Infrared Local Positioning Systems (IRLPS) offer a cost-effective and accurate alternative for indoor localization, where GNSS signals are typically not available. Despite their potential, IRLPS face significant challenges such as noise, multipath effects, multiple access interference, and calibration requirements, which limit their performance. In response, this work explores the integration of machine learning by proposing a Feedforward Neural Network (FNN) trained on energy measurements collected from a quadrant photodiode. We conducted a thorough analysis of hyperparameters and an ablation study across five neural network topologies and identified configurations that balance accuracy and model complexity. Experimental evaluations in a controlled indoor environment (2.4×2.4×3.4 m3) demonstrate that even simple FNN architectures can generalize well and achieve a high accuracy, with 90% of the positioning errors being below 0.05 m.

Non-intrusive load monitoring (NILM) enables appliance-level behaviour analysis by examining the aggregated electrical consumption signals. These techniques hold significant potential for applications ranging from electrical load management to remote human health monitoring. Despite its potential, NILM faces challenges in adapting to evolving appliance baselines, including the integration of new devices or the replacement of existing ones. These challenges aggravate when dealing with a large number of appliances, or even more if there are overlapping energy consumption profiles, thus reducing the effectiveness of load monitoring techniques. In real-world scenarios, the scarcity of labelled data further intensifies these issues, increasing the risk of overfitting. This limits the ability of NILM models to generalise and perform effectively on unseen data. To address these limitations, this work presents some methods for accurately classifying known appliances while identifying unknown ones by using features derived from electrical current signals. The framework includes a feature extraction stage that explores neural networks with both supervised and unsupervised learning techniques to derive latent representations. Additionally, the appliance distinction stage optimises data distribution for recognised known appliances and evaluates two distinct approaches (a supervised method and a semi-supervised one) for detecting unseen appliances. Experimental evaluations demonstrate promising results, achieving over 95% accuracy for the supervised feature extraction method and 83% for the unsupervised one in classifying known appliances, even under limited data conditions. Furthermore, both approaches performed well in detecting unseen appliances, with detection rates exceeding 90% for the supervised classification method and 70% for the semi-supervised method for certain categories. • A novel NILM approach combines classification with anomaly detection capabilities. • The analysis spans approaches from supervised to unsupervised learning techniques. • The proposals identify known and novel devices, enhancing the system’s adaptability. • Techniques are employed to minimise overfitting while maintaining robustness. • The study addresses challenges in limited-sample, multi-category NILM scenarios.

The combination of lensless diffraction imaging and microfluidics provides a relevant solution for the lab-on-a-chip analysis of cells in the context of the Internet of Medical Things (IoMT). However, the cells are on multiple planes in microfluidics, and they only can be sensed by means of complex algorithms. Taking into account the limited resources available on edge computing, a lightweight multifocal fusion reconstruction algorithm is proposed in this work, which still keeps a high-quality reconstruction. The reconstruction, autofocus, and multifocal fusion are integrated, and the reference light normalization and the object light extraction are achieved to adapt their combination. The integrated process is divided into two stages: the cell diffraction image segmentation and the multifocal fusion, based on mono-focal object light reconstruction and masking, respectively. The single autofocus with the golden section search algorithm is merged into the two stages. Finally, a validation system is built based on an FPGA, with a size of only 45 mm × 45 mm × 75 mm. The average error for 8 μm diameter microspheres is 4.67%. For the white blood cells, the evaluation functions of the criteria, the energy of gradient (EOG), and the variance are improved by 12.2%, 92.3%, and 57.3%, respectively. Under the same accuracy, the global number of searches for focus is reduced by 47%-59%, compared to the two-step golden section method, whereas the global speed is also improved by 53%.

In the context of massive machine-type communications (mMTC) within the Internet of Things (IoT), joint activity detection and channel estimation (JADCE) is a key challenge in enabling massive access due to sporadic device access patterns. In this work, we consider both single-antenna and multiple-antenna base station scenarios and formulate the JADCE problem using the least absolute shrinkage and selection operator (LASSO) framework. To address this problem, we propose a model-driven network that utilizes compressive sensing (CS) and deep learning techniques. Specifically, we first design a prediction-correction alternating direction method of multipliers (PC-ADMM) as the underlying algorithm of the model-driven network. Then, the network is developed based on the PC-ADMM and is designed to be complex-valued. Furthermore, we also model the proximal operator, typically used to generate sparse solutions in LASSO, as a channel attention module within the model-driven network to enhance robustness. Numerical results show that the proposed PC-ADMM framework outperforms existing LASSO-based methods in terms of channel estimation and device activity detection.

