The ability to reliably detect the forthcoming failure of a rechargeable cell without removing it from its normal operating environment remains a significant goal in battery research. In this work we have cycled in the laboratory a previously-aged 3.2 A h, 3.6 V 18650 INR LiNixMnyCo 1-x-yO2 cell for 300 d until failure was apparent, using a current waveform representative of use in an electric vehicle application. Electrochemical impedance spectroscopy (EIS) down to 5 mu Hz was also performed on the cell as a 'gold-standard' measure, at the beginning, end and part way through the cycling. Analysis of voltage and current time series data using both parametric (equivalent circuit model) and non-parametric (wavelet-based analysis) approaches allowed us to successfully reconstruct the EIS data. As the battery aged, impedance gradually increased at frequencies between 10-4 Hz-10-1 Hz. The increase accelerated around 50 d before the battery ultimately failed. The acceleration in rate of change of impedance was detectable while the cycle efficiency remained high, indicating that a user of the cell would be unlikely to detect any change in the cell based on its performance or by common measures of state-of-health. The results imply upcoming failure may be detectable from time series analysis weeks before any noticeable drop in cell performance.

Using an equivalent circuit model (ECM) of a battery that involves fractional elements we analytically derive Peukert’s empirical equation along with generalisations of the equation for the increasing capacity of the battery as the charge and discharge currents are reduced. The derived generalised Peukert’s Equations are dimensionally consistent and all parameters (including Peukert’s coefficient and the so-called ‘capacity constant’) can be calculated from the parameters of the ECM and operating voltage range of the battery. Experiments are conducted on ten batteries to demonstrate that the resistor fractional-capacitor series ECM fit to discharge times predicts well the impedance spectrum found by electrochemical impedance spectroscopy (EIS), and vice versa, on Li-CO/NCA/NMC and NA-ion batteries. This agreement is not observed on the tested LiFePO4 and LiTO batteries because the impedance spectrum exhibits behaviour not captured by the ECM. Peukert’s Equation predicts ever increasing capacity as both the charge and discharge currents are reduced. The experimental results confirm this behaviour for all batteries down to the lowest current measured (C/256).

Ageing of rechargeable batteries is routinely characterized in the frequency domain by electrochemical impedance spectroscopy, but the technique requires laboratory measurements to be made on a time scale of days. However, the normal cycling of a battery as it is used in situ provides equivalent information in the time domain, though extracting robust frequency information from a time series is challenging. In this work, we explore, in the time domain, the relationship between instantaneous voltage-current phase difference and cycle efficiency. Moreover, we demonstrate that phase measures can be used to identify battery ageing. We have cycled a 250 mA h Nickel-Cobalt cell several hundred times and used Hilbert Transforms to identify phase difference between voltage and current. This phase difference becomes closer to zero as the battery ages, commensurate with a drop in energy cycle efficiency. In another experiment, we applied a synthetic current profile mimicking behaviour of an electric car cell, to a 3.2 A h LiNiMnCoO 2 cell, for ~100 days. For this more complicated profile with a wide range of frequency content, we used wavelet analysis to identify changes in phase difference and impedance as the battery aged. For this cell, drop in cycle efficiency was associated with a rise in internal resistance. The results imply that time-series analysis of in situ measurements of voltage and current, when applied with equivalent circuit models and underlying theory, can identify markers of battery ageing.

Existing time-domain fractional model simulations of batteries are either limited to short time sequences, frequently less than 100 s, if truly fractional or use low order RC-ladder fractional approximations to reduce computational burden. Here we present an entirely-passive, truly fractional, equivalent-circuit model of a battery. We rely on a Reimann-Louiville fractional order differintegral to account for long time-scales out to 12 days. An analytical solution is provided for the differintegral, subject to the constraint of piecewise constant current. We validate our model fitting against a multi-day sequence of measured time domain data and EIS measured to 10 μHz. The spectral content of the current waveform is identified as a crucial factor. The full evaluation of fractional elements leads to residual error of voltage waveforms that is amongst the best in the literature despite the model having only five parameters. In the time-domain, a root-mean-square error (RMSE) as low as 2.8mV is achieved while maintaining a frequency-domain RMSE of 14% from measured impedance values over a span of 6 decades. The use of time-weighted regression is shown to be important to the time-domain fit.

