Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

In recent years, commercial exoskeletons have seen widespread use across various industrial processes, offering assistance in tasks that are physically demanding for humans. While exoskeletons enhance task performance, it's crucial to understand their impact during periods of non-task activities, such as walking between workstations. This study utilized IMU sensors to investigate potential differences in gait dynamics when individuals walk with and without exoskeleton assistance. Our research reveals notable discrepancies in both range of motion (ROM) and step length when individuals employ exoskeletons during non-task-related walking. These disparities were investigated through the extraction of gait joint angles' features and the application of classification algorithms.

In this research, we investigate the efficacy of information fusion techniques for the purpose of gait pattern recognition using hip joint angle data captured by smartphone sensors. The classifiers tested were the Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB) algorithms, which gave the highest classification accuracy. But, to further enhance the classification accuracy, we integrated score-level fusion (SLF) and decision-level fusion (DLF), leveraging multiple classifier algorithms. Our experiment results reveal that information fusion techniques improve the overall accuracy with 90.5% for the score-level fusion and 91% for the decision-level fusion (voting scheme), indicating the effectiveness of ensemble methods in hip joint angle-based recognition systems. Lastly, some Limitations of the study, such as the use of treadmills and the focus on healthy adult gait patterns are recognized, highlighting areas for future research, including the application to individuals with gait-affecting conditions.

In recent years, irrigations have been built on dry areas in Majes-Arequipa. Over time, the irrigations water forms moist areas in lower areas, which can have positive or negative consequences. Therefore, it is important to know in advance where the water from the new irrigation will appear. The limited availability of real-time satellite image data is still a hindrance to some applications. Data from environmental satellites NOAA (National Oceanic and Atmospheric Administration) are available fee and license free. In order to receive data, users must obtain necessary equipment. In this work we present a satellite data acquisition system with an RTL SDR receiver, two 137-138 Mhz designed antennas, Orbitron, SDRSharp, WXTolmag and MatLab software. We have designed two antennas, a Turnstile Crossed dipole antenna with Balun and a quadrifilar helicoidal antenna. The antennas parameter measurements show very good correspondence with those obtained by simulation. The RTL SDR RTL2832U receiver, combined with our antennas and software, forms the system for recording, decoding, editing and displaying Automatic Picture Transmission (APT) signals. The results show that the satellite image receptions are sufficiently clear and descriptive for further analysis.

Satellite image processing of an ecosystem allows us to understand it and know, prevent, and investigate the events that take place in the environment. For this we need cheap and simple image reception systems, which include antennas, receivers and hardware/software for signal processing. In this work a turnstile antenna, a quadrifilar helical antenna and a V-dipole antenna were designed, constructed and used to obtain automatic NOAA image transmission signal and convert to a NOAA satellite image.

Environmental monitoring plays a very important role for the health of people and the quality of life in daily places such as home, schools, offices and industries. The temperature, humidity and carbon dioxide concentration levels have to be limited in these environments. In this work, a system web-based environmental monitoring based on WSN technology is presented. Sensors, repeaters and a gateway form the WSN IoT system, where user-friendly interfaces are used to decrease the complexity of the set up. The control of the WSN and the gateway, and the data monitoring can be done through the web server running over the gateway along the software that provides user-friendly interfaces. The data visualization is displayed on a live data dashboard. The system is tested in a university and two industry environments.

Automatic Design of Algorithms through Evolution (ADATE) is a program synthesis system that creates recursive programs in a functional language with automatic invention of recursive help functions and self-adaptive optimization of numerical values. We implement a neuron in a pulse coupled neural network (PCNN) as a recursive function in the ADATE language and then use ADATE to automatically evolve better PCNN neurons for image segmentation. Our technique is generally applicable for automatic improvement of most image processing algorithms and neural computing methods. It may be used either to generally improve a given implementation or to tailor that implementation to a specific problem, which with respect to image segmentation for example can be road following for autonomous vehicles or infrared image segmentation for heat seeking missiles that are to distinguish the heat source of the target from flares.

The development of the International Monitoring System (IMS) for the verification of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has led to a rapid revival of interest in infrasound. Furthermore, the installation of low-frequency sensors at seismic sites has increased in recent years, providing researchers with large and heterogeneous data-sets in near real-time. New techniques are needed to better process all of this data and to extract meaningful information quickly for various applications. In particular, there is a need to find distinct features in the infrasonic signals that allow one to distinguish low level nuclear tests from seismic events.