The calculation of division and square root is widely used in edge computing related to image processing, clustering, recognition, and reconstruction, among others. Their common multi-stage serial calculation takes longer, and requires a higher latency, and more redundant hardware resources for multi-dimensional parallel computations. This work proposes a low-latency power series approximate digital computing (PSADIC) paradigm and architecture, which achieves a fast low-latency calculation of multi-dimensional mathematical expressions, focusing on the co-calculation of division and square root. This approach allows to compute not only the division and the square root, but also the inverse and the inverse square root. Moreover, serial and pipelined architectures have been designed here to achieve smaller areas and lower latencies, respectively. Compared to the multi-stage calculation with CORDIC (COordinate Rotation DIgital Computer), the mean relative error distance (MRED) is reduced by 67% in PSADIC. Under TSMC (Taiwan Semiconductor Manufacturing Company) 40nm CMOS technology, the proposed serial and pipelined architectures achieve a 66.67% overall latency reduction compared to the CORDIC when using both its own maximum clock frequency, and a 75% cycle latency reduction. Meanwhile, both the ADP (Area Delay Product) and PDP (Power Delay Product) are also optimized.

The demand for accurate and reliable local positioning systems has grown rapidly with the increasing use of Location-Based Services (LBS). Emerging technologies such as optical, RF, acoustic, mechanical, and magnetic are feasible options for indoor positioning. Optical-based systems have gained attention due to the widespread LED lamp availability and centimeter-level accuracy. In this context, this work presents the implementation and validation of a centralized optical Local Positioning System (LPS) based on a set of mobile emitter LEDs and a minimum of two fixed receivers, enabling simultaneous position of all tags in the infrastructure (centralized approach). The system is designed for long-distance measurements, using Visible or Infrared Light signals. By incorporating geometric considerations into a Non-Linear Least Squares Estimator (NLSE), the system can estimate the 3D position of a mobile object (i.e. person, drone). The comparison of different deployments demonstrates that the proposed system estimates the 3D position with less than 4mm of error in 90% of cases when using four non-aligned receivers and a single LED.

Indoor Device-Free Localisation (DFL) systems are receiving increasing attention to replace traditional active ones in some applications, such as those that support independent living for vulnerable people who are not able to carry sensors. These systems analyse the influence in the channel transmission response of certain emitted signals when interacting with the human body in order to locate it. Whereas the most widespread DFL systems make use of radio-frequency signals through Radar, WiFi or BLE (Bluetooth Low Energy) sensors, this work focuses on those that use acoustic signals, at ultrasound (US) frequencies, to estimate the position of the person. The emitted acoustic signals are altered by reflections, absorptions and frequency shifts (Doppler effect), derived from the medium and other elements existing in the environment, which are interesting to study in order to develop DFL systems. For this purpose, this work proposes a simulator that models all the phenomena that compose the channel response for the recreation of a real environment. In addition, a preliminary analysis of an ultrasonic positioning system that makes use of ultrasound reflections from the person’s head is presented. The proposed tool is useful to develop new ultrasonic DFL systems that take advantage of the acoustic properties with the aim of obtaining preliminary results previously to address a real prototype.

The accuracy of optical positioning systems can be compromised by multiple factors (reflections, calibration, etc.). As an alternative to the triangulation, multilateration, or fingerprinting techniques typically used in these systems, stochastic estimation techniques can be used, such as Kalman Filters (KF) in its different variants. They estimate the receiver position based on the acquired measurements and the estimated positions in previous iterations. This work presents the evaluation of a 3D optical positioning system, based on four LED beacons and a quadrant receiver, using an Extended Kalman Filter (EKF). The implementation of a measurement noise covariance matrix that is adjusted depending on the angle and distance between transmitters and receiver, obtained in the previous iteration, is analysed. The receiver position estimation using both a dynamic and a static measurement noise covariance matrix is evaluated and compared with simulations and experimental tests. In simulations, the achieved errors are below 6 cm and 12 cm in 75% of the cases when using a dynamic and a static noise matrix, respectively. In the experimental tests, the obtained errors in 75% of the cases for the position in plane XY and the rotation angle γ are smaller than 7.44cm and 1.06° with a dynamic noise matrix; and below 7.59 cm and 1.62° for a static one.

