Ilaria De Munari received the Electronic Engineering degree and the Ph.D. degree in information technologies from the University of Parma, Parma, Italy, in 1991 and 1995, respectively. In 1997, she joined the Department of Information Engineering (presently the Department of Engineering and Architecture), of the University of Parma, as a Research Assistant, where she has been an Associate Professor of electronics since 2004. She was involved in several activities related to assistive technologies, managing some projects in the framework of Ambient Assisted Living Joint Programme in collaboration with other European partners. Her past research interests were tied to the reliability of electronic devices and to the design of electronic systems for Human Activity Recognition. Currently her research encompasses the design of sensors and digital systems based on microcontroller and field-programmable gate array. She has authored or co-authored over 100 papers in technical journals or proceedings of international conferences.
Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to
extend ... more Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%.
This work was supported by the project "Biosensoristica innovativa per i test sierologici e molec... more This work was supported by the project "Biosensoristica innovativa per i test sierologici e molecolari e nuovi dispositivi PoCT per la diagnosi di infezione da SARS-CoV-2" funded in 2020 by "Bando Straordinario di Ateneo per Progetti di Ricerca Biomedica in Ambito SARS-COV-2 e COVID-19"-University of Parma ABSTRACT Recent advances in Internet-of-Things technology have opened the doors to new scenarios for biosensor applications. Flexibility, portability, and remote control and access are of utmost importance to move these devices to people's homes or in a Point-of-Care context and rapidly share the results with users and their physicians. In this paper, an innovative portable device for both quantitative and semi-quantitative electrochemical analysis is presented. This device can operate autonomously without the need of relying on other devices (e.g., PC, tablets, or smartphones) thanks to built-in Wi-Fi connectivity. The developed hardware is integrated into a cloud-based platform, exploiting the cloud computational power to perform innovative algorithms for calibration (e.g., Machine Learning tools). Results and configurations can be accessed through a web page without the installation of dedicated APPs or software. The electrical input/output characteristic was measured with a dummy cell as a load, achieving excellent linearity. Furthermore, the device response to five different concentrations of potassium ferri/ferrocyanide redox probe was compared with a bench-top laboratory instrument. No difference in analytical sensitivity was found. Also, some examples of application-specific tests were set up to demonstrate the use in real-case scenarios. In addition, Support Vector Machine algorithm was applied to semi-quantitative analyses to classify the input samples into four classes, achieving an average accuracy of 98.23%. Finally, COVID-19 related tests are presented and discussed.
Medical & Biological Engineering & Computing, 2017
then shared between different persons. This allows some kind of Plug&Play interaction. Furthermor... more then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min −1 , which outperform current literature findings.
The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, parti... more The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, particularly if connected to a cloud service for data sharing. There are several ways to connect sensors to the cloud: wearable or portable devices often lean on a smartphone that acts as a gateway, while other sensors, such as smart sensors for continuous monitoring (e.g. fall detectors) are connected through wireless networks covering a limited area (e.g. ZigBee or Wi-Fi). Their functionality can be improved using them in both outdoor and indoor environments without other devices. NB-IoT is a recently introduced wide-range protocol with a good compromise between low power, low deployment costs, payload length, and data rate. Traditionally, sensor nodes rely on only one type of radio: an innovative solution could be a sensor node exploiting a combination of different transmission technologies with the aim of achieving higher portability. In this paper, a hybrid solution based on NB-IoT/Wi-Fi is presented. The Wi-Fi connection is primarily selected due to its lower power consumption (compared to NB-IoT), while NB-IoT is activated only when a Wi-Fi network is not available. This study aims to evaluate the power consumption of the proposed solution with respect to single radio NB-IoT technology. Test boards have been implemented, and several data transmission tests have been carried out with both NB-IoT and Wi-Fi radios. Different received powers and payload lengths have been considered to analyze the impact on the energy profile of smart sensors. It has been demonstrated that using NB-IoT for both indoor and outdoor leads to an acceptable battery discharge time, with a strong dependence on the payload length. Under certain conditions, the proposed hybrid solution results in a battery duration up to two times higher than single-radio NB-IoT. INDEX TERMS Sensor systems and applications, smart devices, power measurements, low-power electronics, Internet of Things (IoT).
