Sensors

Salmonella enterica is a major food-borne pathogen of world-wide concern. Sensitive and rapid detection methods to assess product safety before retail distribution are highly desirable. Since Salmonella is most commonly associated with poultry products, an evanescent wave fiber-optic assay was developed to detect Salmonella in shell egg and chicken breast and data were compared with a time-resolved fluorescence (TRF) assay. Anti-Salmonella polyclonal antibody was immobilized onto the surface of an optical fiber using biotin-avidin interactions to capture Salmonella. Alexa Fluor 647-conjugated antibody (MAb 2F-11) was used as the reporter. Detection occurred when an evanescent wave from a laser (635 nm) excited the Alexa Fluor and the fluorescence was measured by a laser-spectrofluorometer at 710 nm. The biosensor was specific for Salmonella and the limit of detection was established to be 103 cfu/mL in pure culture and 104 cfu/mL with egg and chicken breast samples when spiked with 102 cfu/mL after 2–6 h of enrichment. The results indicate that the performance of the fiber-optic sensor is comparable to TRF, and can be completed in less than 8 h, providing an alternative to the current detection methods.

The process of collecting low-level kinetic energy, which is present in all moving systems, by using energy harvesting principles, is of particular interest in wearable technology, especially in ultra-low power devices for medical applications. In fact, the replacement of batteries with innovative piezoelectric energy harvesting devices can result in mass and size reduction, favoring the miniaturization of wearable devices, as well as drastically increasing their autonomy. The aim of this work is to assess the power requirements of wearable sensors for medical applications, and address the intrinsic problem of piezoelectric kinetic energy harvesting devices that can be used to power them; namely, the narrow area of optimal operation around the eigenfrequencies of a specific device. This is achieved by using complex numerical models comprising modal, harmonic and transient analyses. In order to overcome the random nature of excitations generated by human motion, novel excitation modalities are investigated with the goal of increasing the specific power outputs. A solution embracing an optimized harvester geometry and relying on an excitation mechanism suitable for wearable medical sensors is hence proposed. The electrical circuitry required for efficient energy management is considered as well.

The response of resistive In2O3−x sensing devices was investigated as a function of the NO2 concentration in different operative conditions. Sensing layers are 150 nm thick films manufactured by oxygen-free room temperature magnetron sputtering deposition. This technique allows for a facile and fast manufacturing process, at same time providing advantages in terms of gas sensing performances. The oxygen deficiency during growth provides high densities of oxygen vacancies, both on the surface, where they are favoring NO2 absorption reactions, and in the bulk, where they act as donors. This n-type doping allows for conveniently lowering the thin film resistivity, thus avoiding the sophisticated electronic readout required in the case of very high resistance sensing layers. The semiconductor layer was characterized in terms of morphology, composition and electronic properties. The sensor baseline resistance is in the order of kilohms and exhibits remarkable performances with respect to gas sensitivity. The sensor response to NO2 was studied experimentally both in oxygen-rich and oxygen-free atmospheres for different NO2 concentrations and working temperatures. Experimental tests revealed a response of 32%/ppm at 10 ppm NO2 and response times of approximately 2 min at an optimal working temperature of 200 °C. The obtained performance is in line with the requirements of a realistic application scenario, such as in plant condition monitoring.

Ubiquitous geo-sensing enables context-aware analyses of physical and social phenomena, i.e., analyzing one phenomenon in the context of another. Although such context-aware analysis can potentially enable a more holistic understanding of spatio-temporal processes, it is rarely documented in the scientific literature yet. In this paper we analyzed the collective human behavior in the context of the weather. We therefore explored the complex relationships between these two spatio-temporal phenomena to provide novel insights into the dynamics of urban systems. Aggregated mobile phone data, which served as a proxy for collective human behavior, was linked with the weather data from climate stations in the case study area, the city of Udine, Northern Italy. To identify and characterize potential patterns within the weather-human relationships, we developed a hybrid approach which integrates several spatio-temporal statistical analysis methods. Thereby we show that explanatory factor analysis, when applied to a number of meteorological variables, can be used to differentiate between normal and adverse weather conditions. Further, we measured the strength of the relationship between the ‘global’ adverse weather conditions and the spatially explicit effective variations in user-generated mobile network traffic for three distinct periods using the Maximal Information Coefficient (MIC). The analyses result in three spatially referenced maps of MICs which reveal interesting insights into collective human dynamics in the context of weather, but also initiate several new scientific challenges.

