Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms

Joint Ultra-wideband and Signal Strength-based Through-building Tracking for Tactical Operations

Accurate device free localization (DFL) based on received signal strength (RSS) measurements requires placement of radio transceivers on all sides of the target area. Accuracy degrades dramatically if sensors do not surround the area. However, law enforcement officers sometimes face situations where it is not possible or practical to place sensors on all sides of the target room or building. For example, for an armed subject barricaded in a motel room, police may be able to place sensors in adjacent rooms, but not in front of the room, where the subject would see them. In this paper, we show that using two ultra-wideband (UWB) impulse radios, in addition to multiple RSS sensors, improves the localization accuracy, particularly on the axis where no sensors are placed (which we call the x-axis). We introduce three methods for combining the RSS and UWB data. By using UWB radios together with RSS sensors, it is still possible to localize a person through walls even when the devices are placed only on two sides of the target area. Including the data from the UWB radios can reduce the localization area of uncertainty by more than 60%.Comment: 9 pages, conference submissio

Ultra wideband: applications, technology and future perspectives

Ultra Wide Band (UWB) wireless communications offers a radically different approach to wireless communication compared to conventional narrow band systems. Global interest in the technology is huge. This paper reports on the state of the art of UWB wireless technology and highlights key application areas, technological challenges, higher layer protocol issues, spectrum operating zones and future drivers. The majority of the discussion focuses on the state of the art of UWB technology as it is today and in the near future

An efficient iris image thresholding based on binarization threshold in black hole search method

In iris recognition system, the segmentation stage is one of the most important stages where the iris is located and then further segmented into outer and lower boundary of iris region. Several algorithms have been proposed in order to segment the outer and lower boundary of the iris region. The aim of this research is to identify the suitable threshold value in order to locate the outer and lower boundaries using Black Hole Search Method. We chose these methods because of the ineffient features of the other methods in image indetification and verifications. The experiment was conducted using three data set; UBIRIS, CASIA and MMU because of their superiority over others. Given that different iris databases have different file formats and quality, the images used for this work are jpeg and bmp. Based on the experimentation, most suitable threshold values for identification of iris aboundaries for different iris databases have been identified. It is therefore compared with the other methods used by other researchers and found out that the values of 0.3, 0.4 and 0.1 for database UBIRIS, CASIA and MMU respectively are more accurate and comprehensive. The study concludes that threshold values vary depending on the database