Nowadays, analytical techniques are moving towards the development of smart biosensing strategies... more Nowadays, analytical techniques are moving towards the development of smart biosensing strategies for the point-of-care accurate screening of disease biomarkers, such as human epididymis protein 4 (HE4), a recently discovered serum marker for early ovarian cancer diagnosis. In this context, the present work represents the first implementation of a competitive enzyme-labelled magneto-immunoassay exploiting a homemade IoT Wi-Fi cloud-based portable potentiostat for differential pulse voltammetry readout. The electrochemical device was specifically designed to be capable of autonomous calibration and data processing, switching between calibration, and measurement modes: in particular, firstly, a baseline estimation algorithm is applied for correct peak computation, then calibration function is built by interpolating data with a four-parameter logistic function. The calibration function parameters are stored on the cloud for inverse prediction to determine the concentration of unknown samples. Interpolation function calibration and concentration evaluation are performed directly on-board, thus reducing the power consumption. The analytical device was validated in human serum, demonstrating good sensing performance for analysis of HE4 with detection and quantitation limits in human serum of 3.5 and 29.2 pM, respectively, reaching the sensitivity that is required for diagnostic purposes, with high potential for applications as portable and smart diagnostic tool for point-of-care testing.
Recent research in wearable sensors have led to the development of an advanced platform capable o... more Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.
In modular distributed architectures, the adoption of a communication method that is at the same ... more In modular distributed architectures, the adoption of a communication method that is at the same time robust and has a low and predictable latency is of utmost importance in order to support the required system dynamics. The aim of this paper is to evaluate the consequences of the random jitter on machine drives distributed control, caused by the messages' re-transmission in case of an error in the received data. To achieve this goal, two different Forward Error Correction (FEC) techniques are introduced in the chosen protocol, so that the recipient of the message can correct random errors without the need of any additional round trip delays needed to request and obtain a re-transmission. Experimentally validated simulations are used to evaluate the impact of random network derived jitter on a real world closed loop control system for distributed power electronic converters.
The design of an FPGA hardware architecture requires, traditionally, its description in a dedicat... more The design of an FPGA hardware architecture requires, traditionally, its description in a dedicated language (Hardware Description Language, HDL), which is often not well suited to manage wide and complex models. The design process can be simplified if the entire architecture can be described in a high abstraction level framework such as Simulink. In this paper a Simulink model-based design of a pipelined accumulator suitable for applications such as Support Vector Machine algorithms is presented. The compatibility with the HDL Coder workflow enables the direct FPGA model implementation. Moreover, the workflow output has been compared with a native VHDL equivalent floating-point accumulator intellectual property.
Reed-Solomon (RS) codes are one of the most used solutions for error correction logic in data com... more Reed-Solomon (RS) codes are one of the most used solutions for error correction logic in data communications. RS decoders are composed of several blocks: among them, many efforts have been made to optimize the error magnitude evaluation module. This paper aims to assess the performance of an innovative algorithm introduced in the literature by Lu et al. under different systems configurations and hardware platforms. Several configurations of the encoded message chosen between those typically used in different applications have been designed to be run on an FPGA (field programmable gate array) device and an MCU (microcontroller unit). The performances have been evaluated in terms of resource usage and output delay for the FPGA and in terms of code execution time for the MCU. As a benchmark in the analysis, the well-established Forney's method is exploited: it has been implemented in the same configurations and on the same hardware platforms for a proper comparison. The results show that the theoretical finding are fully confirmed only in the MCU implementation, while on FPGA, the choice of one method with respect to the other depends on the optimization feature (i.e., time or area) that has been decided as a preference in the specific application.
