The disadvantage of single-sine electrochemical impedance spectroscopy (EIS) compared to multi-sine methods is the long measurement duration. This publication presents a method called optimized frequencies EIS (OFEIS) for shortening the measurement duration applicable to electrochemical interfaces that can be described by the Randles circuit. This method is based on an iterative curve analysis during the measurement process and a subsequent circular fitting in the Nyquist plot. The method is analyzed using simulated data and measurements with ion-sensitive field-effect transistors (ISFETs). In the pH measurements carried out with ISFETs, OFEIS only required an average of approx. 64% of the measurement duration in relation to 30 logarithmically distributed frequency points between 10 Hz and 100 kHz. For this, approx. 10-11 measuring points are used and an average absolute percentage error of 2.2% is achieved when determining the charge transfer resistance.

The electrochemical impedance spectroscopy (EIS) is a measurement method for characterizing bio-recognition events of a sensor, such as field-effect transistor-based biosensors (BioFETs). Due to the lack of portable impedance spectroscopes, EIS applies mainly in laboratories preventing application-oriented use in the field. This work presents a portable impedance analyzer (PIA) providing a 4-channel EIS of BioFETs. It performs the analysis of the recorded spectra by determining the charge transfer resistance Rct with a power-saving algorithm. Therefore, a circle is fitted into the Nyquist representation of the Randles circuit, from whose zero crossings Rct can be determined. The introduced algorithm was evaluated on 100 simulated spectra of Randles circuits. To analyze the overall system, an adjustable reference circuit was developed that simulates configurable Randles circuits. Additional measurements with pH-sensitive ion-sensitive field-effect transistors (ISFETs) demonstrate the application of the measurement system with electrochemical sensors. Using simulated spectra, the circular fitting is able to detect Rct with a median accuracy of 1.2% at an average nominal power of 40 mW and 3054 µs computing time. The PIA with the embedded implementation of the circuit fitting achieves a median error for Rct of 4.2% using the introduced Randles circuit simulator (RCS). Measurements with ISFETs show deviations of 6.5 ± 2.8% compared to the complex non-linear least squares (CNLS), but is significantly faster and more efficient. The presented system allows a portable, power-saving performance of EIS. Future optimizations for a specific applications can improve the presented system and enable novel low-power and automated measurements of biosensors outside the laboratory.

This paper presents a novel scheme dubbed Collision Diversity (CoD) SCRAM, which is provisioned to meet the challenging requirements of the future 6G, portrayed in massive connectivity, reliability, and ultra-low latency. The conventional SCRAM mechanism, which stands for Slotted Coded Random Access Multiplexing, is a hybrid decoding scheme, that jointly resolves collisions and decodes the Low Density Parity Check (LDPC) codewords, in a similar analogy to Belief Propagation (BP) decoding on a joint three-layer Tanner graph. The CoD SCRAM proposed herein tends to enhance the performance of SCRAM by adopting an information-theoretic approach that tends to maximize the attainable Spectral Efficiency. Besides, due to the analogy between the two-layer Tanner graph of classical LDPC codes, and the three-layer Tanner graph of SCRAM, the CoD SCRAM adopts the well-developed tools utilized to design powerful LDPC codes. Finally, the proposed CoD scheme tends to leverage the collisions among the users in order to induce diversity. Results show that the proposed CoD SCRAM scheme surpasses the conventional SCRAM scheme, which is superior to the state-of-the-art Non-Orthogonal Multiple Access (NOMA) schemes. Additionally, by leveraging the collisions, the CoD SCRAM tends to surpass the Sphere-Packing Bound (SPB) at the respective information block length of the underlying LDPC codes of the accommodated users.

We propose a new strategy using a sandwich approach for the detection of two HF biomarkers: tumor necrosis factor-α (TNF-α) and interleukin-10 (IL-10). For this purpose, magnetic nanoparticles (MNPs) (MNPs@aminodextran) were biofunctionalized with monoclonal antibodies (mAbs) using bis (sulfosuccinimidyl) suberate (BS3) as a cross-linker for the pre-concentration of two biomarkers (TNF-α and IL-10). In addition, our ISFETs were biofunctionalized with polyclonal antibodies (pAbs) (TNF-α and IL-10). The biorecognition between pAbs immobilized on the ISFET and the pre-concentrate antigen (Ag) on MNPs was monitored using electrochemical impedance spectroscopy (EIS). Our developed ImmunoFET showed a low detection limit (0.03 pg/mL) toward our target analyte when compared to previously published electrochemical immunosensors. It showed a higher sensitivity than for other HF biomarkers. Finally, the standard addition method was used to determine the unknown concentration in artificial saliva. The results matched with the expected values well.

