As GARCH models and stable Paretian distributions have been revisited in the recent past with the papers of Hansen and Lunde (J Appl Econom 20: 873–889, 2005) and Bidarkota and McCulloch (Quant Finance 4: 256–265, 2004), respectively, in this paper we discuss alternative conditional distributional models for the daily returns of the US, German and Portuguese main stock market indexes, considering ARMA-GARCH models driven by Normal, Student’s t and stable Paretian distributed innovations. We find that a GARCH model with stable Paretian innovations fits returns clearly better than the more popular Normal distribution and slightly better than the Student’s t distribution. However, the Student’s t outperforms the Normal and stable Paretian distributions when the out-of-sample density forecasts are considered.

This paper describes a complete visual neuroprosthesis wireless system designed to restore useful visual sense to profoundly blind people. This visual neuroprosthesis performs intracortical microstimulation through one or more arrays of microelectrodes implanted into the primary visual cortex. The whole system is composed by a primary unit located outside the body and a secondary unit, implanted inside the body. The primary unit comprises a neuromorphic encoder, a forward transmitter, and a backward receiver. The developed neuromorphic encoder generates the spikes to stimulate the cortex by approximating the spatio-temporal receptive fields characteristic response of ganglion cells. Power and stimuli information are carried to inside the cranium by means of a low-coupling transformer, which establishes a wireless inductive link between the two units. The secondary unit comprises a forward receiver, microelectrode stimulation circuitry and a backward transmitter that is used to monitor the implant. Address event representation is used for communicating spike events. Data is modulated with binary frequency-shift keying and differential binary phase-shift keying in the forward and in the backward directions, respectively. A prototype of the proposed system was developed and tested. Experimental results show that the spikes to stimulate the visual cortex are accurately generated and that the efficiency of the inductive link is relatively high, about 28% in average for 1 cm intercoil distance providing a power of about 50 milliwatts to the secondary implanted unit. Application specific integrated circuits were designed for this secondary unit, showing that, with current technology, it is possible to implement such a unit, respecting the power constraints.

This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using Software Defined Radio (SDR) technology, targeting key UAV communication links, including geolocation, radio control, and video transmission. It applies jamming techniques that successfully disrupt UAV communications. GPS spoofing techniques were also implemented, with both static and dynamic spoofing tested to mislead the drones’ navigation systems. Dynamic spoofing, combined with no-fly zone enforcement, proved to be particularly effective in forcing drones to land or exhibit erratic behavior. The conclusions of this study highlight the effectiveness of these techniques in neutralizing unauthorized UAVs, while also identifying the need for future research in countering drones that operate on alternative frequencies, such as 4G/5G, to enhance the system’s robustness in evolving drone environments.

A new expression for the cumulative distribution function (CDF) of the Hoyt (Nakagami-q) distribution is derived. Its evaluation requires only a single numerical integration and its form is particularly well-suited for numerical computation by a Gauss-Chebyshev quadrature (GCQ). This quadrature is analysed and an upper bound on the absolute error is given. As example results to demonstrate the GCQ method's simplicity and efficiency, the computation of the Hoyt CDF and of the outage probability in Hoyt fading channels are presented.

In this work we propose an artificial neural network (ANN) for magnetic micro cytometry pattern recognition and automated counting. The method is tested for detecting analytes in the 2-3 μm range. The cytometer is composed of a disposable cartridge and an acquisition platform. The disposable cartridge contains microfluidic channels with a 10×100 μm ² cross section on top of a substrate with magnetoresistive (MR) sensors. The custom analog signal chain performs with an integrated noise of 2.99 μV _RMS in a 10 kHz bandwidth. To employ the ANN we synthesize a training data set based on the magnetic-dipole equation and several data set expansion methods. The ANN is tested on an experiment with 2.8 μm magnetic particles (MP) and compared with an improved threshold-based method with reduced false positive. The ANN produces a maximum of 90% detection rate, improving on the 30-50% detection rates of other single sensor methods published in literature.