LiDAR-Based Maintenance of a Safe Distance between a Human and a Robot Arm.

This paper demonstrates the capabilities of three-dimensional (3D) LiDAR scanners in supporting a safe distance maintenance functionality in human-robot collaborative applications. The use of such sensors is severely under-utilised in collaborative work with heavy-duty robots. However, even with a relatively modest proprietary 3D sensor prototype, a respectable level of safety has been achieved, which should encourage the development of such applications in the future. Its associated intelligent control system (ICS) is presented, as well as the sensor's technical characteristics. It acquires the positions of the robot and the human periodically, predicts their positions in the near future optionally, and adjusts the robot's speed to keep its distance from the human above the protective separation distance. The main novelty is the possibility to load an instance of the robot programme into the ICS, which then precomputes the future position and pose of the robot. Higher accuracy and safety are provided, in comparison to traditional predictions from known real-time and near-past positions and poses. The use of a 3D LiDAR scanner in a speed and separation monitoring application and, particularly, its specific placing, are also innovative and advantageous. The system was validated by analysing videos taken by the reference validation camera visually, which confirmed its safe operation in reasonably limited ranges of robot and human speeds.

Human Gait Activity Recognition Machine Learning Methods.

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject's quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm's robustness was also verified with the successful detection of freezing gait episodes in a Parkinson's disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.

Capturing Conversational Gestures for Embodied Conversational Agents Using an Optimized Kaneda-Lucas-Tomasi Tracker and Denavit-Hartenberg-Based Kinematic Model.

In order to recreate viable and human-like conversational responses, the artificial entity, i.e., an embodied conversational agent, must express correlated speech (verbal) and gestures (non-verbal) responses in spoken social interaction. Most of the existing frameworks focus on intent planning and behavior planning. The realization, however, is left to a limited set of static 3D representations of conversational expressions. In addition to functional and semantic synchrony between verbal and non-verbal signals, the final believability of the displayed expression is sculpted by the physical realization of non-verbal expressions. A major challenge of most conversational systems capable of reproducing gestures is the diversity in expressiveness. In this paper, we propose a method for capturing gestures automatically from videos and transforming them into 3D representations stored as part of the conversational agent's repository of motor skills. The main advantage of the proposed method is ensuring the naturalness of the embodied conversational agent's gestures, which results in a higher quality of human-computer interaction. The method is based on a Kanade-Lucas-Tomasi tracker, a Savitzky-Golay filter, a Denavit-Hartenberg-based kinematic model and the EVA framework. Furthermore, we designed an objective method based on cosine similarity instead of a subjective evaluation of synthesized movement. The proposed method resulted in a 96% similarity.

A Pot-Like Vibrational Microfluidic Rotational Motor.

Constructing a micro-sized microfluidic motor always involves the problem of how to transfer the mechanical energy out of the motor. The paper presents several experiments with pot-like microfluidic rotational motor structures driven by two perpendicular sine and cosine vibrations with amplitudes around 10 µm in the frequency region from 200 Hz to 500 Hz. The extensive theoretical research based on the mathematical model of the liquid streaming in a pot-like structure was the base for the successful real-life laboratory application of a microfluidic rotational motor. The final microfluidic motor structure allowed transferring the rotational mechanical energy out of the motor with a central axis. The main practical challenge of the research was to find the proper balance between the torque, due to friction in the bearings and the motor's maximal torque. The presented motor, with sizes 1 mm by 0.6 mm, reached the maximal rotational speed in both directions between -15 rad/s to +14 rad/s, with the estimated maximal torque of 0.1 pNm. The measured frequency characteristics of vibration amplitudes and phase angle between the directions of both vibrational amplitudes and rotational speed of the motor rotor against frequency of vibrations, allowed us to understand how to build the pot-like microfluidic rotational motor.

Preparation and Characterisation of Waste Poultry Feathers Composite Fibreboards.

The growth of poultry meat production is increasing industrial waste quantities every year. Feathers represent a huge part of the waste, and international directives and restrictions prevent landfilling of such biodegradable materials with high burning values. Furthermore, with their unique properties, poultry waste feathers are already a reliable resource for many byproducts, such as keratin extraction, fibres, hydrogel production, etc., all trying to achieve a high-added value. However, mass reduction of waste feathers into useful applications, such as development of alternative building materials, is also an important aspect. To take advantage of feathers' thermal insulation capabilities, sound damping, and biodegradability, we worked towards mixing waste feathers with wood residues (wood shavings, dust, and mixed residues) for production of composite fibreboards, comparable to the market's medium density fibreboards. The emphasis was to evaluate waste poultry feathers as the component of natural insulation composites, along with mixed waste wood residues, to improve their mechanical properties. Various composite fibreboards with different shares of wood and feathers were produced and tested for mechanical, thermal, and acoustic properties, and biodegradability, with comparison to typical particle boards on the market. The addition of waste feather fibres into the fibreboards' structure improved thermal insulation properties, and the biodegradability of fibreboards, but decreased their bending strength. The sound transition acoustic loss results of the presented combination fibreboards with added feathers improved at mid and high frequencies. Finally, production costs are estimated based on small scale laboratory experiments of feather processing (cleaning and drying), with the assumption of cost reduction in cases of large industrial application.

