Phase Space Analysis of Cardiac Spectra

Cardiac diseases are one of the main reasons of mortality in modern, industrialized societies, and they cause high expenses in public health systems. Therefore, it is important to develop analytical methods to improve cardiac diagnostics. Electric activity of heart was first modeled by using a set of nonlinear differential equations. Latter, variations of cardiac spectra originated from deterministic dynamics are investigated. Analyzing the power spectra of a normal human heart presents His-Purkinje network, possessing a fractal like structure. Phase space trajectories are extracted from the time series graph of ECG. Lower values of fractal dimension, D indicate dynamics that are more coherent. If D has non-integer values greater than two when the system becomes chaotic or strange attractor. Recently, the development of a fast and robust method, which can be applied to multichannel physiologic signals, was reported. This manuscript investigates two different ECG systems produced from normal and abnormal human hearts to introduce an auxiliary phase space method in conjunction with ECG signals for diagnoses of heart diseases. Here, the data for each person includes two signals based on V_4 and modified lead III (MLIII) respectively. Fractal analysis method is employed on the trajectories constructed in phase space, from which the fractal dimension D is obtained using the box counting method. It is observed that, MLIII signals have larger D values than the first signals (V_4), predicting more randomness yet more information. The lowest value of D (1.708) indicates the perfect oscillation of the normal heart and the highest value of D (1.863) presents the randomness of the abnormal heart. Our significant finding is that the phase space picture presents the distribution of the peak heights from the ECG spectra, giving valuable information about heart activities in conjunction with ECG.Comment: 10 pages, 8 figures, 1 table. arXiv admin note: text overlap with arXiv:2305.1045

Büyük patlama büyük çöküş optimizasyon yöntemi ile ultra geniş band sensörlerinin iç mekân konum belirleme doğruluklarının iyileştirilmesi

Ultra geniş band teknolojisi, birçok iç mekân konum belirleme sisteminde başarılı çözümler sunan, diğer yöntemlere kıyasla daha iyi performans gösteren, gelişmekte olan bir teknolojidir. Bu çalışmada, ultra geniş band (Ultra Wide Band-UWB) sensörler kullanılarak bir iç mekân konum belirleme sistemi geliştirilmiş ve kullanılan ek algoritmalarla, standart donanımların sağladığı doğruluk düzeyi arttırılırken aynı zamanda ortalama hatayı azaltmak hedeflenmiştir. Bu amaçla Büyük Patlama - Büyük Çöküş (Big Bang-Big Crunch veya BB-BC) optimizasyon yöntemi deneysel iç mekân konumlandırma sistemine uygulanmış ve ölçüm doğruluğu üzerindeki olumlu etkisi yapılan testlerle kanıtlanmıştır. Test alanı olarak 7.35 m × 5.41 m boyutlarında 39.76 m2'lik bir alan seçilmiş ve özel olarak tasarlanmış bir tavan sistemine yerden 2.85 m yüksekliğe üç farklı UWB alıcı yerleştirilmiş ve 182 adet test noktasından 60 sn. süreyle toplam 10.920 ölçüm alınmıştır. Ölçüm sonuçları Büyük Patlama - Büyük Çöküş optimizasyon algoritması ile düzeltilerek, ortalama hatası önceki 20.72 cm değerinden 15.02 cm’ye düşürülmüş, böylelikle ölçüm sonuçlarının doğruluğu arttırılmıştır

Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi

Düşük enerjili Bluetooth işaretçi (Bluetooth low energy - BLE beacon) teknolojisi, iç mekan konum belirleme sistemlerinde başarılı ve düşük maliyetli çözümler sunan gelişmekte olan bir teknolojidir. Bu çalışmada, BLE işaretçileri (beacons) kullanan bir iç mekan konum belirleme sistemi geliştirilmiş, kullanılan ilave algoritmalarla standart sensörlerden elde edilen konum değerlerinin doğruluğunun artırılması amaçlanmıştır. Bunun için, deneysel iç mekan konum algılama sisteminden elde edilen konum bilgilerine Büyük Patlama – Büyük Çöküş (Big Bang – Big Crunch (BB-BC)) optimizasyon yöntemi uygulanmış ve konum doğruluğunun geliştirildiği yapılan testlerle kanıtlanmıştır. Test alanı olarak, 9,60 m × 3,90 m boyutundaki 37,44 m2'lik alan seçilmiş ve 2,40 m × 1,30 m boyutundaki oniki tane ızgara alanına ayak izi (fingerprinting) algoritması uygulanmıştır. Test alanına dört tane BLE işaretçi (beacon) yerleştirilmiş, on iki test alanından 150 saniye boyunca toplam 9.000 ölçüm yapılmıştır. Ölçüm sonuçları Büyük Patlama – Büyük Çöküş optimizasyon yöntemi ile Öklid uzaklık eşleştirme yöntemi ve Kalman Filtresi kullanılarak iyileştirilmiş, bu sayede konum doğruluğu %26,62'den %75,69'a arttırılmıştır