A new compact diagnostic device exploiting the integration of screen printed electrode-based immu... more A new compact diagnostic device exploiting the integration of screen printed electrode-based immunosensors and remote-controlled IoT-WiFi acquisition board has been realized and validated for diagnosis of Celiac Disease as case of study. The immunodevice is based on chemisorption of open tissue transglutaminase enzyme on the surface of gold nanoparticles-functionalized carbon screen printed electrodes. IgA and IgG anti-tissue transglutaminase target antibodies are recognized by the immobilized bioreceptor as highly specific biomarkers related to Celiac Disease. The signal from the amperometric sensor is acquired and processed through on-purpose developed IoT-WiFi integrated board, allowing for real-time data sharing on cloud services to directly notify all users (physicians, caregivers, etc.) on device outcome. The proposed solution does not require customized hardware or software. The analytical performances of the immunosensors were optimized by experimental design, obtaining diagnostically useful limit of detection (LOD) and limit of quantitation (LOQ) values (LODIgA = 3.2 AU mL−1; LODIgG = 1.4 AU mL−1; LOQIgA = 4.6 AU mL−1; LOQIgG = 2.3 AU mL−1) as well as good intermediate precision (RSD < 5%). The high discrimination capability of the IoT-Wi-Fi device between positive and negative serum control resulted to be suitable for diagnostic purposes, with outstanding statistical significance (p < 0,001)
IEEE Transactions on Instrumentation and Measurement, 2020
The measurement of the analyte concentration in electrochemical biosensors traditionally requires... more The measurement of the analyte concentration in electrochemical biosensors traditionally requires costly laboratory equipment to obtain accurate results. Innovative portable solutions have recently been proposed, but usually, they lean on personal computers (PCs) or smartphones for data elaboration and they exhibit poor resolution or portability and proprietary software. This paper presents a low-cost portable system, assembling an ad hoc-designed analog front end (AFE) and a development board equipped with a system on chip integrating a microcontroller and a Wi-Fi network processor. The wireless module enables the transmission of measurements directly to a cloud service for sharing device outcome with users (physicians, caregivers, and so on). In doing so, the system does not require neither the customized software nor other devices involved in data acquisition. Furthermore, when any Internet connection is lost, the data are stored on board for subsequent transmission when a Wi-Fi connection is available. The noise output voltage spectrum has been characterized. Since the designed device is intended to be battery-powered to enhance portability, investigations about battery lifetime were carried out. Finally, data acquired with a conventional benchtop Autolab PGSTAT-204 electrochemical workstation are compared with the outcome of our developed device to validate the effectiveness of our proposal. To this end, we selected ferri/ferrocyanide as redox probe, obtaining the calibration curves for both the platforms. The final outcomes are shown to be feasible, accurate, and repeatable.
Multiplication is a fundamental operation in most signal and image processing applications. In th... more Multiplication is a fundamental operation in most signal and image processing applications. In this paper, a new architecture for a Vedic multiplier implementing ‘Urdhava-tiryakbhyam’ methodology is proposed. The presented architecture is completely modular and is conceived to be implemented in model-based designs where the configurability is of utmost importance. This architecture is prone to be implemented both as pure combinational and pipelined fashion to fit the needed frequency clock. The proposed multiplier exploits 4:2 compressor blocks instead of standard full-adders. Five different 4:2 compressor architectures from literature have been compared. The designs are developed as model-based schemes in SIMULINK and then automatically coded in VHDL (Very High-speed Integrated Circuits Hardware Description Language) through the HDL coder of MATLAB. The code is synthetized on an Artix 7 FPGA (Field Programmable Gate Array) and performances are evaluated in terms of area occupancy (i.e., LUTs number) and propagation delay (i.e., output time). Results show that despite the achieved configurability and modular architecture, the proposed solution performs equally or in some cases even better compared to solutions already presented in literature.
In a multiple parallel-connected inverters system, limiting the circulating current phenomenon is... more In a multiple parallel-connected inverters system, limiting the circulating current phenomenon is mandatory since it may influence efficiency and reliability. In this paper, a new control method aimed at this purpose and conceived to be implemented on a Field Programmable Gate Array (FPGA) device is presented. Each of the inverters, connected in parallel, is conceived to be equipped with an FPGA that controls the Pulse-Width Modulation (PWM) waveform without intercommunication with the others. The hardware implemented is the same for every inverter; therefore, the addition of a new module does not require redesign, enhancing system modularity. The system has been simulated in a Simulink environment. To study its behavior and to improve the control method, simulations with two parallel-connected inverters have been firstly conducted, then additional simulations have been performed with increasing complexity to demonstrate the quality of the algorithm. The results prove the ability of the method proposed to limit the circulating currents to negligible values.