Indoor optical positioning systems have increased in popularity in recent years because they can provide centimeter accuracy in three dimensions (3D) utilizing light-emitting diodes (LEDs) and photoreceptors. This work presents the design of a positioning system, which is based on a set of four photoreceptors functioning as beacons at known places for a single LED to be positioned. However, it might be extended to additional emitters with some medium access control. The associated processing is explained, as well as the basic assumptions to be addressed when approaching its hardware implementation, such as the preliminary partitioning of tasks between hardware and software, and the fixed-point representation of the processing to be implemented in hardware. The system has been validated by simulation in a$2\times 2\times 3.4\ \mathbf{m}^{3}$volume, yielding mean absolute errors around 0.004 m for the X and Y axes, and around 0.01 m for the Z-axis, as well as lower standard deviations than 0.004 m for the X and Y axes and 0.01 m for the Z-axis.

This work presents an Android application that allows indoor positioning of portable devices, such as mobile phones or tablets, and its smooth transition to outdoors localization. Different technologies and methods have been combined for that purpose: Global Navigation Satellite Systems (GNSS), Ultrasonic Local Positioning Systems (ULPS), measurements of an Inertial Measurement Unit (IMU) carried by the user, and Graph-Matching techniques. The U-LPSs, composed by a set of ultrasonic beacons, are placed in indoor areas where higher precision is required, such as rooms or waypoints. The navigation between different U-LPSs is done through the use of an IMU. The accumulated drift error of the IMU is corrected upon reaching a ULPS; furthermore, graph-matching has also been integrated whether the map of the building is known. Outdoor GNSS signals are used in conjunction with inertial measurements. An Extended Kalman Filter (EKF) has been proposed for the fusion of the ultrasonic signals, the inertial signals from the IMU and the GNSS position provided by the chipset integrated in the portable device. In this way, an Android application installed on the user’s portable device allows to obtain real-time location and displays trajectory indoors, outdoors and in the transition between both.

Power-line communication (PLC) systems are used for data transmission through the mains. The deployment of intelligent networks and the high demand for broadband communications have made this technology spread worldwide in recent years. Multicarrier modulations, such as filter-bank multicarrier (FBMC), allow broadband links to be achieved by improving intercarrier interference, at the cost of adding complexity to the system. Nevertheless, the main disadvantage of PLC is the medium, commonly with significant noise and interference. To compensate these effects inserted by the channel, channel estimators and equalizers are often included at the reception stage, at the expense of increasing the computational load and complexity of the resulting receiver. This article proposes a hardware/software (HW/SW) architecture for the real-time implementation of an FBMC transmultiplexer with a channel estimator and an L-Adaptive sine/cosine modulated filter bank equalizer for transmultiplexers (L-ASCET) equalizer. The FBMC transmultiplexer and the L-ASCET equalizer are implemented in HW, whereas the channel estimation and the determination of the equalizer coefficients are specified in SW, all forming a system-on-chip architecture. The performance provided by the proposal has been evaluated in terms of the signal-to-noise ratio, the root-mean-square error, and the bit error rate.

Power-Line Communications have spread worldwide in recent years, mainly due to their increasing importance as a feasible alternative to provide broadband data access in certain domains, such as Smart Grids, Internet of Things or industrial environments in general, where the mains are available. Broadband PLC implies significant computational requirements, often related to multi-carrier modulations and high data rates that should be managed in parallel. Furthermore, these data rates also involve the necessity of specific analog front-ends (AFE), capable of tackling the corresponding high sampling frequencies in A/D and D/A converters. This work describes the design of an FPGA-based (Field-Programmable Gate Array) architecture, in charge of managing an ad-hoc AFE for broadband PLC. The proposal also implements a Filter-Bank Multi-Carrier (FBMC) modulation as medium access technique and a synchronism based on pilot sequences. A dedicated peripheral has been designed for that purpose, which has been integrated in a System-on-Chip (SoC). The proposal has been verified experimentally, validating the expected functionality.

Multi-carrier modulations used in Power-Line Communications (PLC), such as Filter-Bank Multi-Carrier (FBMC), allow broadband links and improve communications through the PLC channel, at the expense of adding complexity to the system. Although they perform suitably in most cases, the main drawback is often the PLC channel, noisy and with significant interferences. To compensate these adverse effects introduced by the channel, the reception stage includes a channel estimator and an equalizer, which also implies a higher computational load and complexity of the system at this stage, becoming difficult to achieve feasible architectures for real-time implementations. This work proposes an efficient dual-core solution to carry out the channel estimation and calculation of ASCET coefficients in the ARM processor of the Zynq-7000 System-on-Chip (SoC), with the objective of studying the feasibility of reaching a hardware/software architecture for the implementation of an FBMC transmultiplexer with channel estimator and equalizer. Preliminary results shown here include the evaluation of the time consumed by each part of the algorithm, and present the acceleration obtained by the dual-core proposal, depending on the order of L-ASCET equalizer considered.