In this thesis three methods for feature extraction from infrasound and other types of low frequency signal data are discussed: (1) discrete wavelets transforms (DWTs); (2) time scale spectra (TSSs) using continuous wavelet transforms (CWTs); and (3) empirical mode decomposition (EMD). The dimensionality of the feature space can range from a few to thousands. For processing high-dimensional data we use multi-modal data space to find low-dimensional structures. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space.

The overall goal of our research is a system for automatic identification and classification of low-frequency signals in real-time that is easy to implement in hardware. In this thesis we present our design and implementation of the discrete wavelet transform (DWT) on FPGAs for processing a continuous flow of data to obtain optimal extraction of feature information. FPGA simulation and implementation has been realized by using the polyphase structure, the filter reuse method and techniques such as pipelining and basic operations on the QUARTUS II platform. VHDL has been used to describe the functionality of the discrete wavelet transform and ModelSim has been used for the functional verification.

Advancements in electronics provide a vital new option for implementation of low-frequency smart sensors that can perform signal processing close to the sensors and transmit the data wirelessly. These smart sensors can improve the efficiency of an automatic classification system and reduce the cost of actual infrasound microphones. The design of a digital wireless data acquisition system using a QF4512 programmable signal converter from Quickfilter Technologies, a MSP430 microcontroller from Texas Instruments and a F2M03GLA Bluetooth module from Free2move for infrasonic records is also presented in this thesis. The digital wireless data acquisition system has passed extensive laboratory and field tests (e.g. with man-made explosions).

A study of using a miniature 3-axis fluxgate magnetometer to get fingerprints from ferrous objects is also presented in this thesis. In this experiment, distinguishing features of iron samples of four different shapes were determined using wavelet methods. Systematic differences were observed between the signatures of the four shaped iron samples.

The goal of the research presented in this thesis is to extract features, to filter and get fingerprints from signals detected by infrasound, seismic and magnetic sensors. If this can be achieved in a real time system, then signals from various events can be detected and identified in an otherwise torrent data.

Several approaches have been analyzed. Wavelet transform methods are used together with ampligram and time scale spectrum to analyze infrasound, seismic and magnetic data. The energy distribution in the frequency domain may be seen in wavelet scalograms. A scalogram displays the wavelet coefficients as a function of the time scale and of the elapsed time. The ampligram is a useful method of presentation of the physical properties of the time series. The ampligram demonstrate the amplitude and phase of components of the signal corresponding to different spectral densities. The ampligram may be considered as an analogy to signal decomposition into Fourier components. In that case different components correspond to different frequencies. In the present case different components correspond to different wavelet coefficient magnitudes, being equivalent to spectral densities. The time scale spectrum is a forward wavelet transform of each row (wavelet coefficient magnitude) in the ampligram. The time scale spectrum reveals individual signal components and indicates the statistical properties of each component: deterministic or stochastic.

Next step is to distinguish between different sources of infrasound on-line. This will require signal classification after detection is made. The implementation of wavelet – neural network in hardware may be a first choice. In this work the Independent Component Analysis is presented to improve the quality of the infrasonic signals by removing background noise before the hardware classification. The implementation of the discrete wavelet transform in a Field Programmable Gate Array (FPGA) is also included in this thesis using Xilinx System Generator and Simulink software.

A study of using infrasound recordings together with a miniature 3-axis fluxgate magnetometer to find meteorites as soon as possible after hitting the earth is also presented in this work.

Ovarian cancer is one of the leading causes of death front cancer in women. The lifetime risk is around 1.5%, which makes it the second most common gynecologic malignancy (the first one being breast cancer). To have a definitive diagnose, a surgical procedure is generally required and suspicious areas (samples) will be removed and sent for microscopic and other analysis. This paper describes the result of a pilot study in which an electronic nose is used to "smell" the aforementioned samples, analyze the multi-sensor signals and have a close to real-time answer on the detection of cancer. Besides being, fast. the detection method is inexpensive and simple. Experimental analysis using real ovarian carcinoma samples shows that the use of proper algorithms for analysis of the multi-sensor data front the electronic nose yielded surprisingly good results with more than 77% classification rate. The electronic nose used in this pilot study was originally developed to be used as a "bomb dog" and can distinguish between e.g. TNT. Dynamex. Prillit. However, it was constructed to be a flexible multi-sensor device and the individual (16) sensors call easily be replaced/exchanged. This is suggestive for further investigations to obtain even better results with new, specific sensors. In another pilot experiment, headspace of an ovarian carcinoma sample and a control sample were analyzed using gas chromatography-mass spectrometry. Significant differences in chemical composition and compound levels were recorded, which would explain the different response obtained with the electronic nose.

