MILITAAROBJEKTIDE VALVETEHNOLOOGIA ARENG

Artikkel analüüsib viimastel aastatel maailmas käivitunud infotehno loogilisi arenguhüppeid, mis on toonud olulisi muutusi valvesüsteemide alale. Militaarobjektide valvesüsteeme on käsitletud nii sensorseadmete, süsteemi arhitektuuri kui ka üha rohkem tehisintellekti kasutava andmetöötluse vaates. Käsit luse süstematiseerimiseks on esile toodud neli põhilist alamsüsteemi ning teema sidu miseks maailma arengusuundadega on analüüsitud kaheksa olulise infotehnoloogilise uurimisteema kasvukõveraid. Detailsemalt käsitletud sensorseadmed on järgmised: 1) valvekaamerad nähtava valguse, lähiinfrapuna ja soojuskiirguse diapasoonidele; 2) sensoritega varustatud targad piirdetarad; 3) süsteemid mehitamata õhusõidukite avastamiseks ja jälgi miseks. Kaamerate puhul on analüüsitud kujutise sensorite arengutendentse ja DORI (detekteerimine, jälgimine, eristamine, identifitseerimine) tuvastusstandardit. Artiklis käsitletakse olulisemaid termineid ning arengusuundi, pidades silmas materjali võimalikku kasutamist tehniliste erialade väljaõppes Kaitseväe Akadeemias ja mujal

Investigation of planar coil for eddy current conductivity measurements in wide frequency range

Peer Reviewe

Investigation of planar coil for eddy current conductivity measurements in wide frequency range

Peer Reviewe

Slope-Based Event-Driven Feature Extraction For Cardiac Arrhythmia Classification

International audienceTo detect cardiovascular diseases (CVD), electrocardiogram (ECG) of a patient must be recorded and analyzed for a long period. For an effective diagnosis, the ECG recording system must automatically adapt to new patients. This paper presents a low-complexity artificial neural network that exclusively uses the consecutive slopes of ECG signal as inputs. These features are extracted using a level-crossing ADC and a simple TDC-based event-driven processing chain. The proposed clockless system can detect arrhythmias in ECG with 98.4% accuracy and reduce the ANN hardware complexity by more than half compared to recent literature. It is perfectly adapted to integrated wearable monitoring systems and shows good adaptability to new patients. Keywords-Artificial neural network (ANN), electrocardiogram (ECG), cardiac arrhythmia classification (CAC), event-driven, time-to-digital converter (TDC), levelcrossing AD

Antidictionary-Based Cardiac Arrhythmia Classification for Smart ECG Sensors

International audienceCardiovascular diseases can be detected early by analyzing the electrocardiogram of a patient using wearable systems. In the context of smart sensors, detecting arrhythmias with good accuracy and ultra-low power consumption is required for long-term monitoring. This paper presents a novel cardiac arrhythmia classification method based on antidictionaries. The features are sequences of consecutive slopes that are generated from event-driven processing of the input signal. The proposed system shows an average detection accuracy of 98% while offering an ultra-low complexity. This antidictionary-based method is also particularly suited to imbalanced datasets since the antidictionaries are created exclusively from heartbeats classified as normal beats

Non-Standard Electrode Placement Strategies for ECG Signal Acquisition

Background: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. Methods: In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. Results: Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. Conclusions: The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed

Methods for Detection of Bioimpedance Variations in Resource Constrained Environments

Changes in a certain parameter are often a few magnitudes smaller than the base value of the parameter, specifying significant requirements for the dynamic range and noise levels of the measurement system. In case of electrical bioimpedance acquisition, the variations can be 1000 times smaller than the entire measured value. Synchronous or lock-in measurement of these variations is discussed in the current paper, and novel measurement solutions are presented. Proposed methods are simple and robust when compared to other applicable solutions. A common feature shared by all members of the group of the proposed solutions is differentiation. It is achieved by calculating the differences between synchronously acquired consecutive samples, with lock-in integration and analog differentiation. All these methods enable inherent separation of variations from the static component of the signal. The variable component of the bioimpedance can, thus, be acquired using the full available dynamic range of the apparatus for its detection. Additive disturbing signals and omnipresent wideband noise are considered and the method for their reduction is proposed