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At the heart of any process of observation there are certain basic things which must happen. These might be analysed in abstract terms, or then again in terms of the quantum physical irreducibles. The paper links between these concerns. It starts by exploring the macroscopic processes which are essential to observation and from there it develops the outline of a path heading towards a continuum based model of quantum mechanics, and in particular, electrodynamics. It exposes attributes which are fundamental to the existence of an observer process, and then explores how the seat of those attributes may be found in the way we set‐up our notion of the basic properties of physical reality. Such a model is offered as a contribution to what is called “second‐order cybernetics”, namely the study of how we go about observing the observer process.

The purpose of this study is to develop new tandem configurations of erbium-doped fiber amplifier (EDFA) using all possible pump-signal-direction schemes in simulation to identify a system configuration with the best performance in the means of the highest gain with the lowest noise figure (NF) output.

The spatial evolution of the physical properties such as gain, NF, population density and amplified spontaneous emission along the total length of the double-stage single-pass EDFA and single-stage double-pass EDFA configurations under all possible pumping direction schemes was investigated. Giles and Desurvire method was used for the mathematical modeling of these configurations where the two level coupled system model equations was solved in MATLAB. In the simulation of the all proposed configurations, an input signal of −35 dBm at the wavelength of 1550 nm and a total of 14 mW pump power at the wavelength of 1480 nm was used for a consistent analysis.

The numerical value of the results obtained is specific to the input parameter values used in the simulations; however, configuration-wise, the EDFA systems found with the best performance are not parameter specific.

The results of this numerical work will help future experimental research of designing and developing ultra-efficient EDFA systems.

The overall performance of an electronic nose system will depend on the individual performance of its constituent elements. Although often overlooked, it is clear that careful design/selection of the front‐end signal conditioning circuit is of critical importance if optimal performance of the odour sensing system is to be achieved. In this paper circuits are reviewed, which have been employed as front‐end signal conditioners for resistance‐based sensors in electronic nose systems, with many of the conclusions drawn being equally applicable to other resistor sensors. The relevant equations governing the behaviour of each circuit methodology are derived and advantages and disadvantages are discussed. The performance of the circuit is then quantitatively assessed in a specific test case, in which the maximum sensitivity of the circuit is calculated in relation to the task of interfacing to a theoretical thin‐film conducting‐polymer sensor.

The purpose of this paper is to outline an integrative, high-level, neurocomputational theory of brain function based on temporal codes, neural timing nets, and active regeneration of temporal patterns of spikes within recurrent neural circuits that provides a time-domain alternative to connectionist approaches.

This conceptual-theoretical paper draws from cybernetics, theoretical biology, neurophysiology, integrative and computational neuroscience, psychology, and consciousness studies.

The high-level functional organization of the brain involves adaptive cybernetic, goal-seeking, switching, and steering mechanisms embedded in percept-action-environment loops. The cerebral cortex is conceived as a network of reciprocally connected, re-entrant loops within which circulate neuronal signals that build up, decay, and/or actively regenerate. The basic signals themselves are temporal patterns of spikes (temporal codes), held in the spike correlation mass-statistics of both local and global neuronal ensembles. Complex temporal codes afford multidimensional vectorial representations, multiplexing of multiple signals in spike trains, broadcast strategies of neural coordination, and mutually reinforcing, autopoiesis-like dynamics. Our working hypothesis is that complex temporal codes form multidimensional vectorial representations that interact with each other such that a few basic processes and operations may account for the vast majority of both low- and high-level neural informational functions. These operational primitives include mutual amplification/inhibition of temporal pattern vectors, extraction of common signal dimensions, formation of neural assemblies that generate new temporal pattern primitive “tags” from meaningful, recurring combinations of features (perceptual symbols), active regeneration of temporal patterns, content-addressable temporal pattern memory, and long-term storage and retrieval of temporal patterns via a common synaptic and/or molecular mechanism. The result is a relatively simplified, signal-centric view of the brain that utilizes universal coding schemes and pattern-resonance processing operations. In neurophenomenal terms, waking consciousness requires regeneration and build up of temporal pattern signals in global loops, whose form determines the contents of conscious experience at any moment.

Understanding how brains work as informational engines has manifold long-reaching practical implications for design of autonomous, adaptive robotic systems. By proposing how new concepts might arise in brains, the theory bears potential implications for constructivist theories of mind, i.e. how observer-actors interacting with one another can self-organize and complexify.

The theory is highly original and heterodox in its neural coding and neurocomputational assumptions. By providing a possible alternative to standard connectionist theory of brain function, it expands the scope of thinking about how brains might work as informational systems.

Nature and occurrence of food-borne pathogens in raw and processed food products evolved greatly in the past few years due to new modes of transmission and resistance build-up against sundry micro-/macro-environmental conditions. Assurance of food health and safety thus gained immense importance, for which bio-sensing technology proved very promising in the detection and quantification of food-borne pathogens. Considering the importance, different studies have been performed, and different biosensors have been developed. This study aims to summarize the different biosensors used for the deduction of food-borne pathogens.