Monitoring State of Charge battery cells is necessary for all the most common batteries to avoid ... more Monitoring State of Charge battery cells is necessary for all the most common batteries to avoid damages and to extend lifetime. State of Charge must be estimated since it can not be directly measured on the cell. Among all developed techniques, Equivalent Circuit Models is one of the most interesting and it is based on modeling the behavior of the electric components of the cell. However, a real-time parameter estimation is necessary, due to the change of them during the battery life. FPGA is first choice for these applications, for its flexibility and hardware reconfigurability. Traditionally, FPGA is configured with a synthetized Hardware Description Language, HDL, but this process can be time consuming depending on the model complexity. In this paper, a Simulink model-based design for a Li-Ion cell parameters identification is presented. This approach together to HDL Coder Simulink tool, assures higher code portability and short time to market.
The trend toward technology ubiquity in human life is constantly increasing and the same tendency... more The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, and online platforms. In this paper, a new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced. The proposed solution lowers costs and implementation burden by using the Internet connection that leans on standard home modem-routers, already present normally in the homes, and reducing the need for range extenders thanks to the long range of the Wi-Fi signal. Since the main drawback of the Wi-Fi implementation is the high energy drain, low power design strategies have been considered to provide each battery-powered sensor with a lifetime suitable for a consumer application. Moreover, in order to consider the higher consumption arising in the case of the Wi-Fi/Internet connectivity loss, dedicated operating cycles have been introduced obtaining an energy savings of up to 91%. Performance was evaluated: in order to validate the use of the system as a hardware platform for behavioral services, an activity profile of a user for two months in a real context has been extracted.
A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can pro... more A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can provide users with a different interaction path, based on the interpretation of his/her brain activity. Such technology, applied to Ambient Assisted Living (AAL) contexts, could potentially make the full set of features of such systems accessible to users affected by severe motor impairments, for whom the interaction with the surrounding environment is troublesome. In this paper, a low cost BCI development platform, consisting of a hardware acquisition unit and a Matlab-based prototyping environment is presented. BCI performance assessed by means of an illustrative application example using a 4 class SSVEP paradigm to switch on and off lights. Comparison with other reference methods from literature is also presented.
Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to
extend ... more Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%.
This work was supported by the project "Biosensoristica innovativa per i test sierologici e molec... more This work was supported by the project "Biosensoristica innovativa per i test sierologici e molecolari e nuovi dispositivi PoCT per la diagnosi di infezione da SARS-CoV-2" funded in 2020 by "Bando Straordinario di Ateneo per Progetti di Ricerca Biomedica in Ambito SARS-COV-2 e COVID-19"-University of Parma ABSTRACT Recent advances in Internet-of-Things technology have opened the doors to new scenarios for biosensor applications. Flexibility, portability, and remote control and access are of utmost importance to move these devices to people's homes or in a Point-of-Care context and rapidly share the results with users and their physicians. In this paper, an innovative portable device for both quantitative and semi-quantitative electrochemical analysis is presented. This device can operate autonomously without the need of relying on other devices (e.g., PC, tablets, or smartphones) thanks to built-in Wi-Fi connectivity. The developed hardware is integrated into a cloud-based platform, exploiting the cloud computational power to perform innovative algorithms for calibration (e.g., Machine Learning tools). Results and configurations can be accessed through a web page without the installation of dedicated APPs or software. The electrical input/output characteristic was measured with a dummy cell as a load, achieving excellent linearity. Furthermore, the device response to five different concentrations of potassium ferri/ferrocyanide redox probe was compared with a bench-top laboratory instrument. No difference in analytical sensitivity was found. Also, some examples of application-specific tests were set up to demonstrate the use in real-case scenarios. In addition, Support Vector Machine algorithm was applied to semi-quantitative analyses to classify the input samples into four classes, achieving an average accuracy of 98.23%. Finally, COVID-19 related tests are presented and discussed.
Medical & Biological Engineering & Computing, 2017
then shared between different persons. This allows some kind of Plug&Play interaction. Furthermor... more then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min −1 , which outperform current literature findings.