Fractional capacitors, commonly called constant-phase elements or CPEs, are used in modeling and control applications, for example, for rechargeable batteries. Unfortunately, they are not natively supported in the well-used circuit simulator SPICE. This manuscript presents and demonstrates a modeling approach that allows users to incorporate these elements in circuits and model the response in the time domain. The novelty is that we implement for the first time a particular configuration of RC elements in parallel in a Foster-type network with SPICE in order to simulate a constant-phase element across a defined frequency range. We demonstrate that the circuit produces the required impedance spectrum in the frequency domain, and shows a power-law voltage response to a step change in current in the time domain, consistent with theory, and is able to reproduce the experimental voltage response to a complicated current profile in the time domain. The error depends on the chosen frequency limits and the number of RC branches, in addition to very small SPICE numerical errors. We are able to define an optimum circuit description that minimizes error while maintaining a short computation time. The scientific value is that the work permits rapid and accurate evaluation of the response of CPEs in the time domain, faster than other methods, using open source tools.

Digital video incurs many distortions during processing, compression, storage, and transmission, which can reduce perceived video quality. Developing adaptive video transmission methods that provide increased bandwidth and reduced storage space while preserving visual quality requires quality metrics that accurately describe how people perceive distortion. A severe problem for developing new video quality metrics is the limited data on how the early human visual system simultaneously processes spatial and temporal information. The problem is exacerbated by the fact that the few data collected in the middle of the last century do not consider current display equipment and are subject to medical intervention during collection, which does not guarantee a proper description of the conditions under which media content is currently consumed. In this paper, the 27840 thresholds of the visibility of spatio-temporal sinusoidal variations necessary to determine the artefacts that a human perceives were measured by a new method using different spatial sizes and temporal modulation rates. A multidimensional model of human contrast sensitivity in modern conditions of video content presentation is proposed based on new large-scale data obtained during the experiment. We demonstrate that the presented visibility model has a distinct advantage in predicting subjective video quality by testing with video quality metrics and including our and other visibility models against three publicly available video datasets.

A pdf of an article submitted to IEEE Circuits and Systems. A method of describing a constant phase element with the SPICE family of circuit simulators.

There is a strong need for non-reference video quality metrics for user-generated video content to prevent loss of video quality caused by distortion during recording, compression, and signal transmission. Here we contribute to advancing the issue of streaming quality by creating a large-scale dataset with video compression and transmission artefacts. Our final dataset consists of 4.1 million video quality perceptual thresholds by users. We also created a new first non-reference video quality metric that includes the psychophysical features of the user’s video experience, which provides stability in predicting the user’s subjective rating of a video. Our experimental results show that the proposed video quality metric achieves the most stable performance on three independent video datasets. We believe our study will expand further research into deep learning-based video quality metrics modelling.

Video traffic from content delivery networks occu-pied 82% of all consumed bandwidth in 2022. Nevertheless, the available bandwidth is sometimes volatile and limited. Adaptive video streaming or, in other words, prediction of quality is the key to increasing throughput and reducing storage. Unfortunately, while developing video quality metrics, a problem exists in the algorithmic representation of the human visual system, such as the cognitive component, namely the delay of human reaction to artifacts, which is not represented in the current works. The presented new methodology of data collection of the delay of the human visual system response to video artifacts in modern terms of providing information in natural conditions is presented. New knowledge of the human visual system adaptation or other words time of reaction of perception of artefacts, including the response to motion perceptions necessary for correct work of video quality assessments, is presented and tested. The proposed work introduced that the use of new data on the human visual system adaptation gives an improvement in the performance of video quality assessment metrics.

Nowadays, numerous video compression quality assessment metrics are available. Some of these metrics are “objective” and only tangentially represent how a human observer rates video quality. On the other hand, models of the human visual system have been shown to be effective at describing spatial coding. In this work we propose a new quality metric which extends the peak signal to noise ratio metric with features of the human visual system measured using modern LCD screens. We also analyse the current visibility models of the early visual system and compare the commonly used quality metrics with metrics containing data modelling human perception. We examine the Pearson’s linear correlation coefficient of the various video compression quality metrics with human subjective scores on videos from the publicly available Netflix data set. Of the metrics tested, our new proposed metric is found to have the most stable high performance in predicting subjective video compression quality.

Microwave medical imaging systems have shown a competitive advantage in stroke detection due to their cost-effectiveness, non-ionization, and portability. However, these systems often rely on time-consuming image reconstruction techniques, which are disadvantageous for timely stroke treatment. In this article, a novel microwave medical sensing (MMS) method is proposed for fast stroke classification and localization, which utilizes space division of the region under examination (RUE) (i.e., the head). The space division is enabled by the generalized scattering matrix (GSM) theory and incorporates the brain anatomy. Then a novel decision-tree learning method is proposed, which facilitates efficient stroke feature identification for classification. The spatial information acquired from the decision-tree also results in rapid stroke localization. To verify the proposed method, we investigate the feasibility of classifying brain strokes between an intracranial hemorrhage (ICH) stroke and an ischemic stroke (IS) with a wearable MMS system. Both numerical and experimental results are obtained. Compared to the traditional method, the classification rates for simulation and experimental results are improved by 14.1% and 19.2%, respectively. Furthermore, by utilizing the a priori information, the localization time is reduced by 21.1%. Finally, the localization accuracy is higher than 0.90 in both simulation and experimental studies. The classification accuracy and localization efficiency are shown to be greatly improved compared to the traditional method, which has great significance for wearable devices. This study proposes an efficient space-division-based detection method to localize the brain stroke without imaging.