Three-dimension infrared positioning systems are a feasible alternative on indoor local positioning systems, where those based on photodetectors are the most typically used in order not to have complex processing algorithms but a fast positioning computation. Optical positioning systems are often characterized by their low cost, low lifetime, and easy integration on the workplace. This work proposes an infrared positioning system based on four infrared LEDs and a QADA receiver. By applying encoding techniques to the infrared transmissions, the points of incidence from those transmitters on the QADA receiver are simultaneously obtained and the polar angle compensated, in order to finally estimate the receiver's position. The geometrical considerations of the system have been derived, including the polar angle and its behaviour with regard to the receiver's position, the angle of incidence and the aperture height. The proposal has been successfully validated by simulation and experimental tests, obtaining positioning errors below 10 cm and errors in the estimation of the polar angle of 0.9oin a 2 × 2 × 2 m volume.

Acoustic local positioning systems (ALPSs) are an interesting alternative for indoor positioning due to certain advantages over other approaches, including their relatively high accuracy, low cost, and room-level signal propagation. Centimeter-level or fine-grained indoor positioning can be an asset for robot navigation, guiding a person to, for instance, a particular piece in a museum or to a specific product in a shop, targeted advertising, or augmented reality. In airborne system applications, acoustic positioning can be based on using opportunistic signals or sounds produced by the person or object to be located (e.g., noise from appliances or the speech from a speaker) or from encoded emission beacons (or anchors) specifically designed for this purpose. This work presents a review of the different challenges that designers of systems based on encoded emission beacons must address in order to achieve suitable performance. At low-level processing, the waveform design (coding and modulation) and the processing of the received signal are key factors to address such drawbacks as multipath propagation, multiple-access interference, near-far effect, or Doppler shifting. With regards to high-level system design, the issues to be addressed are related to the distribution of beacons, ease of deployment, and calibration and positioning algorithms, including the possible fusion of information obtained from maps and onboard sensors. Apart from theoretical discussions, this work also includes the description of an ALPS that was implemented, installed in a large area and tested for mobile robot navigation. In addition to practical interest for real applications, airborne ALPSs can also be used as an excellent platform to test complex algorithms (taking advantage of the low sampling frequency required), which can be subsequently adapted for other positioning systems, such as underwater acoustic systems or ultrawideband radio-frequency (UWB RF) systems.

This paper describes the design, measurement methodology, and experimental validation of an electronic system for monitoring the electrical discontinuity in rails of double-track railway lines in order to detect the rail breakage. The electronic system is based on an emitter node and on the two receiver nodes. The three nodes, together with the four rails to be analyzed, constitute the electrical circuits, whose currents are measured in both the emission and reception nodes. These nodes can communicate through a data network between them, as well as with a remote center that controls and synchronizes the electronic systems along the railway line. The electronic subsystem associated with the emitter node transmits an encoded signal to the rails, modulated by an 800-Hz carrier. A set of eight currents are measured, one per rail at each end of the section defined between the emitter and the receiver, with a maximum length of 7 km. The evaluation of electrical discontinuities in each section is carried out by means of the relative imbalances that can appear between the eight measured currents, according to possible breakages. The method of measurement is characterized by a high immunity to noise. The results presented here are based on a railway line prototype, which implements the electrical propagation models and coupling between the four rails in the double track. This model allows the proposed electronic system and measurement methodology to be successfully verified.

Non-Intrusive Load Monitoring System (NILM) is a collection of techniques and technologies aiming to disaggregate the energy consumption from smart meters to device level consumption. This disaggregation allows us to supervise the activity related to particular devices (e.g. machines in factories or appliances in houses) without intrusiveness along the time. Furthermore, NILM systems enable large scale deployment and low cost expenses. In this paper, we present a new activity monitoring approach based on the use of a NILM system, firstly developed for elderly people activity monitoring at home, which could be potentially applicable to factory automation to supervise the activity of a particular machine for maintenance or other purposes. Our approach monitors the activity of many appliances in households and rates the total activity in terms of “normality” in a predetermined period of time, hence, deviations in the pattern can be detected.

In recent years, communications systems through mains, known as PLC (Powerline Communication), have received a lot of interest as part of intelligent networks or smart grids, as well as for multimedia applications at homes. In broadband PLC, systems using multicarrier modulations allow the mitigation of selective frequency fading in the channel. Nevertheless, these modulations require accurate time synchronization for correct operation. This paper proposes a robust temporal synchronization algorithm based on multilevel complementary sequences for broadband PLC channel with selective frequency fading, providing improvements around 80% in RMSE (Root Mean Squared Error) with respect to the common metrics used in OFDM (Orthogonal Frequency Division Multiplexing), where multipath effect is not considered.

