation of a ParticleSwarm Optimization (PSO) algorithm. Both the detection ofsub-optimal performance areas and its subsequent optimizationare supported by Drive Test (DT) data and network topologyinformation. The antenna optimization algorithm was tested withreal data in 3rd Generation (3G)/4th Generation (4G) networks.In this work, a 3G urban scenario is highlighted, achieving anaverage optimization gain of 78%.