Early Steps in Automated Behavior Mapping via Indoor Sensors

Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies, we chose Wi-Fi, BLE beacon and ultra-wide band (UWB) sensor technologies for their popularity and investigated their applicability in BM. We tested three technologies for error ranges and found an average error of 1.39 m for Wi-Fi in a 36 m2 test area (6 m × 6 m), 0.86 m for the BLE beacon in a 37.44 m2 test area (9.6 m × 3.9 m) and 0.24 m for ultra-wide band sensors in a 36 m2 test area (6 m × 6 m). We simulated the applicability of these error ranges for real-time locations by using a behavioral dataset collected from an active learning classroom. We used two UWB tags simultaneously by incorporating a custom-designed ceiling system in a new 39.76 m2 test area (7.35 m × 5.41 m). We considered 26 observation points and collected data for 180 s for each point (total 4680) with an average error of 0.2072 m for 23 points inside the test area. Finally, we demonstrated the use of ultra-wide band sensor technology for BM

A Clustering-Based Approach for Improving the Accuracy of UWB Sensor-Based Indoor Positioning System

There are several methods which can be used to locate an object or people in an indoor location. Ultra-wideband (UWB) is a specifically promising indoor positioning technology because of its high accuracy, resistance to interference, and better penetration. This study aims to improve the accuracy of the UWB sensor-based indoor positioning system. To achieve that, the proposed system is trained by using the K-means algorithm with an additional average silhouette method. This helps us to define the optimal number of clusters to be used by the K-means algorithm based on the value of the silhouette coefficient. Fuzzy c-means and mean shift algorithms are added for comparison purposes. This paper also introduces the impact of the Kalman filter while using the measured UWB test points as an input for the Kalman filter in order to obtain a better estimation of the position. As a result, the average localization error is reduced by 43.26% (from 16.3442 cm to 9.2745 cm) when combining the K-means algorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system

Analytical Expense Management System

Although the development of communication technologies (e.g: UMTS, ADSL) allowed the elaboration of multiple users' Web applications (e.g. information storage), there are still many improvements on many applications to be done and uncovered areas. Expense management systems on Web application area are still in their infancy. Expense management software is widely spread in companies and most of time supported by their intranet. These solutions are quite simple as they mainly collect the information related to the expenses and may propose a simple aggregation of these figures. The result is close to what an excel sheet provides

Fast multi-view face tracking with pose estimation

In this paper, a fast and an effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classifiers built using either Haar-like filters or Gauss filters. A first classifier extracts faces of any pose from the background. Then more specific classifiers discriminate between different poses. The tree of classifiers is trained by hierarchically sub-sampling the pose space. Finally, Condensation algorithm is used for tracking the faces. Experiments show large improvements in terms of detection rate and processing speed compared to state-of-the-art algorithms

Nonlinear Continuous-Time Generalised Predictive Control Through Successive System Linearisations

Non linear predictive control may require significant off-line and online computations. This paper gives a nonlinear predictive control algorithm based on successive off-equilibrium linearisation which requires less computation whilst accounting for nonlinear system behaviour. A nonlinear example (the pendulum and cart system) is used to show that the method provides a practical tradeoff between computational requirements and performance. 1 Introduction Predictive control based on non-linear models often requires significant computations, both off-line and on-line. This can preclude practical implementation when processor power is limited and yet high bandwidth control is required. This is particularly true in the context of mechanical systems which typically have much higher closed loop bandwidth that process engineering systems. Different algorithms have been proposed to enable a faster optimisation (see for instance [19, 21, 20]). Alternatively, an intermittent receding horizon a..