Cortisol, a steroid hormone mostly known as "the stress hormone," plays many essential functions in humans due its involvement in several metabolic pathways. It is well-known that cortisol dysregulation is implied in evolution and progression of several chronic pathologies, including cardiac diseases such as heart failure (HF). However, although several sensors have been proposed to date for the determination of cortisol, none of them has been designed for its determination in saliva in order to monitor HF progression. In this work, a silicon nitride based Immuno field-effect transistor (ImmunoFET) has been proposed to quantify salivary cortisol for HF monitoring. Sensitive biological element was represented by anti-cortisol antibody bound onto the ISFET gate via 11-triethoxysilyl undecanal (TESUD) by vapor-phase method. Potentiometric and electrochemical impedance spectroscopy (EIS) measurements were carried out for preliminary investigations on device responsiveness. Subsequently, a more sensitive detection was obtained using electrochemical EIS. The proposed device has proven to have a linear response (R2 always >0.99), to be sensitive (with a limit of detection, LoD, of 0.005 ± 0.002 ng/mL), selective in case of other HF biomarkers (e.g. N-terminal pro B-type natriuretic peptide (NT-proBNP), tumor necrosis factor-alpha (TNF-α), and interleukin 10 (IL-10)), and accurate in cortisol quantification in saliva sample by performing the standard addition method.

Physical and chemical phenomena of an electrochemical system can be described by electrochemical impedance spectroscopy (EIS). The spectral response of impedimetric biosensors is often modeled by the Randles circuit, whose parameters can be determined by regression techniques. As one of these parameters, the charge transfer resistance R_ct is often used as the sensor response. Regression in the laboratory environment is usually performed using commercial software, which is typically computationally intensive. Therefore, applications of biosensors outside the laboratory require more efficient concepts, especially when miniaturized or portable instrumentations are used. In this work, an approach for geometric elliptical fitting of the graph in the Nyquist diagram is presented and compared with the complex nonlinear least squares (CNLS) regression. The evaluation is based, on the one hand, on artificial spectra and, on the other hand, on real data from a immunologically sensitive field-effect transistor (IMFET) for cortisol measurement in saliva. For simulated noisy data, the average error in computing R_ct using the elliptical fit with -2.7% is worse than using the CNLS with 0.024 % , but the former required only about of computation time compared to the latter. Applying the elliptic fitting to real data from an IMFET, the determination of R_ct showed deviations of only 0.7± 2.7% compared to CNLS. The impact of these variations on a standard addition method (SAM) was demonstrated for quantitative analysis of cortisol concentration. After application-oriented evaluations considering the possible accuracies, the elliptical fitting could prove to be a resource-saving option for the analysis of impedance spectra in mobile applications.

Heart failure (HF) is a chronic cardiovascular disease that represents main cause of mortality worldwide, particularly for elderly. N-terminal pro-brain natriuretic peptide (NT-proBNP) was identified as the gold standard biomarker for HF diagnosis and therapy monitoring. Presently, saliva analysis represents an emerging and powerful tool for clinical applications and electrochemical immunosensors have shown their potential in Healthcare applications as selective and reliable systems for detecting clinical biomarkers. This work presents the detection of NT-proBNP in saliva samples by an immunologically modified Field effect Transistor (IMFET). TESUD ((11-triethoxysilyl) undecanal) was used as cross-linker to immobilise anti-NT-proBNP antibody onto the device. Our IMFET that was then tested in different matrices (e.g. phosphate buffered saline (PBS), artificial saliva and human saliva) using electrochemical impedance spectroscopy (EIS), and it resulted selective to NT-proBNP with good sensitivity (detection limit of 0.02 pg/mL) and a wide linear range (0.02-1 pg/mL and 0.5-20 pg/mL). Finally, NT-proBNP concentration in ten saliva samples was determined by performing the standard addition method. An enzyme-linked immunosorbent assay was used for confirming IMFET results, highlighting both IMFET accuracy (analyte recovery of 99 +/- 8%) and precision (coefficient of variation always <10%), and supporting the suitability of the device for determining salivary NT-proBNP.

Cellular mobile communication systems have evolved from voice-centric to universal communication systems. After release 10 of the 3GPP standard, the group started working on specific enhancements tailored for machine-type communication (MTC), e.g. improving energy consumption, coverage enhancements and support for massive number of devices per cell. During the study for MTC enhancements, congestion on the random access channel was identified as a major issue in case a very high number of MTC devices tries to simultaneously access the cell. The existing access class barring (ACB) is a threshold based approach affecting all devices in a cell. Thus a new barring mechanism for latency-tolerant MTC devices was introduced: enhanced access barring (EAB). There have been numerous publications in the last years on how to optimize ACB and EAB for throughput and latency but only very few considered the major important impact on the overall energy consumption of the devices. Unlike previous publications, this paper investigates the effect of different access barring schemes on the energy consumption of MTC devices in case of high load on the random access channel and compares static and dynamic ACB settings as well as EAB-based solutions.