The present review highlights different biosensors developed apropos to food matrices, factors governing their selection, their potential and applicability. The paper discusses some related key challenges and constraints and also focuses on the needs and future research prospects in this field.

The shift in consumers’ and industries’ perceptions directed the further approach to achieve portable, user and environmental friendly biosensing techniques. Despite of these developments, it was still observed that the comparison among the different biosensors and their categories proved tedious on a single platform; since the food matrices tested, pathogen detected or diagnosed, time of detection, etc., varied greatly and very few products have been commercially launched. Conclusively, a challenge lies in front of food scientists and researchers to maintain pace and develop techniques for efficiently catering to the needs of the food industry.

Biosensors deduction limit varied with the food matrix, type of organism, material of biosensors’ surface, etc. The food matrix itself consists of complex substances, and various types of food are available in nature. Considering the diversity of food there is a need to develop a universal biosensor that can be used for all the food matrices for a pathogen. Further research is needed to develop a pathogen-specific biosensor that can be used for all the food products that may have accuracy to eliminate the traditional method of deduction.

The present paper summarized and categorized the different types of biosensors developed for food-borne pathogens.

The paper aims to focus on the memory-polynomial model (MPM) as special case of Volterra series, implemented in hardware. The behavior of the MPM is fully proved through a comparison with AM-AM and AM-PM measured data. The results show that this simulation technique is able to prove the effectiveness of the MPM implementation as behavioural model for high power radiofrequency amplifiers. The system is able to compensate perturbations caused by modern communication systems.

The implementation uses Matlab-Simulink, and its digital signal processing (DSP) builder. The first stage allows developing the model in Matlab using the DSP builder blockset through the signal compiler block. Then, the design is downloaded to the DSP board.

The paper demonstrates a proper behavior of the MPM as a truncation of the Volterra series, with respect to different inputs. This is a key point, because the series truncations allow first to implement this model in real time and second to obtain a correct precision, for instance when modeling amplification of digital signals in high frequency.

The global system approach permits to easily develop, simulate, and validate a wireless system. The efficiency of a complete connected solution based on Agilent Technologies tools, combining simulations and measurements under true operating conditions, seems to be clearly demonstrated.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This review aims to focus on recent reported research work on the construction and function of different electrochemical DNA biosensors. It also describes different sensing materials, chemistries of immobilization probes, conditions of hybridization and principles of transducing and amplification strategies.

The human disease-related mutated genes or DNA sequence detection at low cost can be verified by the electrochemical-based biosensor. A range of different chemistries is used by the DNA-based electrochemical biosensors, out of which the interactions of nanoscale material with recognition layer and a solid electrode surface are most interesting. A diversity of advancements has been made in the field of electrochemical detection.

Some important aspects are also highlighted in this review, which can contribute in the creation of successful biosensing devices in the future.

This paper provides an updated review of construction and sensing technologies in the field of biosensing.

Field-effect transistor (FET) has excellent electronic properties and inherent signal amplification, and with the development of nanomaterials technology, FET biosensors with nanomaterials as channels play an important role in the field of heavy metal ion detection. This paper aims to review the research progress of silicon nanowire, graphene and carbon nanotube field-effect tube biosensors for heavy metal ion detection, so as to provide technical support and practical experience for the application and promotion of FET.

The article introduces the structure and principle of three kinds of FET with three kinds of nanomaterials, namely, silicon nanowires, graphene and carbon nanotubes, as the channels, and lists examples of the detection of common heavy metal ions by the three kinds of FET sensors in recent years. The article focuses on the advantages and disadvantages of the three sensors, puts forward measures to improve the performance of the FET and looks forward to its future development direction.

Compared with conventional instrumental analytical methods, FETs prepared using nanomaterials as channels have the advantages of fast response speed, high sensitivity and good selectivity, among which the diversified processing methods of graphene, the multi-heavy metal ions detection of silicon nanowires and the very low detection limit and wider detection range of carbon nanotubes have made them one of the most promising detection tools in the field of heavy metal ions detection. Of course, through in-depth analysis, this type of sensor has certain limitations, such as high cost and strict process requirements, which are yet to be solved.

This paper elaborates on the detection principle and classification of field-effect tube, investigates and researches the application status of three kinds of FET biosensors in the detection of common heavy metal ions. By comparing the advantages and disadvantages of each of the three sensors in practical applications, the paper focuses on the feasibility of improvement measures, looks forward to the development trend in the field of heavy metal detection and ultimately promotes the application of field-effect tube development technology to continue to progress, so that its performance continues to improve and the application field is constantly expanding.

This study aims to present a novel technique to localize the human position in a room, to manage people in a specified space.

In this study, a real-time human sensing detection and smart lighting control was designed within a single silicon core. The chip has been successfully realized within 1.5 mm² silicon area using TSMC 0.25 um process.

This chip can read the weak signal of pyroelectric infrared (PIR) sensor to find the position of human body in a dark room and then help control the smart lighting system for an intelligent surveillance system.

This chip presented the retriggering delay control to expand the LED lighting time infinitely to avoid lighting-off suddenly while users stay on a space. This function is very useful in a practical intelligent surveillance system that is mainly based on human detection to better reduce power dissipation and memory space.