This manuscript reconciles cyclic voltammetry (CV) and incremental capacity analysis (ICA) to show that they provide equivalent information, and demonstrates how CV sweep rate is related to ICA charging rate or current density. We use these observations relating to CV and ICA to show how electrochemical impedance spectroscopy (EIS) results depend upon rate of charge movement, and explain the irregular behavior seen in the extra low-frequency (ELF) EIS measurements that we have found to be required for fitting reliable equivalent circuit models to batteries. EIS at ELFs exposes physical and mathematical links between these nonlinear electrochemical and linear electronic techniques, and the connections between EIS, ICA and CV explain why addition of a direct current stimulus during EIS ensures the capture of reliable battery impedance data.

Magnetically assembled bioresorbable nanoswimmers (NSs) can be used to highlight small tumors, thereby increasing the diagnostic capability of existing medical imaging techniques. Built upon our earlier work, this article proposes a novel in vivo computational framework for early cancer detection. Engineered NSs experience a change in their physical properties under the influence of tumor-induced biological gradients. The biologically sensed data by such bio-nano things (NSs) can either trigger an autonomous target-directed motion or be assisted through external manipulation for steering the swarm toward the target. Previously developed externally manipulable in vivo computation requires constant monitoring of NSs, introducing positioning and steering errors along with a limit on the swarm size. A parallel approach called autonomous in vivo computation helps to resolve the above drawbacks, but the tumor homing is slow contributing to a higher percentage of predetection loss of NSs. We propose the spot sampling strategy for an autonomous swarm which considers the whole swarm as a single entity for the purpose of its tracking and steering. We show through computational experiments: 1) that the proposed semi-autonomous in vivo framework can achieve faster tumor sensitization in complex environments having static and mobile obstacles and 2) that the spot sampling provides sufficiently precise data to steer the swarm toward the target, saving around 90% of the monitoring resource. Our proposed framework also helps to achieve a large swarm size (number of NSs) which in return can achieve a higher deposition of NSs on malignant tumors.

This article proposes a novel contrast-enhanced microwave cancer detection (MCD) system to accurately detect the location of a tumor targeted by nanoscale contrast agents. This system adopts the angle-of-arrival (AoA) positioning approach that is conventionally applied to the region where plane waves dominate the propagation. Hence, to ensure the effectiveness of the AoA approach when it is working in close proximity to the human body, a new algorithm is proposed to transfer the antenna’s radiation pattern from the far-field to the detection region where spherical waves can still be observed. This AoA-based MCD system requires fewer antennas than other radar-based positioning approaches, thereby creating a low-profile hardware architecture. In addition, during the detection process, a differential technique is incorporated in order to track the signal change due to the administration of contrast agents, which can significantly suppress the noise inside the biological medium. The AoA-based differential MCD system is numerically studied on an anatomically realistic breast phantom subject to a variety of signal-to-noise ratios (SNRs). The results show that the proposed system can successfully locate the tumor with an average resolution of 0.6 mm. When the SNR in the biological medium is 10 dB, the average positioning error is less than 1.5 mm with sensitivity maintained above 60%, which surpasses the performance of other similar systems. The system is also experimentally validated with a physical breast phantom and stepper-motor-driven rotating antennas, where the reconstructed image shows a minor deviation of 3.61 mm with that of the simulated one.

Motivated by the advancements on bioresorbable nanoswimmers, this paper considers the advantages of direct targeting over systemic targeting for smart tumor homing under the general framework of computational nanobiosensing. Nanoswimmers assembled by magnetic nanoparticles can be used as contrast agents to estimate the locations of tumors inside the human body. Closely observing the response of nanoswimmers (which act as in vivo biosensors) to the tumor-triggered biological gradients and then guiding them through external manipulation, can result in a higher accumulation at the diseased location. Sensor informatics along with data fusion can play a crucial role in such a knowledge-aided targeting process. Specifically, built upon our previous work on direct targeting inspired by the gradient descent optimization, this work is focused on resolving the real-life constraints of in vivo natural computation such as uniformity of the magnetic field and finite life span of the nanoswimmers. To overcome these challenges, we propose a multi-estimate-fusion strategy to obtain a common steering direction for the swarm of nanoswimmers. We show through computational experiments (1) that the mean of individual gradient estimations provides the best choice for symmetrical conditions (tumor location in line with the direction of blood flow) while leader-based swarm steering gives the best results for non-symmetrical search space, and (2) that the iterative memory-driven gradient descent optimization detects the target faster compared to the classical memory-less gradient descent and knowledge-less systemic targeting. Our proposed strategies demonstrate that a clear demarcation between malignant tumors and healthy tissues can be visualized before nanoswimmers are consumed in human vasculature. We believe that our work will help in overcoming the challenges posed by natural in vivo computation for tumor diagnosis at its early stage. • Use of nanoswimmers as potential contrast enhancement agents for tumor amplification. • Computing-inspired tumor localization using biological gradients increases detection probability of early tumors. • Sensor informatics and data fusion facilitate in removing limitations of natural detection process. • Performance evaluation of strategies using computer methods demonstrate better targeting efficiency.