Telegram fragmentation can offer an advantage in high interference domains such as unlicensed bands, commonly used for low power wide area networks (LPWANs). When retransmission is not possible, it is crucial to acquire telegrams at the first shot in order to avoid data loss. These desired high sensitivity constraints in conjunction with the high interference probability require the development of a new type of detector. In this paper both the matched subspace detector (MSD) and the constant false alarm rate (CFAR) MSD are adopted to multiple antennas and extended to the detection of fragmented telegrams. Their performance is discussed and examined analytically and using the Monte Carlo method in the additive white Gaussian noise (AWGN) and interference channel.

In this paper, the sum of a hyper-Erlang and a hyper-exponential distributed random variables is analyzed. Although tedious, the resulting random variable’s probability density function (PDF) can be obtained through convolution of its summands’ PDFs. Alternatively, the resulting distribution can be stated directly in terms of a phase-type distribution. However, computing its PDF can still be very costly and this representation gives little insight on the distribution. It can be shown that the sum of both random variable is again hyper-Erlang distributed of incremented order and can therefore be described without requiring the matrix exponential function. We derive a closed form linear algebra expression for the probability weights of the sum’s hyper-Erlang distribution, which significantly reduces the computational complexity of evaluating its distribution.

Electrochemical impedance spectroscopy is an important procedure with the ability to describe a wide range of physical and chemical properties of electrochemical systems. The spectral behavior of impedimetric sensors is mostly described by the Randles circuit, whose parameters are determined by regression techniques on the basis of measured spectra. The charge transfer resistance as one of these parameters is often used as sensor response. In the laboratory environment, the regression is usually performed by commercial software, but for integrated, application-oriented solutions, separate approaches must be pursued. This work presents an approach for elliptical fitting of the curve in the Nyquist plot, which is compared to the complex nonlinear least squares (CNLS) regression technique. For this purpose, artificial spectra were generated, which were considered both with and without noise superposition. Although the average error in calculating the charge transfer resistance from noisy signals using the elliptical fitting of −2.7% was worse than the CNLS with 2.4 · 10−2%, the former required only about 1/225 of the computing time compared to the latter. Following application-oriented evaluations of the achievable accuracies, the elliptical approach may turn out to be a resource saving alternative.

According to the European statistics, approximately 26 million patients worldwide suffer from heart failure (HF), and this number seems to be steadily increasing. Inflammation plays a central role in the development of HF, and the pro-inflammatory cytokine Tumor necrosis factor-alpha (TNF-alpha) represents inflammation gold-standard biomarker. Early detection plays a crucial role for the prognosis and treat-ment of HF. An Ion Sensitive Field Effect Transistor (ISFET) based on silicon nitride transducer and bio-functionalized with anti-TNF-alpha antibody for label-free detection of salivary TNF-alpha is proposed. Electrochemical impedance spectroscopy (EIS) was used for TNF-alpha detection. Our ImmunoFET offered a detection limit of 1 pg mL(-1), with an analytical reproducibility expressed by a coefficient of variance (CV) resulted < 10% for the analysis of saliva samples, and an analyte recovery of 94 +/- 6%. In addition, it demonstrated high selectivity when compared to other HF biomarkers such as Inteleukin-10, N-terminal pro B-type natriuretic peptide, and Cortisol. Finally, ImmunoFET accuracy in determining the unknown concentration of TNF-alpha was successfully tested in saliva samples by performing standard addition method. The proposed ImmunoFET showed great promise as a complementary tool for biomedical application for HF monitoring by a non-invasive, rapid and accurate assessment of TNF-alpha. (C) 2021 Published by Elsevier B.V.

Indoor localization is a highly researched topic as applications such as asset tracking can provide an enormous benefit for users. As GPS is typically not available indoors, smart phones employ WiFi fingerprinting to locate themselves inside buildings. Such localization is also highly interesting in context of Internet of Things (IoT). Low Power Wide Area Networks (LPWAN) offer cost-effective and long-range connectivity for IoT. Employing this approach, LPWAN sensor nodes can be localized using the received signal level at multiple LPWAN receivers. For testing the performance of LPWAN fingerprinting and the development of new algorithms we developed and installed a network consisting of 21 LPWAN receiver stations on the area of the NuernbergMesse, i.e. the international fair of Nuremberg, Germany. The infrastructure is realized as Cloud RAN (Radio Access Network). This means that the receivers only digitize the channel and the actual decoding of the signals is realized in the Cloud. As a result, the infrastructure is able to support the localization of many state-of-the-art LPWAN system. Initial measurements show the high performance and flexibility of the system. This paper gives an overview of the developed network components and shows the initial measurement results.

