One of the symptoms of Parkinson’s disease is the occurrence of problems with the expression of emotions on the face, called facial masking, facial bradykinesia or hypomimia. Recent medical studies show that this symptom can be used in the diagnosis of this disease. In the presented study, the authors, on the basis of their own research, try to answer the question of whether it is possible to build an automatic Parkinson’s disease recognition system based on the face image. The research used image recordings in the field of visible light and infrared. The material for the study consisted of registrations in a group of patients with Parkinson’s disease and a group of healthy patients. The patients were asked to express a neutral facial expression and a smile. In the detection, both geometric and holistic methods based on the use of convolutional network and image fusion were used. The obtained results were assessed quantitatively using statistical measures, including $F_{1}score$, which was a value of 0.941. The results were compared with a competitive work on the same subject. A novelty of our experiments is that patients with Parkinson’s disease were in the so-called ON phase, in which, due to the action of drugs, the symptoms of the disease are reduced. The results obtained seem to be useful in the process of early diagnosis of this disease, especially in times of remote medical examination.

One of the symptoms of Parkinson’s disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson’s disease, aimed at supporting medical diagnosis using machine learning methods. Several approaches have been used and presented in the literature that discuss the analysis and understanding of images created during the writing of single words or sentences. In this study, we propose an analysis based on a sequence of sentences, which allows us to assess the evolution of writing over time. The study material consisted of handwriting image samples acquired in a group of 24 patients with PD and 24 healthy controls. The parameterization of the handwriting image samples was carried out using domain knowledge. Using the exhaustive search method, we selected the relevant features for the SVM algorithm performing binary classification. The results obtained were assessed using quality measures, including overall accuracy, which was 91.67%. The results were compared with competitive works on the same subject and seem to be better (a higher level of accuracy with a much smaller number of features than those presented by others).

This article investigates the issue of measuring high-power microwave (HPM) pulses. The high energy of these pulses poses a significant threat to many electronic systems, including those used to manage critical infrastructure. This work focuses on requirements for a potential portable measurement device and suggests the application of a method for this purpose, involving the use of a D-dot sensor and a rapid A/D converter. The applied converter enables recording the time waveform on the measuring chain output, also in the case of repetition and time duration of HPM signals. The authors also present a quantitative description of signal processing by the analogue section of the measurement chain solution presented herein and suggest algorithms for digital processing of the signals, the objective of which is to minimize low-frequency interference in the process of reconstructing the time waveform of an electric field using numerical integration.

Techniques for automatic modulation classification (AMC) of radar signals are crucial for spectrum sensing, passive bistatic radars and electronic intelligence (ELINT). Most of existing AMC algorithms were evaluated using solely synthetic data. Meanwhile, signals intercepted in real-life scenarios are extra distorted due to the multipath propagation. This paper presents the novel, pattern recognition AMC method for frequency modulated radar signals, with improved resistance to noise and multipath. This has been achieved by a multi-stage feature selection process, involving over a dozen of popular radar signal metrics. Based on the feature selection results, the two advanced waveform features were utilized in the designed algorithm, i.e. quasi-maximum likelihood (QML) instantaneous frequency estimate and fractional Fourier transform (FrFT) profile. The proposed AMC method has been evaluated using both synthetic and real data. Obtained results show that proposed classification framework achieves overall accuracy of 93.6% for a set of real-life signals acquisitions, corrupted both by noise and multipath influence.

The main goal of this column was to describe how to optimize the excitation coil. In this column, the authors describe how to optimize the measurement algorithm. We explain the necessary quantitative power that the flowmeter coil has to receive to be able to realize a given measurement uncertainty with respect to analog and digital processing of the flow signals.