This paper proposes a software android application to compute the absolute position of smartphones or tablets in an indoor Ultrasonic Local Positioning System (U-LPS) based on hyperbolic trilateration. U-LPS can enhance mobile devices indoor positioning allowing sub-meter accuracy results by a suitable encoding of the ultrasonic transmissions. An external hardware device based on an ultrasonic microphone and an acquisition system can digitize the signals coming from the U-LPS and send them to the mobile device. Once in the device, by means of correlation techniques, time difference of arrival (TDOA) can be computed to obtain the device position and offer a wide variety of Location Based Services (LBS) to the user (building navigation, augmented reality...). Nevertheless, the correlation and positioning algorithms imply a high computational load. An Android application is presented to positioning mobile devices. It can be configured in real-time for different encoding schemes and allows to manage the reception of data coming from an external acquisition module and perform the low level (demodulation, correlation and peak detection) and high level (TDOA computation and hyperbolic trilateration) tasks. The proposed algorithms have been validated in an indoor U-LPS.

Multi-carrier approaches have arisen in the last years as a suitable medium access technique for many communications standards, such as PowerLine Communications (PLC). This one is based on using mains for data transmission, not only in industrial environments but also in public buildings, residences and houses. One of these multi-carrier solutions is based on the Filter-Bank Multi-Carrier (FBMC), where a polyphase filter bank is used to obtain greater spectral separation in the information transmitted over each subcarrier, or higher robustness in noisy environments. Nevertheless, these techniques often imply a high computational load, with also high data rates, which make difficult to achieve real-time implementations for the approaches. This work analyses three different architectures that can be applied to the polyphase filter bank implementation in a FBMC technique for PLC. Every proposal is studied in terms of quantization errors, due to fixed-point representation, and in terms of resource consumption, always taking into account that a FPGA (Field-Programmable Gate Array) device has been considered for the final deployment.

Within the emerging new technologies in Smart Grids, the concern about Non-Intrusive Load Monitoring (NILM) techniques has increased over the last five years. These techniques aim to disaggregate the energy consumption on a household or commercial building into individual appliances from the use of a single-point sensor. Most efforts attempt to disaggregate by using a low sampling frequency and by focusing on steady consumption states. Nevertheless, to achieve a better accuracy, it is necessary a higher sampling frequency and to look up both steady states and transient states. This paper proposes a novel event-based detector that could be used in NILM systems in a previous stage in order to detect transient states and help to the labelling task. This could lead to a better performance, specially taking into account small power changes which are not usually well detected on most low-frequency systems. It is based on the use of Hilbert Transform to obtain the envelope of the signal and the use of an average and a derivate filter to obtain a set of spikes that characterize each transition. The performance has been tested using a set of known metrics obtaining good results in two cases: sampling at 1kHz, which the aim of a possible integration in smart meters; and in a real scenario where 20 different appliance-loads are connected to the power main.

This paper presents the design and implementation of a System-on-Chip (SoC) based on a FPGA (Field-Programmable Gate Array) device to control and configure an ultrasonic beacon array for an extensive ultrasonic Local Positioning System (LPS). The SoC is based on a MicroBlaze microprocessor and some advanced peripherals. Among them, it is possible to remark a LPS controller, in charge of managing the ultrasonic transmissions from the LPS beacons. Furthermore, the SoC can be connected to a router Wi-Fi, in order to obtain the desired configuration for the LPS. This allows to provide a large flexibility to the LPS, since it is possible to modify different parameters, as the carrier frequency, the transmission lengths, the type of modulation, or the sequences used to encode the ultrasonic emissions.

Authors propose with the real-time implementation of a transmultiplexer based on an embedded filter bank as a medium access technique for multicarrier communications, such as in broadband PLC (power-line communications). For that purpose, the signal processing algorithms have been analyzed, not only to determine the corresponding requirements, but also to optimize them for a suitable implementation. The definition of an efficient SoC-based architecture to achieve real-time performance has been carried out. The proposed architecture has been optimized in terms of datawidth, data rates and frequencies, as well as resource consumption.

This work benefits from the capability of the iPhone's or iPad's microphone to acquire high-frequency sound for accurate acoustic code identification. Although the maximum theoretical value for the frequency response of the built-in iOS device microphone is 20 kHz, emissions with frequencies close to 22 kHz have been experimentally detected. The frequencies used in this work are in the range from 18 to 22 kHz, which are high enough to be inaudible for almost every people but low enough to be generated by standard sound hardware. The aim of this work is to develop an inexpensive indoor positioning system where the user gets its location by using an iOS device's microphone. For this purpose a third-generation iPad is used for high-frequency sound data acquisition and neither external acquisition system nor ultrasonic microphone are required. The capability of iOS devices to acquire acoustic signals in the vicinity of 20 kHz has been successfully demonstrated. This fact allows the use of this kind of device for acoustic code detection and accurate positioning by means of multilateration.