The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, parti... more The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, particularly if connected to a cloud service for data sharing. There are several ways to connect sensors to the cloud: wearable or portable devices often lean on a smartphone that acts as a gateway, while other sensors, such as smart sensors for continuous monitoring (e.g. fall detectors) are connected through wireless networks covering a limited area (e.g. ZigBee or Wi-Fi). Their functionality can be improved using them in both outdoor and indoor environments without other devices. NB-IoT is a recently introduced wide-range protocol with a good compromise between low power, low deployment costs, payload length, and data rate. Traditionally, sensor nodes rely on only one type of radio: an innovative solution could be a sensor node exploiting a combination of different transmission technologies with the aim of achieving higher portability. In this paper, a hybrid solution based on NB-IoT/Wi-Fi is presented. The Wi-Fi connection is primarily selected due to its lower power consumption (compared to NB-IoT), while NB-IoT is activated only when a Wi-Fi network is not available. This study aims to evaluate the power consumption of the proposed solution with respect to single radio NB-IoT technology. Test boards have been implemented, and several data transmission tests have been carried out with both NB-IoT and Wi-Fi radios. Different received powers and payload lengths have been considered to analyze the impact on the energy profile of smart sensors. It has been demonstrated that using NB-IoT for both indoor and outdoor leads to an acceptable battery discharge time, with a strong dependence on the payload length. Under certain conditions, the proposed hybrid solution results in a battery duration up to two times higher than single-radio NB-IoT. INDEX TERMS Sensor systems and applications, smart devices, power measurements, low-power electronics, Internet of Things (IoT).
Nowadays, analytical techniques are moving towards the development of smart biosensing strategies... more Nowadays, analytical techniques are moving towards the development of smart biosensing strategies for the point-of-care accurate screening of disease biomarkers, such as human epididymis protein 4 (HE4), a recently discovered serum marker for early ovarian cancer diagnosis. In this context, the present work represents the first implementation of a competitive enzyme-labelled magneto-immunoassay exploiting a homemade IoT Wi-Fi cloud-based portable potentiostat for differential pulse voltammetry readout. The electrochemical device was specifically designed to be capable of autonomous calibration and data processing, switching between calibration, and measurement modes: in particular, firstly, a baseline estimation algorithm is applied for correct peak computation, then calibration function is built by interpolating data with a four-parameter logistic function. The calibration function parameters are stored on the cloud for inverse prediction to determine the concentration of unknown samples. Interpolation function calibration and concentration evaluation are performed directly on-board, thus reducing the power consumption. The analytical device was validated in human serum, demonstrating good sensing performance for analysis of HE4 with detection and quantitation limits in human serum of 3.5 and 29.2 pM, respectively, reaching the sensitivity that is required for diagnostic purposes, with high potential for applications as portable and smart diagnostic tool for point-of-care testing.
Recent research in wearable sensors have led to the development of an advanced platform capable o... more Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.
In modular distributed architectures, the adoption of a communication method that is at the same ... more In modular distributed architectures, the adoption of a communication method that is at the same time robust and has a low and predictable latency is of utmost importance in order to support the required system dynamics. The aim of this paper is to evaluate the consequences of the random jitter on machine drives distributed control, caused by the messages' re-transmission in case of an error in the received data. To achieve this goal, two different Forward Error Correction (FEC) techniques are introduced in the chosen protocol, so that the recipient of the message can correct random errors without the need of any additional round trip delays needed to request and obtain a re-transmission. Experimentally validated simulations are used to evaluate the impact of random network derived jitter on a real world closed loop control system for distributed power electronic converters.
The design of an FPGA hardware architecture requires, traditionally, its description in a dedicat... more The design of an FPGA hardware architecture requires, traditionally, its description in a dedicated language (Hardware Description Language, HDL), which is often not well suited to manage wide and complex models. The design process can be simplified if the entire architecture can be described in a high abstraction level framework such as Simulink. In this paper a Simulink model-based design of a pipelined accumulator suitable for applications such as Support Vector Machine algorithms is presented. The compatibility with the HDL Coder workflow enables the direct FPGA model implementation. Moreover, the workflow output has been compared with a native VHDL equivalent floating-point accumulator intellectual property.