Magnetic objects can cause local variations in the Earth's magnetic field that can be measured with a magnetometer. Here we used tri-axial magnetometer measurements and an analysis method employing wavelet techniques to determine the "signature" or "fingerprint" of different iron objects. Clear distinctions among the iron samples were observed. The time-dependent changes in the frequency powers were extracted by use of the Morlet wavelet corresponding to frequency bands from 0.1 to 100 Hz.

It is well known that industrial and factory environments present considerable challenges for wireless communications. Because every industrial environment is different, and may offer a unique set of obstacles to effective wireless communication, a site characterization is needed at first step in determining improvements of existent wireless technologies to increase the reliability. In this work electric field strength and APD measurements have been performed to characterize electromagnetic interference in an industrial paper plant. Common characteristics of the industrial environments affecting wireless communication were identified. Additionally, results show high interference levels at the frequencies for the DECT band 1880-1890 MHz. The interference level is correlated to the working mode of the electrical engines used in the process.

Wireless communications in industrial environments are maintained under persistent adverse conditions, such as noise, fading and many electromagnetic interference sources. These electromagnetic interferences exhibit usually impulsive characteristics and it can seriously degrade the performance of the current wireless systems. Over the last few years, the amplitude probability distribution (APD) had been formally written into CISPR16 as a measure of the emitted electromagnetic energy from electrical equipment. In this approach we present two APD measurement methods. The first method based on 12-bit A/D converter and the second one based on in-phase and quadrature components of the impulsive noise at frequencies between 20 and 3000 MHz. Electromagnetic interference measurements in three different industrial environments were performed using the developed methods with promising results.

Magnetic objects can cause local variations in the Earth's magnetic field that can be measured with a magnetometer. Here we used triaxial magnetometer measurements and an analysis method employing wavelet techniques to determine the "signature" or "fingerprint" of different iron objects. Clear distinctions among the iron samples were observed. The time-dependent changes in the frequency powers were extracted by use of the Morlet wavelet corresponding to frequency bands from 0.1 to 100 Hz. (c) 2007 Elsevier B.V. All rights reserved.

Infrasound is a low frequency acoustic phenomenon typically in the frequency range 0.01 to 20 Hz. Data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important. Using wavelet decomposition as a tool for removing noise from the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of Discrete Wavelet Transform (DWT) for real-time infrasound data processing, in which only two FIR filters, a high-pass and a low-pass filter, are used. With the filter reuse method and techniques such as pipeline, basic operations, by the VHDL on the platform QUARTUS II, FPGA simulation and implementation are fulfilled. This implementation takes advantage from the low sampling rate used by the infrasound monitoring system that is only 18 Hz.

Food companies worldwide must constantly engage in product development to stay competitive, cover existing markets, explore new markets, and meet key consumer requirements. This ongoing development places high demands on achieving quality at all levels, particularly in terms of food safety, integrity, quality, nutrition, and other health effects. Food product research is required to convert the initial product idea into a formulation for upscaling production with ensured significant results. Sensory evaluation is an effective component of the whole process. It is especially important in the last step in the development of new products to ensure product acceptance. In that stage, measurements of product aroma play an important role in ensuring that consumer expectations are satisfied. To this end, the electronic nose (e-nose) can be a useful tool to achieve this purpose. The e-nose is a combination of various sensors used to detect gases by generating signals for an analysis system. Our research group has investigated the scent factor in some foodstuff and attempted to develop e-noses based on low-cost technology and compact size. In this paper, we present a summary of our research to date on applications of the e-nose in the food industry.

