Development of Foreign Material Detection in Food Sensor Using Electrical Resistance Technique

Food inspection has been a serious matter in the food industry as the contamination by foreign materials such as metals, bone, plastics and glass in food plays a major impact on the industry. In spite of a great deal of effort to prevent mixing foreign bodies in food materials, food manufacturers have still not been able to detect them. Electrical Resistance Sensor to detect the foreign material in food detection sensor is constructed and presented in this work. This project focuses on how to design and implement the system to detect and distinguish between food and foreign material using resistance concept. The electrode plate of Electrical Resistance Sensor (ERS) is designed using COMSOL Multiphysics Software to see the electric field and contour of the electric potential of the system. The resistance value from the sensor is measured based on the AC Circuit concept. The alternating current from the sensor flows to the charge detector circuit providing the voltage corresponding to the resistance between the electrode pair. The voltage from the charge detector circuit has been amplified by the amplifier circuit to obtained DC output from an AC input signal. The voltage form circuit has been converted from the analog to digital signal using Bluetooth Electronics Application via Arduino Uno through HC-05 Bluetooth module. The Bluetooth Electronics Application is used as a graphical user interface (GUI) to display the condition of the material tested including food and foreign material to a smartphone.  The experiment results show that the electrical resistance sensor are able to detect the foreign material in food by changes of the resistance value. If the food was detected with the foreign material (non-conductive), the value of resistance will decrease due to the flow of electric current

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Electrostatic Sensors – Their Principles and Applications

Over the past three decades electrostatic sensors have been proposed, developed and utilised for the continuous monitoring and measurement of a range of industrial processes, mechanical systems and clinical environments. Electrostatic sensors enjoy simplicity in structure, cost-effectiveness and suitability for a wide range of installation conditions. They either provide unique solutions to some measurement challenges or offer more cost-effective options to the more established sensors such as those based on acoustic, capacitive, optical and electromagnetic principles. The established or potential applications of electrostatic sensors appear wide ranging, but the underlining sensing principle and resultant system characteristics are very similar. This paper presents a comprehensive review of the electrostatic sensors and sensing systems that have been developed for the measurement and monitoring of a range of process variables and conditions. These include the flow measurement of pneumatically conveyed solids, measurement of particulate emissions, monitoring of fluidised beds, on-line particle sizing, burner flame monitoring, speed and radial vibration measurement of mechanical systems, and condition monitoring of power transmission belts, mechanical wear, and human activities. The fundamental sensing principles together with the advantages and limitations of electrostatic sensors for a given area of applications are also introduced. The technology readiness level for each area of applications is identified and commented. Trends and future development of electrostatic sensors, their signal conditioning electronics, signal processing methods as well as possible new applications are also discussed

A microfluidics-integrated impedance/surface acoustic resonance tandem sensor

We demonstrate a dual sensor concept for lab-on-a-chip in-liquid sensing through integration of surface acoustic wave resonance (SAR) sensing with electrochemical impedance spectroscopy (EIS) in a single device. In this concept, the EIS is integrated within the building blocks of the SAR sensor, but features a separate electrical port. The two-port sensor was designed, fabricated, and embedded in a soft polymer microfluidic delivery system, and subsequently characterized. The SAR-EIS tandem sensor features low cross-talk between SAR and EIS ports, thus promoting non-interfering gravimetric and impedimetric measurements. The EIS was characterized by means of the modified Randle\u27s cell lumped element model. Four sensitive parameters could be established from the tandem sensor readout, and subsequently employed in a proof of principle study of liposome layers and their interaction with Ca2+ ions, leading to transformation into molecular film structures. The associated shift of the sensing quantities is analysed and discussed. The combination of impedimetric and gravimetric sensing quantities provides a unique and detailed description of physicochemical surface phenomena as compared to a single mode sensing routine

The synthesis of multisensor non-destructive testing of civil engineering structural elements with the use of clustering methods

In the thesis, clustering-based image fusion of multi-sensor non-destructive (NDT) data is studied. Several hard and fuzzy clustering algorithms are analysed and implemented both at the pixel and feature level fusion. Image fusion of ground penetrating radar (GPR) and infrared\ud thermography (IRT) data is applied on concrete specimens with inbuilt artificial defects, as well as on masonry specimens where defects such as plaster delamination and structural cracking were generated through a shear test. We show that on concrete, the GK clustering algorithm exhibits the best performance since it is not limited to the detection of spherical clusters as are the FCM and PFCM algorithms. We also prove that clustering-based fusion outperforms supervised fusion, especially in situations with very limited knowledge about the material properties\ud and depths of the defects. Complementary use of GPR and IRT on multi-leaf masonry walls enabled the detection of the walls’ morphology, texture, as well as plaster delamination\ud and structural cracking. For improved detection of the latter two, we propose using data fusion at the pixel level for data segmentation. In addition to defect detection, the effect of moisture is analysed on masonry using GPR, ultrasonic and complex resistivity tomographies. Within the\ud thesis, clustering is also successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system. Besides, the classification of spectroscopic spatial data from concrete is taken under consideration. In both applications, clustering is used for unsupervised segmentation of data

Electronic hardware design of a low cost tactile sensor device for physical Human-Robot Interactions

International audienceWe propose in this paper a low-cost method of Electrical Impedance Tomography (EIT) data acquisition from soft conductive fabric for the design of robots artificial skin. We use a simple multiplexer/demultiplexer circuit for retrieving the resistance field from the pair-wised electrical current injected and the output voltage measured from the conductive fabric. A microcontroller controls the current injection and voltage output patterns and the analog-to-numeric conversion from the tactile material. After explaining the EIT method, we present the electronics corresponding to the data acquisition and we analyze the material property. Results show that we can acquire and localize in real time spatial patterns of the tactile contact

Electronic hardware design of a low cost tactile sensor device for physical Human-Robot Interactions

International audienceWe propose in this paper a low-cost method of Electrical Impedance Tomography (EIT) data acquisition from soft conductive fabric for the design of robots artificial skin. We use a simple multiplexer/demultiplexer circuit for retrieving the resistance field from the pair-wised electrical current injected and the output voltage measured from the conductive fabric. A microcontroller controls the current injection and voltage output patterns and the analog-to-numeric conversion from the tactile material. After explaining the EIT method, we present the electronics corresponding to the data acquisition and we analyze the material property. Results show that we can acquire and localize in real time spatial patterns of the tactile contact