Reed-Solomon (RS) codes are one of the most used solutions for error correction logic in data com... more Reed-Solomon (RS) codes are one of the most used solutions for error correction logic in data communications. RS decoders are composed of several blocks: among them, many efforts have been made to optimize the error magnitude evaluation module. This paper aims to assess the performance of an innovative algorithm introduced in the literature by Lu et al. under different systems configurations and hardware platforms. Several configurations of the encoded message chosen between those typically used in different applications have been designed to be run on an FPGA (field programmable gate array) device and an MCU (microcontroller unit). The performances have been evaluated in terms of resource usage and output delay for the FPGA and in terms of code execution time for the MCU. As a benchmark in the analysis, the well-established Forney's method is exploited: it has been implemented in the same configurations and on the same hardware platforms for a proper comparison. The results show that the theoretical finding are fully confirmed only in the MCU implementation, while on FPGA, the choice of one method with respect to the other depends on the optimization feature (i.e., time or area) that has been decided as a preference in the specific application.
A new compact diagnostic device exploiting the integration of screen printed electrode-based immu... more A new compact diagnostic device exploiting the integration of screen printed electrode-based immunosensors and remote-controlled IoT-WiFi acquisition board has been realized and validated for diagnosis of Celiac Disease as case of study. The immunodevice is based on chemisorption of open tissue transglutaminase enzyme on the surface of gold nanoparticles-functionalized carbon screen printed electrodes. IgA and IgG anti-tissue transglutaminase target antibodies are recognized by the immobilized bioreceptor as highly specific biomarkers related to Celiac Disease. The signal from the amperometric sensor is acquired and processed through on-purpose developed IoT-WiFi integrated board, allowing for real-time data sharing on cloud services to directly notify all users (physicians, caregivers, etc.) on device outcome. The proposed solution does not require customized hardware or software. The analytical performances of the immunosensors were optimized by experimental design, obtaining diagnostically useful limit of detection (LOD) and limit of quantitation (LOQ) values (LODIgA = 3.2 AU mL−1; LODIgG = 1.4 AU mL−1; LOQIgA = 4.6 AU mL−1; LOQIgG = 2.3 AU mL−1) as well as good intermediate precision (RSD < 5%). The high discrimination capability of the IoT-Wi-Fi device between positive and negative serum control resulted to be suitable for diagnostic purposes, with outstanding statistical significance (p < 0,001)
IEEE Transactions on Instrumentation and Measurement, 2020
The measurement of the analyte concentration in electrochemical biosensors traditionally requires... more The measurement of the analyte concentration in electrochemical biosensors traditionally requires costly laboratory equipment to obtain accurate results. Innovative portable solutions have recently been proposed, but usually, they lean on personal computers (PCs) or smartphones for data elaboration and they exhibit poor resolution or portability and proprietary software. This paper presents a low-cost portable system, assembling an ad hoc-designed analog front end (AFE) and a development board equipped with a system on chip integrating a microcontroller and a Wi-Fi network processor. The wireless module enables the transmission of measurements directly to a cloud service for sharing device outcome with users (physicians, caregivers, and so on). In doing so, the system does not require neither the customized software nor other devices involved in data acquisition. Furthermore, when any Internet connection is lost, the data are stored on board for subsequent transmission when a Wi-Fi connection is available. The noise output voltage spectrum has been characterized. Since the designed device is intended to be battery-powered to enhance portability, investigations about battery lifetime were carried out. Finally, data acquired with a conventional benchtop Autolab PGSTAT-204 electrochemical workstation are compared with the outcome of our developed device to validate the effectiveness of our proposal. To this end, we selected ferri/ferrocyanide as redox probe, obtaining the calibration curves for both the platforms. The final outcomes are shown to be feasible, accurate, and repeatable.
Multiplication is a fundamental operation in most signal and image processing applications. In th... more Multiplication is a fundamental operation in most signal and image processing applications. In this paper, a new architecture for a Vedic multiplier implementing ‘Urdhava-tiryakbhyam’ methodology is proposed. The presented architecture is completely modular and is conceived to be implemented in model-based designs where the configurability is of utmost importance. This architecture is prone to be implemented both as pure combinational and pipelined fashion to fit the needed frequency clock. The proposed multiplier exploits 4:2 compressor blocks instead of standard full-adders. Five different 4:2 compressor architectures from literature have been compared. The designs are developed as model-based schemes in SIMULINK and then automatically coded in VHDL (Very High-speed Integrated Circuits Hardware Description Language) through the HDL coder of MATLAB. The code is synthetized on an Artix 7 FPGA (Field Programmable Gate Array) and performances are evaluated in terms of area occupancy (i.e., LUTs number) and propagation delay (i.e., output time). Results show that despite the achieved configurability and modular architecture, the proposed solution performs equally or in some cases even better compared to solutions already presented in literature.