The development of Local Positioning Systems (LPSs), that allow user positioning in areas where solutions based on GNSS (Global Navigation Satellite System) fail (as indoor environments), has been an active research field in the last years. In LPSs based on Ultra-Wideband (UWB), near-far effect is a significant issue that severely constrains the system performance. In this work, we present a novel UWB LPS based on CDMA (Code Division Multiple Access) and Loosely Synchronized sequences and we compare their performance with an UWB LPS based on TDMA (Time Division Multiple Access), which is a multiple access scheme commonly used to cope with near-far effect. The first approach provides the benefits of simultaneous access, thus minimizing hardware requirements and near-far effect.

The current stage of railway transportation systems must deal with increased safety and reliability issues. A key point is improving occupancy track circuit performance and providing them with redundancy, higher noise immunity, and the capability to acquire additional information about the track section involved. This work proposes a novel track circuit based on the encoding of the electrical transmissions with Kasami codes. Track circuit emitters send signals coded with a known sequence that can be identified by the corresponding receivers using correlation techniques; these processes increase immunity to noise and changes in environmental conditions. An appropriate selection of orthogonal sequences for encoding, as well as different carrier frequencies for transmissions, allow simultaneous emissions and receptions without cross interference.

A method to locate reflectors, based on the spectral analysis of ultrasonic received echoes, is provided in this study. The main contribution of this work lies in the spectral analysis for bearing estimation. A single ultrasonic transducer, acting as emitter and receiver, is used. The transmitted signal is a 1023-bits Kasami code BPSK modulated by a carrier that ranges from 30 kHz to 55 kHz, implying a 6-component transmitted pulse. Since the ultrasonic transducer works as an angle-dependent filter, it is possible to estimate the bearing angle of the reflector based on the received spectrum. It is shown how the amplitude of each frequency contains information about the environment, particularly about the bearing angle of the reflector. Finally, a set of experimental results is included to validate the sensory system, showing how it can deal with realistic reflectors.

On current railway systems, it is becoming ever more necessary to install safety elements to avoid accidents. One of the causes that can provoke serious accidents is the existence of obstacles on the tracks, either fixed or mobile. In this paper, a multisensory system that can inform the monitoring system about the existence of obstacles is proposed. The system for obstacle detection consists of two emitting and receiving barriers, which are placed on opposing sides of the railway, respectively, and use infrared and ultrasonic sensors, thus establishing different optical and acoustic links between them. The interruption of one or several links should produce an alarm. However, even without the existence of objects, degradation of links could occur due to atmospheric attenuation, solar radiation, etc., also producing an activation of the alarm system. Since detection is based on the lack of radiation in the detectors, the use of complementary sensors for the same task is justified. Since the minimum size of an object for which an alarm is required to be generated is 50 × 50 × 50 cm, in some situations, several links are interrupted; however, alarms should not be generated. Typical cases are the flight of leaves or the movement of small animals in the scanned area. To avoid alarm activation in such situations, this paper proposes the combined use of diverse techniques of data fusion, based on fuzzy logic and the Dempster-Shafer theory of evidence, to validate the existence of objects, providing a highly reliable detection system.

Ultra-wideband (UWB) technology is currently considered the most attractive solution to develop a Local Positioning System. In fact, the IEEE 802.15.4a standard includes a link (MAC) and physical (PHY) layer with indoor positioning capabilities. This paper presents a study of the typical modulations schemes used with UWB and its adaptation to ultrasonic frequencies. Moreover, several tests of the modulation schemes in the ultrasonic frequency range are shown, in order to apply these techniques in a local positioning system. The main goal is to implement the algorithms in FPGAs using UWB radio technology. Therefore, the use of ultrasonic frequencies can be considered as a previous step of the future work.

The current development of railway transport gives a significant importance to the design of suitable and reliable safety systems. Among them, the common track circuit can be improved to enhance its performance, by providing redundancy, immunity to noise and more information from the track circuit. This work proposes a novel track circuit based on the encoding of the electrical transmissions with Kasami codes. Every node emits its corresponding code that can be detected at next nodes by correlation techniques. The suitable selection of orthogonal sequences, as well as different carrier frequencies for emissions, allow to have simultaneous emissions and receptions without cross interferences.