In this paper, a novel approach to implement a stray insensitive CMOS interface for differential capacitive sensors is presented. The proposed circuit employs, for the first time, second-generation voltage conveyors (VCIIs) and produces an output voltage proportional to differential capacitor changes. Using VCIIs as active devices inherently allows the circuit to process the signal in the current domain, and hence, to benefit from its intrinsic advantages, such as high speed and simple implementation, while still being able to natively interface with voltage mode signal processing stages at necessity. The insensitiveness to the effects of parasitic capacitances is achieved through a simple feedback loop. In addition, the proposed circuit shows a very simple and switch-free structure (which can be used for both linear and hyperbolic sensors), improving its accuracy. The readout circuit was designed in a standard 0.35 μm CMOS technology under a supply voltage of ±1.65 V. Before the integrated circuit fabrication, to produce tangible proof of the effectiveness of the proposed architecture, a discrete version of the circuit was also prototyped using AD844 and LF411 to implement a discrete VCII. The achieved measurement results are in good agreement with theory and simulations, showing a constant sensitivity up to 412 mV/pF, a maximum linearity error of 1.9%FS, and acknowledging a good behavior with low baseline capacitive sensors (10 pF in the proposed measurements). A final table is also given to summarize the key specs of the proposed work comparing them to the available literature.

Distributed estimation over sensor networks has attracted much attention due to its various applications. The mean-square error (MSE) criterion is one of the most popular cost functions used in distributed estimation, which achieves its optimality only under Gaussian noise. However, impulsive noise also widely exists in real-world sensor networks. Thus, the distributed estimation algorithm based on the minimum kernel risk-sensitive loss (MKRSL) criterion is proposed in this paper to deal with non-Gaussian noise, particularly for impulsive noise. Furthermore, multiple tasks estimation problems in sensor networks are considered. Differing from a conventional single-task, the unknown parameters (tasks) can be different for different nodes in the multitask problem. Another important issue we focus on is the impact of the task similarity among nodes on multitask estimation performance. Besides, the performance of mean and mean square are analyzed theoretically. Simulation results verify a superior performance of the proposed algorithm compared with other related algorithms.

Accurate crop performance monitoring and production estimation are critical fortimely assessment of the food balance of several countries in the world. Since 2001, theFamine Early Warning Systems Network (FEWS NET) has been monitoring cropperformance and relative production using satellite-derived data and simulation models inAfrica, Central America, and Afghanistan where ground-based monitoring is limitedbecause of a scarcity of weather stations. The commonly used crop monitoring models arebased on a crop water-balance algorithm with inputs from satellite-derived rainfallestimates. These models are useful to monitor rainfed agriculture, but they are ineffectivefor irrigated areas. This study focused on Afghanistan, where over 80 percent ofagricultural production comes from irrigated lands. We developed and implemented aSimplified Surface Energy Balance (SSEB) model to monitor and assess the performanceof irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250-m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water- use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water- balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields.

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods.

The outbreak of the coronavirus disease (COVID-19) pandemic caused by the novel coronavirus (SARS-CoV-2) has been declared an international public health crisis. It is essential to develop diagnostic tests that can quickly identify infected individuals to limit the spread of the virus and assign treatment options. Herein, we report a proof-of-concept label-free electrochemical immunoassay for the rapid detection of SARS-CoV-2 virus via the spike surface protein. The assay consists of a graphene working electrode functionalized with anti-spike antibodies. The concept of the immunosensor is to detect the signal perturbation obtained from ferri/ferrocyanide measurements after binding of the antigen during 45 min of incubation with a sample. The absolute change in the [Fe(CN)6]3−/4− current upon increasing antigen concentrations on the immunosensor surface was used to determine the detection range of the spike protein. The sensor was able to detect a specific signal above 260 nM (20 µg/mL) of subunit 1 of recombinant spike protein. Additionally, it was able to detect SARS-CoV-2 at a concentration of 5.5 × 105 PFU/mL, which is within the physiologically relevant concentration range. The novel immunosensor has a significantly faster analysis time than the standard qPCR and is operated by a portable device which can enable on-site diagnosis of infection.