In this paper, we describe a new low-cost and portable electronic nose instrument, the Multisensory Odor Olfactory System MOOSY4. This prototype is based on only four metal oxide semiconductor (MOS) gas sensors suitable for IoT technology. The system architecture consists of four stages: data acquisition, data storage, data processing, and user interfacing. The designed eNose was tested with experiment for detection of volatile components in water pollution, as a dimethyl disulphide or dimethyl diselenide or sulphur. Therefore, the results provide evidence that odor information can be recognized with around 86% efficiency, detecting smells unwanted in the water and improving the quality control in bottled water factories.

In recent years, with the advent of new and cheaper sensors, the use of olfactory systems in home, industry and hospital has get a new start. Multi-sensor systems can improve the ability to distinguish between complex mixtures of volatile substances. In this approach, 32 metal oxide semiconductor (MOS) sensors operating at different temperatures have been used to develop a multi-sensor olfactory system. 

In recent years, with the advent of new and cheaper sensors, the use of olfactory systems in homes, industries, and hospitals has a new start. Multisensor systems can improve the ability to distinguish between complex mixtures of volatile substances. To develop multisensor systems that are accurate and reliable, it is important to take into account the anomalies that may arise because of electronic instabilities, types of sensors, and air flow. In this approach, 32 metal oxide semiconductor sensors of 7 different types and operating at different temperatures have been used to develop a multisensor olfactory system. Each type of sensor has been characterized to select the most suitable temperature combinations. In addition, a prechamber has been designed to ensure a good air flow from the sample to the sensing area. The multisensor system has been tested with good results to perform multidimensional information detection of two fruits, based on obtaining sensor matrix data, extracting three features parameters from each sensor curve and using these parameters as the input to a pattern recognition system.

In recent years, industrial wireless applications have emerged rapidly. The use of short-range radio communication systems in factories increases the flexibility in industrial processes by reducing the use of cables. However, the technological challenges involved in wireless communication in industrial environments are not trivial; they result in disadvantages with respect to reliability and security because of electromagnetic interference. To gain an understanding of the performance limits of these wireless applications, knowing the characteristics of these environments is essential. In this approach, amplitude probability distribution and rms delay spread measurements have been used to perform electromagnetic site surveys in three factory automation infrastructures.

Thanks to advances in digital technology many hospitals are becoming populated with wireless medical applications to control life critical functions. Electromagnetic interference can cause severe performance degradations on these wireless applications. Several accidents have been reported which calls for a more thorough characterization of these interferences in areas where critical wireless applications are used. In this paper the results of electromagnetic interference measurements performed in a hospital are presented. The amplitude probability distribution (APD) and the inter arrival pulse probability distribution (PSD) are used to characterize these environments. In addition, Middleton parameters can be calculated from the measured data. This study is considered to be a first effort to characterize the 20 MHz -2500 MHz band in hospitals.

Results of three years' field measurements on radio propagation in industrial environments have been analyzed using four propagation models: the Saleh-Valenzuela model, the twocluster model, the indoor power delay profile model, and our more recent adjusted model. In this study, we used the results of measurements performed at a steel mill, a paper mill, and in a laboratory environment for three frequency bands (183-683 MHz, 1640-2140 MHz, and 2200-2700 MHz) and for line-of-sight and non-line-of-sight cases.

Combining electronics, telecommunications, and information technology to connect devices and remote systems is perhaps the best feature of the future Machine-to-Machine (M2M) technology. Wireless communication technologies for managing future M2M applications are becoming mature, but electromagnetic interference and time dispersion in industrial environments can limit the successful functioning of these wireless systems, leading to a failure in the control of critical functions. The characterization of these environments is necessary for collecting and specifying M2M requirements. In this paper, we present the conclusions from measurements carried out in four different industrial environments during the past three years.

The Ricean K-factor and antenna diversity properties for indoor industrial environments have been characterized for 433 and 868 MHz. The high amount of metallic structures gives a multipath environment that heavily differs from other environments e.g. indoor office environments. The results show that low correlation between receiving antennas can be achieved for shorter antenna distances than in other environments.

Several studies have characterized industrial environments as being highly reflective. In this paper, we provide the data obtained from electromagnetic field measurements performed at the warehouse of a paper mill. The data is also compared to simulated data. This data proves the existence of non-reflective and very high absorption industrial environments where wireless communication is impossible at certain frequencies. Furthermore, in such environment, radio performance cannot be improved by multiple antenna solutions such as MIMO (Multiple Input Multiple Output) since multiple reflections are effectively absorbed.