In a multiple parallel-connected inverters system, limiting the circulating current phenomenon is... more In a multiple parallel-connected inverters system, limiting the circulating current phenomenon is mandatory since it may influence efficiency and reliability. In this paper, a new control method aimed at this purpose and conceived to be implemented on a Field Programmable Gate Array (FPGA) device is presented. Each of the inverters, connected in parallel, is conceived to be equipped with an FPGA that controls the Pulse-Width Modulation (PWM) waveform without intercommunication with the others. The hardware implemented is the same for every inverter; therefore, the addition of a new module does not require redesign, enhancing system modularity. The system has been simulated in a Simulink environment. To study its behavior and to improve the control method, simulations with two parallel-connected inverters have been firstly conducted, then additional simulations have been performed with increasing complexity to demonstrate the quality of the algorithm. The results prove the ability of the method proposed to limit the circulating currents to negligible values.
Monitoring State of Charge battery cells is necessary for all the most common batteries to avoid ... more Monitoring State of Charge battery cells is necessary for all the most common batteries to avoid damages and to extend lifetime. State of Charge must be estimated since it can not be directly measured on the cell. Among all developed techniques, Equivalent Circuit Models is one of the most interesting and it is based on modeling the behavior of the electric components of the cell. However, a real-time parameter estimation is necessary, due to the change of them during the battery life. FPGA is first choice for these applications, for its flexibility and hardware reconfigurability. Traditionally, FPGA is configured with a synthetized Hardware Description Language, HDL, but this process can be time consuming depending on the model complexity. In this paper, a Simulink model-based design for a Li-Ion cell parameters identification is presented. This approach together to HDL Coder Simulink tool, assures higher code portability and short time to market.
The trend toward technology ubiquity in human life is constantly increasing and the same tendency... more The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, and online platforms. In this paper, a new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced. The proposed solution lowers costs and implementation burden by using the Internet connection that leans on standard home modem-routers, already present normally in the homes, and reducing the need for range extenders thanks to the long range of the Wi-Fi signal. Since the main drawback of the Wi-Fi implementation is the high energy drain, low power design strategies have been considered to provide each battery-powered sensor with a lifetime suitable for a consumer application. Moreover, in order to consider the higher consumption arising in the case of the Wi-Fi/Internet connectivity loss, dedicated operating cycles have been introduced obtaining an energy savings of up to 91%. Performance was evaluated: in order to validate the use of the system as a hardware platform for behavioral services, an activity profile of a user for two months in a real context has been extracted.
A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can pro... more A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can provide users with a different interaction path, based on the interpretation of his/her brain activity. Such technology, applied to Ambient Assisted Living (AAL) contexts, could potentially make the full set of features of such systems accessible to users affected by severe motor impairments, for whom the interaction with the surrounding environment is troublesome. In this paper, a low cost BCI development platform, consisting of a hardware acquisition unit and a Matlab-based prototyping environment is presented. BCI performance assessed by means of an illustrative application example using a 4 class SSVEP paradigm to switch on and off lights. Comparison with other reference methods from literature is also presented.
Uploads
Papers by Ilaria De Munari
extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques
in general can provide real-time SoC estimation without the need to design a cell model. In this
work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained
model was 10-fold cross-validated and then designed in Hardware Description Language to be run
on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a
linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx
Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model
to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good
performance was further verified when applied to a dataset acquired from different driving cycles to
the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%.
extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques
in general can provide real-time SoC estimation without the need to design a cell model. In this
work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained
model was 10-fold cross-validated and then designed in Hardware Description Language to be run
on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a
linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx
Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model
to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good
performance was further verified when applied to a dataset acquired from different driving cycles to
the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%.