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The purpose of this study is to propose a pulse width based, in-pixel, arbitrary size kernel convolution processor. When image sensors are used in machine vision tasks, large amount of data need to be transferred to the output and fed to a processor. Basic and low-level image processing functions such as kernel convolution is used extensively in the early stages of most machine vision tasks. These low-level functions are usually computationally extensive and if the computation is performed inside every pixel, the burden on the external processor will be greatly reduced.

In the proposed architecture, digital pulse width processing is used to perform kernel convolution on the image sensor data. With this approach, while the photocurrent fluctuations are expressed with changes in the pulse width of an output signal, the small processor incorporated in each pixel receives the output signal of the corresponding pixel and its neighbors and produces a binary coded output result for that specific pixel. The process is commenced in parallel among all pixels of the image sensor.

It is shown that using the proposed architecture, not only kernel convolution can be performed in the digital domain inside smart image sensors but also arbitrary kernel coefficients are obtainable simply by adjusting the sampling frequency at different phases of the processing.

Although in-pixel digital kernel convolution has been previously reported however with the presented approach no in-pixel analog to binary coded digital converter is required. Furthermore, arbitrary kernel coefficients and scaling can be deployed in the processing. The given architecture is a suitable choice for smart image sensors which are to be used in high-speed machine vision tasks.

The purpose of this paper is to design a kernel convolution processor. High-speed image processing is a challenging task for real-time applications such as product quality control of manufacturing lines. Smart image sensors use an array of in-pixel processors to facilitate high-speed real-time image processing. These sensors are usually used to perform the initial low-level bulk image filtering and enhancement.

In this paper, using pulse-width modulated signals and regular nearest neighbor interconnections, a convolution image processor is presented. The presented processor is not only capable of processing arbitrary size kernels but also the kernel coefficients can be any arbitrary positive or negative floating number.

The performance of the proposed architecture is evaluated on a Xilinx Virtex-7 field programmable gate array platform. The peak signal-to-noise ratio metric is used to measure the computation error for different images, filters and illuminations. Finally, the power consumption of the circuit in different operating conditions is presented.

The presented processor array can be used for high-speed kernel convolution image processing tasks including arbitrary size edge detection and sharpening functions, which require negative and fractional kernel values.

The purpose of this paper is to present a DNA hybridization detection sensor. An inexpensive fabrication procedure was used so that the sensors can be disposed economically after the measurement is completed.

Field effect transistor (FET) devices are used in the proposed structure. The FET device acts as a charge detection element and produces an amplified output current based on surface charge variations. As amplification is performed directly at the sensor frontend, noise sources have less effect on the detected signal, and thus, acceptably low DNA concentrations can be detected with simple external electronics. ZnO nano layers are used as the FET active semiconductor channel. Furthermore, a photobiasing approach is used to adjust the operating point of the proposed FET without the need for an additional gate terminal.

The proposed sensor is evaluated by applying matched and unmatched target DNA fragments on the fabricated sensors with capture probes assembled either directly on the ZnO surface or on a nano-platinum linker layer. It is observed that the presented approach can successfully detect DNA hybridization at the nano mole range with no need for complex laboratory measurement devices.

The presented photobiasing approach is effective in the adjustment of the sensor sensitivity and decreases the fabrication complexity of the achieved sensor compared with previous works.

Sensor networks have found wide applications in the monitoring of environmental events such as temperature, earthquakes, fire and pollution. A major challenge with sensor network hardware is their limited available energy resource, which makes the low power design of these sensors important. This paper aims to present a low power sensor which can detect sound waveform signatures.

A novel mixed signal hardware is presented to correlate the received sound signal with a specific sound signal template. The architecture uses pulse width modulation and a single bit digital delay line to propagate the input signal over time and analog current multiplier units to perform template matching with low power usage.

The proposed method is evaluated for a chainsaw signature detection application in forest environments, under different supply voltage values, input signal quantization levels and also different template sample points. It is observed that an appropriate combination of these parameters can optimize the power and accuracy of the presented method.