Wireless communication is expected to improve the safety and the productivity in underground tunnels for industrial use. However the multiple shapes and structures of tunnels affect wireless communication characteristics in terms of signal propagation which is significantly different from terrestrial environments. This paper presents comprehensive broadband measurements and simulations of multipath propagation and path loss in two underground environments. The results can be used in the development of new communication systems in tunnels that provide industrial services.

In recent years, the use of wireless systems in industrial applications has experienced spectacular growth. Unfortunately, industrial environments often present impulsive noise which degrades the reliability of wireless systems. OFDM is an enhanced technology used in industrial communication to monitor the work and movement of employees using high quality video. However, OFDM is sensitive to high amplitude impulsive noise because the noise energy spreads among all OFDM sub-carriers. This paper proposes a receiver structure consisting of two stages: a detector stage combining Fisher’s Quadratic discriminant and Gaussian Hypothesis techniques, and a suppression stage optimized by setting well defined thresholds. The receiver structure has been tested by simulations and measurements providing an increment in the probability of detection and improving the system performance.

Impulsive noise is a major source of degradation in industrial communications. Orthogonal frequency-division multiplexing (OFDM) is an extended technique used in many industrial communications, however the performance of OFDM systems is reduced under an impulsive noise source. To increase the system performance, impulsive noise detection and suppression techniques can be designed in the communication system. OFDM has high levels of peak-to-average power ratio (PAPR), thus PAPR reduction techniques, such as selected mapping (SLM), are implemented in OFDM systems. This paper proposes an impulsive noise detection exploiting the statistical properties of the OFDM envelope when applying SLM. The proposed detection technique increases the probability of detection and improves the BER of the communication system compared to other impulsive detection techniques.

Modern underground mines require reliable wireless communication for transmitting voice data, operating surveillance cameras, and monitoring mining equipment such as heavy vehicles. The electromagnetic characteristics of mines therefore have to be considered when determining the type of wireless technology for such critical applications. In this reported work, measurements of radio interference levels, path loss, and multipath propagation are performed in the world's now largest iron ore mine, situated in Sweden, to determine a suitable wireless technology for this mine.

Experience has shown that Bluetooth, Wireless LAN (WLAN), Digital Enhanced Cordless Telecommunications (DECT) and other Industrial, Scientific and Medical (ISM) frequency band wireless technologies developed for office use, have encountered problems when used in critical industrial applications. The development of more reliable wireless solutions requires extensive knowledge of industrial environments with regards to both electromagnetic interference and wave propagation. This study presents the results of the analysis of two important classes of industrial environments having opposite characteristics, one being highly absorbent and the other being highly reflective, with respect to radio wave propagation. The analysis comprises both multipath and path loss characterisation. The results show that wireless solutions with different fundamental properties must be chosen for each of these environments to ensure high reliability. The conclusions of this work can be used as an important reference for further research in this area, as well as the design of new standards and guidelines for selecting wireless solutions in similar industrial environment classes.

In this paper a technique to detect multiple impulsive interference in an industrial environment is proposed and evaluated. The technique is based on discriminant analysis which iteratively peels-off the impulsive interferences. The probability of detection of the technique is tested with and without the iterative peeling-off part. The simulations show that the SIR can be improved by applying the detection technique and then blank or clip the impulsive interference components. The improvement in the SIR depends on the impulsive interference parameters and it can reach up to 17 dB.

The quality of wine is checked both during the production process and upon consumption. Therefore, manual wine-tasting work is still valuable. Due to the nature of wine, many volatile components are released, and it is therefore difficult to determine which elements need to be controlled. Acetic acid is one of the substances found in wine and is a crucial substance for wine quality. Gas sensor systems may be a potential alternative for manual wine tasting. In this work, we have developed a TGS2620 gas sensor module to analyze acetic acid levels in red wine. The gas sensor module was refined according to the Venturi effect along with signal slope analysis, providing promising results. The example included in this paper demonstrates that there is a direct relationship between the slope of the MOS gas sensor response and the acetic acid concentration. This relationship is useful to evaluate the ethanol oxidation in acetic acid in red wine during its production process.