A Microfluidic Rotational Motor Driven by Circular Vibrations.

Constructing micro-sized machines always involves the problem of how to bring the energy (electric, magnetic, light, electro wetting, vibrational, etc.) source to the device to produce mechanical movements. The paper presents a rotational micro-sized motor (the diameter of the rotor is 350 µm) driven by low frequency (200-700 Hz) circular vibrations, made by two piezoelectric actuators, through the medium of a water droplet with diameter of 1 mm (volume 3.6 µL). The theoretical model presents how to produce the circular streaming (rotation) of the liquid around an infinitely long pillar with micro-sized diameter. The practical application has been focused to make a time-stable circular stream of the medium around the finite long vibrated pillar with diameter of 80 µm in the presence of disturbances produced by the vibrated plate where the pillar is placed. Only the time-stable circular stream in the water droplet around the pillar produces enough energy to rotate the micro-sized rotor. The rotational speed of the rotor is controlled in both directions from -20 rad/s to +26 rad/s. 3D printed mechanical amplifiers of vibrations, driven by piezoelectric actuators, amplify the amplitude of the piezoelectric actuator up to 20 µm in the frequency region of 200 to 700 Hz.

From municipal/industrial wastewater sludge and FOG to fertilizer: A proposal for economic sustainable sludge management.

After a ban on the depositing of untreated sludge in landfills, the sludge from municipal and industrial water-treatment plants can be regarded as a problem. Waste products of the water treatment process can be a problem or an opportunity - a source for obtaining raw materials. In the European Union, raw sludge and fats, oil and grease (FOG) from municipal and industrial wastewater treatment plants (WWTP) cannot be deposited in any natural or controlled environment. For this reason, it must be processed (stabilized, dried) to be used later as a fertilizer, building material, or alternative fuel source suitable for co-incineration in high temperature furnaces (power plants or concrete plants). The processes of drying sludge, where heat and electricity are used, are energy consuming and economically unattractive. Beside energy efficiency, the main problem of sludge drying is in its variability of quality as a raw material. In addition to this, sludge can be contaminated by a number of organic and inorganic pollutants and organisms. Due to the presence or absence of pollutants, different end products can be economically interesting. For example, if the dried sludge contains coliform bacteria, viruses, helminths eggs or smaller quantities of heavy metals, it cannot be used as a fertilizer but can still be used as a fuel. The objectives of the current article is to present a batch-processing pilot device of sludge or digestate that allows the following: (1) low pressure and low temperature energy effective drying of from 10 to 40% remaining water content, (2) disinfection of pathogen (micro)organisms, (3) heavy metal reduction, (4) production of products of predetermined quality (e.g. containing different quantities of water; it can be used as a fertilizer, or if the percentage of water in the dry sludge is decreased to 10%, then the dried sludge can be used as a fuel with a calorific value similar to coal). An important feature is also the utilization of low-pressure technology to prevent odorous gasses from spreading into the environment. There are presented two new technologies: a) Sewage sludge or digestate drying in the vacuum chamber consumes approx. 1 kWh/dm3 of evaporated water and, therefore, reaches a price of 180-240 Euros/t Dry Matter (DM), and b) Heavy metals' reduction using adsorbing reaction with magnetite nanostructures can decrease the level of heavy metals in the sewage sludge or digestate up to 20% in one cycle, which can be repeated several times on the same sludge. The aim of the paper is to present a newly developed technology which can provide economic and safe use of moderate heavy metals polluted sewage sludge on agricultural lands as organic fertilizer and, therefore, returning the nutrients (nitrogen, phosphorous, potassium) back to the human food chain, instead of being incinerated or landfilled. The proposed drying technology is economically sustainable due to the low vacuum and temperature (35 °C-40 °C), that increases the efficiency of the heat pump (coefficient of performance 5-7,2) of the energy produced by the anaerobic digestion. Hence, the main emphasis is given to the development of: an efficient method for heavy metals' reduction in the sludge treatment chain by using chitosan covered magnetite nanoparticles, an efficient drying method in a vacuum with low temperature energy which can be exploited from sludge digestion to reduce organic matter, and an energy sustainable concept of sludge treatment, with the addition of fats, oil and grease (FOG) to produce enough biogas for sludge drying to produce fertilizer.

Major improvements of quartz crystal pulling sensitivity and linearity using series reactance.

This paper presents a new method of substantially improving frequency pullability and linearity using reactance in series with an AT fundamental crystal operated with a series load capacitance in the range of 3 to 50 pF and frequencies in the range of 3.5 to 21 MHz. The research describes high quartz pullability and linearity by varying the load capacitance. The paper also gives impedance circuits for crystal unit (3.5 MHz) together with load capacitance and compensation reactance. The experimental results show that the new approach using compensation method of quartz crystal connected in series reactance increases the frequency pulling range by ×25 to ×100 depending on the type of oscillator and compensation factor "k" in the temperature range of 10 to 40 °C.