The proposed mixed signal architecture allows voltage and power reduction compared with conventional methods. A network of these sensors can be used to detect sound signatures in energy limited environments. Such applications can be found in the detection of chainsaw and gunshot sounds in forests to prevent illegal logging and hunting activities.

Nanopore-based molecular sensing and measurement, specifically DNA sequencing, is advancing at a fast pace. Some embodiments have matured from coarse particle counters to enabling full human genome assembly. This evolution has been powered not only by improvements in the sensors themselves, but also in the assisting microelectronic CMOS readout circuitry closely interfaced to them. In this light, this paper aims to review established and emerging nanopore-based sensing modalities considered for DNA sequencing and CMOS microelectronic methods currently being used.

Readout and amplifier circuits, which are potentially appropriate for conditioning and conversion of nanopore signals for downstream processing, are studied. Furthermore, arrayed CMOS readout implementations are focused on and the relevant status of the nanopore sensor technology is reviewed as well.

Ion channel nanopore devices have unique properties compared with other electrochemical cells. Currently biological nanopores are the only variants reported which can be used for actual DNA sequencing. The translocation rate of DNA through such pores, the current range at which these cells operate on and the cell capacitance effect, all impose the necessity of using low-noise circuits in the process of signal detection. The requirement of using in-pixel low-noise circuits in turn tends to impose challenges in the implementation of large size arrays.

The study presents an overview on the readout circuits used for signal acquisition in electrochemical cell arrays and investigates the specific requirements necessary for implementation of nanopore-type electrochemical cell amplifiers and their associated readout electronics.

The purpose of this study is to construct imaging pixels using novel bioactive films. Despite the notable progress in electronic imaging devices, these sensors still cannot compete with biological vision counterparts such as the human eye. Light sensitive biolayers and pigments in living organisms show superior performance in terms of low noise operation and speed. Although photoactive biolayers have been used to construct electronic imaging devices, they are usually hard to develop, and the organisms that produce these active layers have low growth rates.

Among 40 pigment producing prokaryotic marine bacteria, four strains which show faster growth rates in the presence of light are screened and characterized by Fourier transform infrared spectroscopy and visible absorption. Subsequently, they are used as active layers in light sensitive sensors. The performance of the obtained cells is eventually evaluated by time domain photoresponse measurements.

It is shown that while the obtained strains have high growth rates and their mass volume reproduction is relatively simple, they provide many interesting characteristics such as high speed and low noise operation when incorporated as photosensitive layers.

Because the mass reproduction of the obtained cultures is simple, they are an appropriate choice for use in planner and flexible document imaging devices and DNA microarray sensors.

The purpose of this study is to propose a model to evaluate the performance of organizational units considering intellectual capital (IC) and employee loyalty approach applying principal component analysis and data envelopment analysis (PCA-DEA) method.

Organization units are considered as decision-making units, IC components including human capital (HC), structural capital (SC) and customer capital are inputs and employee loyalty is output. The principal component analysis was used to converts inputs and outputs into the independent variables. As a return to scale is variable, a modified envelopment input-oriented BCC model applied to obtain the efficiency of organization units. Also, all units of organization are ranked. Eventually, sensitivity analysis performed to show how input variables influence on output variable.

Operation, design and construction, production planning, internal affairs, quality control and security were recognized as efficient units. Also, units of operation, internal affairs and quality control ranked first to third, and the human resource unit earned the last rank. In addition, results of sensitivity analysis on input variables showed that the order of impact intensity is: customer capital, HC and SC, respectively.

Existence a framework for the development of human resource strategies and prioritization in the allocation of organizational resources to improve the performance of the organization considering human resources is vital. Most of the previous studies, just have examined the impact of IC on different dimensions of organizational performance. Meanwhile, evaluating the performance of IC with employee loyalty approach, using PCA-DEA simultaneously can evaluate and measure the impact of IC on the performance of the organization and its units regarding employee loyalty, which has a significant impact on improving the organization’s level of IC and human resource management.