Loosely Synchronous (LS) codes improves the performance of Local Positioning Systems (LPS) in terms of noise immunity, capability of simultaneous measurements and precision in the location, provided that the multipath spread and the relative time offset between the codes are within the zero correlation zone (ZCZ) that they achieve in their correlation functions. Furthermore, these codes can be very efficiently generated and correlated if compared to a straightforward implementation. This work presents the hardware implementation of the correlation tasks associated to an ultrasonic LPS based on LS sequences generated from Complementary Sets of Sequences (CSS). This implementation is able to carry out all the correlation processing tasks in a time shorter than the sampling period.

In this work the specific software tools designed to manage a high performance data acquisition board to be used in active multi-sensory systems is presented. Specifically, the developments which has been carried out allow to monitor the coded emission/reception of two sensory barriers for obstacle detection on railways, based on infrared (IR) and ultrasonic (US) sensors. Additionally, it has been developed the tools to detect the coded emissions. As a result of that, it can be detected the existence of obstacles between both barriers, based on the lack of signal at the receiving barrier. The developed software can be considered the previous step to implement the hardware prototype. Initially it has been validated the feasibility of the algorithm, optimizing the processing time to obtain the detection results in quasi-real time.

In local positioning systems (LPS) active beacons are placed in the environment, so the accurate coordinates of these beacons are necessary for positioning algorithms. Such coordinates or positions can be obtained by means of hand-made measurements (a slow and less flexible method), or by using calibration algorithms (i.e., automatic determination of the beacon coordinates from pre-determined measurements). In this work a new method is presented to estimate the optimal position of the test-points, necessary to calibrate an ultrasonic LPS. The method has been developed for both, spherical and hyperbolic trilateration. The results from simulated and real data shows the improvement obtained when using in the calibration these optimal test-points instead of randomly selected ones.

This work proposes a data fusion technique to validate the detection of objects in railway environments. The system for obstacle detection is based on an infrared emitting barrier and another of receivers placed on opposing side in the railway, establishing different optical links between them. The interruption of one or several links in the sensorial system should produce an alarm. Since the detection is based on the radiation lack in the detectors, channel degradation can produce a similar situation as if there was one obstacle. Since the interruption of the links is the necessary condition, but not sufficient, for the existence of an object, this work proposes the fusion of the sensors information by means of the Dempster-Shafer evidential theory to obtain a certainty value of the existence of objects supposing a risk for the railway.

The determination of the relative position among mobile objects or members of a robot team is a useful information when the systems are not restricted to a particular environment and they cannot depend on an external infrastructure to obtain their location information. The solution for this kind of problems cannot be divided and treated separately for each object. Therefore it is necessary to implement mechanisms that allow to simultaneously determine the spatial relations among objects not knowing their absolute location in the environment. After that, a positioning algorithm can be computed whose complexity depends on the number of observations made and the precision required in the system. In this work, the implementation of a relative positioning system is described where only acoustic emissions are made in order to obtain information about the other objects. These data are obtained by using simultaneous round-trip-time-of-flight measurements. Finally, using this information, the object positions are computed with a multidimensional scaling technique, and a closer solution is achieved with least-square algorithms.

In this work a processing algorithm is presented to obtain the impulse response of the transmission channel in a MIMO ultrasonic system. In this algorithm the macro-sequences (Ms) obtained from complementary sets of M sequences are used to obtain the impulse response of the environment. The signals received by the sensorial system are demodulated and correlated to find the transmitted signals. It is possible to obtain more detailed information from the environment through the analysis carried out over the correlated signal to extract the impulse response of the environment. In order to eliminate the cross correlation among the Ms and improve the localization of the correlation peaks, a post-process algorithm is applied to the correlated signal to find them with more accurately. Finally, a matrix of impulse responses of the transmission channels can be recovered by using the correlation peaks.

Kasami sequences have been successfully used in communications, navigation and related systems due to their low cross-correlation values, compared to those from other binary sequences. In this work, different alternatives for the hardware implementation of a correlator of Kasami sequences are presented: for short sequences a combinational design is proposed, whereas three sequential designs are suggested for longer Kasami sequences. These three sequential designs differ about the management of the memory: one stores the necessary data in slices of the FPGA; another uses external memory; and finally, the last one uses the internal RAM blocks in the FPGA.

