A Sensors System for Indoor Localisation of a Moving Target Based on Infrared Pattern Recognition

Estimating the position of a robot, a vehicle or a person in an indoor area is a significan

The Front End Design of a Health Monitoring System

Abstract. In this paper an efficient e-health platform based on a low-cost sensor controller system is presented, exhibiting enhanced key characteristics able to provide broad coverage of medical scenarios in a reliable and flexible way. The heart of the system is a low-cost sensor controller capable of performing both simple medical tests and more advanced ones communicating with a Gateway and a tablet or smart phone providing instructions to the patient. Equipped with a simple and flexible communication protocol for data and command exchange, the developed platform is capable of readily supporting a variety of sensors with different sampling profiles. Furthermore, first promising results of on-going work pave the way for achieving considerable enhancement of sensors' accuracy (close to high-cost commercial ones) and significant extension of platform's portability through power consumption minimization. These characteristics have been verified by experimenting with various medical scenarios one of which is demonstrated here in detail

Low Power OFDM Receiver Exploiting Data Sparseness and DFT Symmetry

An undersampling technique appropriate for Orthogonal Frequency Division Multiplexing (OFDM) that can be implemented with low complexity hardware is presented. The OFDM receiver can operate in undersampling mode, 75% of the time. One of the advantages of the proposed scheme is the reduction down to the half of the power consumed by the Analog Digital Converter (ADC) and the Fast Fourier Transform (FFT). The FFT memory requirements for sample storage can also be reduced and its operating speed can be increased. Simulations were performed for two Quadrature Amplitude Modulation orders (16-QAM and 32-QAM), two options for the FFT size (1024 and 4096), two alternative input symbol structures for the inverse FFT (IFFT), and several sparseness levels and samples substitution options. The Symbol Error Rate (SER) and image reconstruction examples are used to show that a full reconstruction or a very low error can be achieved. Although the proposed undersampling method is evaluated for wired channel, it can also be used without any modification to wireless Single Input Single Output (SISO) systems (with an expected SER degradation). It can also be used with Multiple Input Multiple Output (MIMO) systems when an appropriate arrangement of the IFFT input symbols is adopted

Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems

Several techniques have been proposed that attempt to reconstruct a sparse signal from fewer samples than the ones required by the Nyquist theorem. In this paper, an undersampling technique is presented that allows the reconstruction of the sparse information that is transmitted through Orthogonal Frequency Division Multiplexing (OFDM) modulation. The properties of the Discrete Fourier Transform (DFT) that is employed by the OFDM modulation, allow the estimation of several samples from others that have already been obtained on the side of the receiver, provided that special relations are valid between the original data values. The inherent sparseness of the original data, as well as the Forward Error Correction (FEC) techniques employed, can assist the information recovery from fewer samples. It will be shown that up to 1/4 of the samples can be omitted from the sampling process and substituted by others on the side of the receiver for the successful reconstruction of the original data. In this way, the size of the buffer memory used for sample storage, as well as the storage requirements of the Fast Fourier Transform (FFT) implementation at the receiver, may be reduced by up to 25%. The power consumption of the Analog Digital Converter on the side of the receiver is also reduced when a lower sampling rate is used

Plant Disease Diagnosis for Smart Phone Applications with Extensible Set of Diseases

A plant disease diagnosis method that can be implemented with the resources of a mobile phone application, that does not have to be connected to a remote server, is presented and evaluated on citrus diseases. It can be used both by amateur gardeners and by professional agriculturists for early detection of diseases. The features used are extracted from photographs of plant parts like leaves or fruits and include the color, the relative area and the number of the lesion spots. These classification features, along with additional information like weather metadata, form disease signatures that can be easily defined by the end user (e.g., an agronomist). These signatures are based on the statistical processing of a small number of representative training photographs. The extracted features of a test photograph are compared against the disease signatures in order to select the most likely disease. An important advantage of the proposed approach is that the diagnosis does not depend on the orientation, the scale or the resolution of the photograph. The experiments have been conducted under several light exposure conditions. The accuracy was experimentally measured between 70% and 99%. An acceptable accuracy higher than 90% can be achieved in most of the cases since the lesion spots can recognized interactively with high precision

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications

Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections

Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%