In this paper, a novel Lyapunov–Krasovskii stable fuzzy proportional-integral-derivative (PID) (FPID) controller is introduced for load frequency control of a time-delayed micro-grid (MG) system that benefits from a fuel cell unit, wind turbine generator and plug-in electric vehicles.

Using the Lyapunov–Krasovskii theorem, the adaptation laws for the consequent parameters and output scaling factors of the FPID controller are developed in such a way that an upper limit (the maximum permissible value) for time delay is introduced for the stability of the closed-loop MG system. In this way, there is a stable FPID controller, the adaptive parameters of which are bounded. In the obtained adaptation laws and the way of stability analyses, there is no need to approximate the nonlinear model of the controlled system, which makes the implementation process of the proposed adaptive FPID controller much simpler.

It has been shown that for a different amount of time delay and intermittent resources/loads, the proposed adaptive FPID controller is able to enforce the frequency deviations to zero with better performance and a less amount of energy. In the proposed FPID controller, the increase in the amount of time delay leads to a small increase in the amount of overshoot/undershoot and settling time values, which indicate that the proposed controller is robust to the time delay changes.

Although the designed FPID controllers in the literature are very efficient in being applied to the uncertain and nonlinear systems, they suffer from stability problems. In this paper, the stability of the FPID controller has been examined in applying to the frequency control of a nonlinear input-delayed MG system. Based on the Lyapunov–Krasovskii theorem and using rigorous mathematical analyses, the stability conditions and the adaptation laws for the parameters of the FPID controller have been obtained in the presence of input delay and nonlinearities of the MG system.

A hydraulic elevator including the hydraulic actuator and cabin is highly nonlinear with many parameters and variables. Its state-space model is in non-companion form and uncertain due to the parametric errors, flexibility of the ropes, friction and external load disturbances. A model-based control cannot perform well while a precise model is not available and all state variables cannot be measured. To overcome the problems, this paper aims to develop a direct adaptive fuzzy control (DAFC) for the hydraulic elevator.

The controller is an adaptive PD-like Mamdani type fuzzy controller using position error and velocity error as inputs. The design is based on the stability analysis.

The proposed control can overcome uncertainties, guarantee stability, provide a good tracking performance and operate as active vibration suppression by tracking a smooth trajectory. The controller is not involved in the nonlinearity, uncertainty and vibration of the system due to being free from model. Its performance is superior to a PD-like fuzzy controller due to being adaptive as illustrated by simulations.

The proposed DAFC is applied for the first time on the hydraulic elevator. Compared to classic adaptive fuzzy, it does not require all system states. In addition, it is not limited to the systems, which have the state-space model in companion form and constant input gain, thus is much less computational and easier to implement.

Anxiety disorders have a high prevalence in children. Those children with anxious symptoms are more likely to experience significant disruption in their lives. This disruption can interrupt or even stop a child from participating in a variety of typical childhood experiences. It is understood that genetic and environmental factors may cause this disorder. The purpose of this paper is to focus on environmental factors, namely, the mediating role of maladaptive schemas in mothers’ child-rearing and childhood anxiety disorders.

This study used correlation-modeling to assess the analysis. The sample included 326 students (aged 9-12 years old) and their mothers. The parenting style (Baumrind, 1973), Early Maladaptive Schema (Rijkeboer and de Boo, 2010), and anxiety disorders (Muris et al., 2006) questionnaires were used in this study.

The results showed a relationship between parenting styles of mothers and childhood anxiety disorders, a significant correlation between childhood maladaptive schemas and childhood anxiety disorders, a relation between child-rearing styles and childhood maladaptive schemas, and finally a mediating role on childhood anxiety disorders and mothers’ child-rearing styles for some childhood maladaptive schemas.

This research contributes to the knowledge base of the importance of children’s mental health. The paper analyzes the relationship of mothers’ parenting styles and children’s anxiety. It also focuses on maladaptive schemas as a mediator and its relationship with childhood anxiety disorders.