Abstract Fitting the data of an electrochemical impedance spectroscopy (EIS) typically requires manual estimation of the initial values before regression algorithms such as complex nonlinear least squares (CNLS) can be applied. This makes the success rate of the fitting dependent on the user input. Furthermore, the Randles circuit consists of parameters with substantially differing magnitudes (e.g. capacitors and resistors), which can also strongly affect the success rate of the fitting due to numerical effects. The aim of this work is to investigate methods addressing the described limitations of fitting. Therefore, a Python implementation performing a fit for EIS is benchmarked with an equivalent open source library. The examined implementation optionally includes the normalization of the parameter values, the standardization of the impedances and a pre-fit. Applying the same equivalent circuit without additional signal processing steps and with fixed initial values defined by midpoints of the value ranges, both implementations were able to fit 46.50% of the simulated database with different spectra. Applying the normalization of the parameter values (76.25%) or the same method with additional pre-fit (97.75%) lead to a significant improvement of the success rate. The standardization of impedances did not affect the success rate.

The possible fields of application for small sensor nodes are tremendous and still growing fast. Concepts like the Internet of Things (IoT), Smart City or Industry 4.0 adopt wireless sensor networks for environmental interaction or metering purposes. As they commonly operate in license-exempt frequency bands, telemetry transmissions of sensors are subject to strong interferences and possible shadowing. Especially in the scope of Low Power Wide Area (LPWA) communications, this scenario results in high computational effort and complexity for the receiver side to perceive the signals of interest. Therefore, this paper investigates means to an adequate segmentation of receive spectra for a partial spectrum exchange between base stations of telemetry-based IoT sensor networks. The distinct interchange of in-phase and quadrature (IQ) data could facilitate stream combining techniques to mask out interferences amongst other approaches. This shall improve decoding rates even under severe operation conditions and simultaneously limit the required data volume. We refer to this approach of a reception network as Edge-RAN (Random Access Network). To cope with the high data rates and still enable a base station collaboration, especially in wirelessly connected receiver mesh networks, different filter bank techniques and block transforms are examined, to divide telemetry spectra into distinct frequency sub-channels. Operational constraints for the spectral decomposition are given and different filter methodologies are introduced. Finally, suitable metrics are established. These metrics shall assess the performance of the presented spectrum segmentation schemes for the purpose of a selective partial interchange between sensor network receivers.

Electrochemical impedance spectroscopy (EIS) is a useful approach for modeling the equivalent circuit of biosensors such as field-effect transistor (FET)-based biosensors. During the process of sensor development, laboratory potentiostats are mainly used to realize the EIS. However, those devices are normally not applicable for real use-cases outside the laboratory, so miniaturized and optimized instrumentations are needed. Various integrated circuits (IC) are available that provide EIS, but these make developed systems highly dependent on semiconductor manufacturers, including component availability. In addition, these generally do not meet the instrumentation requirements for FET-based biosensors, thus external circuitry is necessary as well. In this work, an instrumentation is presented that performs EIS between 10 Hz and 100 kHz for FET-based biosensors. The instrumentation includes the generation of the excitation signal, the configuration of the semiconductor and the readout circuit. The readout circuit consists of a transimpedance amplifier with automatic gain adjustment, filter stages, a magnitude and a phase detection circuit. Since magnitude and phase are converted to a DC signal, digitization of the results is trivial without further signal processing steps, minimizing the computational load on the microcontroller. The transmission behavior of the magnitude and phase measurement circuits shows a high linearity for sinusoidal signals. Furthermore, the overall system was tested with resistors, whereby the magnitude measurement error (1.7%) and the phase shift error (1.6°) were determined within the working range of the instrumentation. The functionality of the instrumentation is demonstrated using pH-sensitive field-effect transistors (ISFET) in various solutions.Clinical relevance— Based on the electrochemical impedance spectroscopy of FET-based biosensors such as ImmunoFETs, new point-of-care testing (POCT) devices can be developed that e.g. quantitatively detect the concentration of biomarkers with very low detection limits in body fluids. The instrumentation presented in this work can be part of new generation of diagnostic tools featuring innovative sensor technologies.