Background and Objective: Motivated by the advancements on bioresorbable nanoswimmers, this paper considers the advantages of direct targeting over systemic targeting for smart tumor homing under the general framework of computational nanobiosensing. Nanoswimmers assembled by magnetic nanoparticles can be used as contrast agents to estimate the locations of tumors inside the human body. Methods: Closely observing the response of nanoswimmers (which act as in vivo biosensors) to the tumor-triggered biological gradients and then guiding them through external manipulation, can result in a higher accumulation at the diseased location. Sensor informatics along with data fusion can play a crucial role in such a knowledge-aided targeting process. Specifically, built upon our previous work on direct targeting inspired by the gradient descent optimization, this work is focused on resolving the real-life constraints of in vivo natural computation such as uniformity of the magnetic field and finite life span of the nanoswimmers. To overcome these challenges, we propose a multi-estimate-fusion strategy to obtain a common steering direction for the swarm of nanoswimmers. Results: We show through computational experiments (1) that the mean of individual gradient estimations provides the best choice for symmetrical conditions (tumor location in line with the direction of blood flow) while leader-based swarm steering gives the best results for non-symmetrical search space, and (2) that the iterative memory-driven gradient descent optimization detects the target faster compared to the classical memory-less gradient descent and knowledge-less systemic targeting. Conclusion: Our proposed strategies demonstrate that a clear demarcation between malignant tumors and healthy tissues can be visualized before nanoswimmers are consumed in human vasculature. We believe that our work will help in overcoming the challenges posed by natural in vivo computation for tumor diagnosis at its early stage.

Chilling injury is a physiological disorder that can develop in kiwifruit during prolonged cool storage. At an early stage, symptoms are not visually apparent without cutting the fruit open. Development of non-destructive methods for early stage chilling injury detection has been a recent research focus. Using standard visible - near-infrared interactance spectroscopy - spectral characteristics of chill-damaged kiwifruit were investigated, particularly the spectral discrimination of chill-damaged fruit from fruit with visually similar tissue damage caused by impacts and rots. The initial expectation had been that the main visual damage symptom, in all cases internal water-soaked tissue, would dominate observed spectral characteristics. This proved not to be the case, the presence of tissues with a granular and/or corky appearance within chill-damaged fruit being a dominant feature. Compared with sound fruit, the spectral pattern changes of all damaged fruit looked different. While the spectral pattern differences of rotten and chill-damaged fruit appeared similar, those from impact-damaged fruit differed.

This paper proposes a novel fuzzy-inspired biosensing strategy for contrast-enhanced tumor classification by using multiple features of the disease. Specifically, the proposed strategy considers two features of breast cancers, i.e., tissue malignancy and distance between cancerous cells, to determine the cancer status through the fuzzy relation transfer analysis. The analysis enables an intuitive yet systematic way to characterize the variation of classification fuzziness occurred in the biosensing process when nanoscale materials are utilized as contrast agents. Subsequently, root-mean-square-error of the membership function is introduced to evaluate the sensing integrity. Finally, numerical results are presented to demonstrate the principles of the proposed strategy.

This letter proposes a general formula to determine the amount of compound required to produce water-oil-based phantoms that can emulate the electrical properties of a specific body tissue at microwave frequencies. The existing process to produce such a phantom usually involves mixing together a number of different materials, where the proportion of each compound needs to be known a priori to reproduce the dielectric properties of a specific tissue. In this letter, we first investigate the dielectric variation of the mixture by changing the proportion of each compound over a wide frequency band. By utilizing Lichtenecker's logarithmic mixture formula, the gradient descent iterative optimization method is applied to train the relations obtained, and a formula that determines the amount of each compound required to produce a phantom given a specific tissue type is derived. The formula is verified by constructing representative tissues of the human head, with the durability and fidelity of the produced phantom also analyzed.