Signal coding and pulse compression techniques have been widely used in the design of high performance airborne sonar as a mean to increase their robustness to noise and enhance their spatial resolution. These systems are still today in continuous development, with the appearance of new encoding schemes and processing algorithms which not only improve the previous features, but also provide the system with additional properties. All these improvements are achieved at the expense of increasing the computational complexity of the signal processing tasks, what put into risk the real-time operation capability of the system. This work presents the hardware implementation in a single FPGA of all the stages forming the detection module of an advanced sonar system which is based on the emission of complementary sets of sequences. This implementation is able to carry out all the data processing in the time interval between two successive firings, also obtaining a portable module that makes possible its incorporation into mobile robots

The use of complementary sets of sequences is especially relevant in multisensorial systems, with simultaneous emissions from different sources and low signal-to-noise ratio (SNR). There exist efficient generation and correlation algorithms that give a reduction in the computational load and complexity of a hardware implementation. In this work a new hardware design approach for an efficient correlator of complementary sets of sequences (CCS) is presented. It is based on utilising reconfigurable logic to gain the required flexibility in terms of correlator parameters. The configuration of the design is carried out at the pre-synthesis stage, so the user can change this configuration according to the requirements of the application

This paper presents a reflector recognition and localization technique in three-dimensional (3-D) environments, using only times-of-flight (TOFs) data obtained from ultrasonic transducers. The recognition and localization technique is based on the principal component analysis applied to the TOF vectors originating from a sensor that contains two emitting transducers and several receivers. The two emitters simultaneously transmit two coded pulses that are detected later on and discriminated by the receivers, after being reflected in the environment. The proposed technique allows for the possibility of not only recognizing the reflectors, but also estimating approximately its localization referred to the sensor. This technique has been tested with three types of reflectors in 3-D environments: planes, edges, and corners. The achieved results are very satisfactory for reflectors located in the range 50-350 cm.

This work shows different proposals to implement generic convolution with images from a vision camera. The developed algorithms present different methods to perform convolutions using VHDL language. The main problem of this operation is the high number of mathematical operations; numerous multiplications, additions and divisions are needed. This cause has led to create design electronic circuits, whose structure is based on internal FPGA resources (field programmable gate array). So the obtained result has been optimised in terms of speed and required resources in circuits. These kinds of generic operations are often used in artificial vision, as a pre-processing stage for images. This step precedes image processing.

The detection of the passage of railway units by certain points of the railway line (mainly in the junctions or bifurcations of the routes), of reliable way, is a functionality of great importance for the safe management of the circulation. Most of the existing systems to that end bases of its operation on the detection of the disturbance that undergoes a magnetic flux (generated by means of coils) to the passage of the wheel of the train. With the system proposed here, we make this detection with the coils located next to the rail of the route, but without additional electronics located in this place. All the electronic systems are concentrated in a point that can be to a considerable distance from the rail. This makes easier the tasks of maintenance of the equipment, in addition to protecting it from the noises and aggressions of the environment. In order to compensate the attenuation of signal in the wires and the external noise, we propose a codification of the signal sent to the emitting coil by means of pairs of Golay complementary sequences.

Recent sonar modules applied to autonomous robots usually consist of several transducers, associated geometrically and electronically to improve performances and results, compared to systems based on only one isolated transducer. Nevertheless, in order to take advantage of these associations, it is necessary to develop new algorithms to consider suitably all the information given by times-of-flight (TOFs) from transducers. This work describes a new method to analyze these TOFs, so they can be used to obtain a heuristic map of the environment. A new operation mode is also proposed to allow the system adaptation to different environmental conditions in run-time.

This paper presents an ultrasonic sensor made up of several transducers located on various different horizontal planes, designed for navigation and localization applications in the mobile robot (MR) field. The sensor proposed is equipped with two emitters and fourteen receivers. The times-of-flight (OTF) provided by the transducers enable the tree types of basic reflectors (planes, corners and edges) to be classified. Optimal classification of the reflectors will facilitate both localization of the mobile robot and the generation of environment maps that are useful for navigation tasks. The innovation with this sensor as regards prior papers in this field lies in the high degree of redundancy that it provides, which greatly facilitates the detection of erroneous measurements caused by false reflections, and in the simplicity of the classification algorithm used, which gives it a low computational load.

This work deals with the implementation of the discrete 2D wavelet transform in VHDL. The proposed system has as advantages the segmentation of the wavelet algorithm in different processes and presents as novelty the control and addressing of input and output data. After introducing the key concepts of wavelet filtering the algorithm of wavelet transform is shown. Next, the functional simulation of the different blocks is described. The control algorithm capable to address the input data correctly and the state machine used to follow the evolution of the whole system are studied. One vertical processor and two horizontal processors in parallel are used to filter the image. Lastly, it becomes necessary a stage of output data reordering to obtain the image with the standard format. Results and applications are given.