In the estimation of a probability density function (PDF) by kernel method, two inherent problems are the choice of sampling methods and the selection of a Bandwidth. In this article, we use the balanced and unbalanced ranked set sampling (RSS) methods and Bayesian Bandwidth to estimate a PDF by kernel method. To compare our method with existing methods, we use an extensive simulation study to compare the RSS with simple random sampling (SRS) PDF estimator and also Bayesian Bandwidth with other existing Bandwidths. As an application, we use the household expenses and income data of the Statistical Center of Iran in 2021 to estimate the PDF of the total expenses of the households in Tehran province.

Upscaling based on the Bandwidth of the kernel function is a flexible approach to upscale the data because the cells will be coarse-based on variability. The intensity of the coarsening of cells in this method can be controlled with Bandwidth. In a smooth variability region, a large number of cells will be merged, and vice versa, they will remain fine with severe variability. Bandwidth variation can be effective in upscaling results. Therefore, determining the optimal Bandwidth in this method is essential. For each Bandwidth, the upscaled model has a number of upscaled blocks and an upscaling error. Obviously, higher thresholds or Bandwidths cause a lower number of upscaled blocks and a higher sum of squares error (SSE). On the other hand, using the smallest Bandwidth, the upscaled model will remain in a fine scale, and there will be practically no upscaling. In this work, different approaches are used to determine the optimal Bandwidth or threshold for upscaling. Investigation of SSE changes, the intersection of two charts, namely SSE and the number of upscaled block charts, and the changes of SSE values versus Bandwidths, are among these approaches. In this particular case, if the goal of upscaling is to minimize the upscaling error, the intersection method will obtain a better result. Conversely, if the purpose of upscaling is computational cost reduction, the SSE variation approach will be more appropriate for the threshold setting.

This paper presents a Transimpedance Amplifier (TIA) for high sensitivity Near Infrared Spectroscopy (NIRS). The proposed TIA is based on the Regulated Cascode (RGC) structure with an extra transistor employed to implement additional feed-forward path and achieve higher gain values. The extra transistor senses a partially amplified input signal, available in the conventional circuit, and conveys an additional ac current into the load, which provides a higher gain. In addition, a Bandwidth extension method is introduced using a capacitor and resistor, which can improve amplifier’s Bandwidth by 40%. The proposed TIA is designed in 0.18µm CMOS technology and achieves a transimpedance gain of 101.9dB with a -3dB Bandwidth of 91.2 MHz considering 2pF of photodiode capacitance at the TIA input. The input referred noise is 4.4pA/√Hz while dissipating 151µW power.

The purpose of the present study was to examine the effects of Bandwidth Modeling on learning a simple timing task. Twenty eight right handed subjects (14 males, 14 females, aged 19-20) voluntarily participated in the study and were assigned randomly to two groups of Bandwidth and yoked. The task was pressing the keys number 2, 6, 8 and 4 on a sequential timing apparatus while keeping certain absolute timing. People in the Bandwidth group received the model whenever their performance went out from predetermined Bandwidth around the goal movement time (a 10 percent Bandwidth), while subjects in the other group were yoked with Bandwidth group. The experiment consisted of acquisition, retention and transfer phases and absolute timing error was used as dependent variable. The results of retention and transfer tests showed that Bandwidth group had less errors than yoked group. The findings of present experiment were in accordance with anticipations of Challenge Point Theory, and showed that Bandwidth modeling is an appropriate technique for adjusting learning condition with learner’s needs. Also advantages of Bandwidth method that in past just had been confirmed in KR presentation scheduling domain, nicely generalized to observational learning domain.

In this paper two recently developed meta-heuristic optimization methods, known as Colliding Bodies Optimization (CBO) and Enhanced Colliding Bodies Optimization (ECBO), are used for optimum nodal ordering to minimize Bandwidth of sparse matrices. The CBO is a simple optimization algorithm which is inspired by a collision between two objects in one-dimension. Each agent is modeled as a body with a specified velocity and mass. A collision happens between pairs of bodies and the new positions of the colliding bodies are updated based on the collision laws. The enhanced colliding bodies optimization (ECBO) utilizes memory to save some best so-far-solution to improve the performance of the CBO without increasing the computational cost. This algorithm utilizes a mechanism to escape from local optima. The Bandwidth of some graph matrices, which have equivalent pattern to structural matrices, is minimized using these approaches. Comparison of the obtained results with those of some existing methods shows the robustness of these two new meta-heuristic algorithms for Bandwidth optimization.

The  optical fiber stands for an exceptionally appealing correspondence medium since it offers an enormous data transmission and low lessening, also, can in this manner encourage requesting administrations like excellent visual communication as well alternatives in PC organizations. In this study, a good strategy simulation based on mathematical equations has been presented for a unique optical channel correspondence. Also, this paper shows the nonlinear analysis  phenomenon of fiber scattering, modulator as well recipient reaction periods, coding type of waveform. The light source spectral width have influence to the presentation of the fiber optics correspondence like link length, information rate, BER. Additionally, this paper show the force and rising period, spending plan is utilized to get a good guess of the communication length as well the piece rate utilizing optical framework test scheme.

An important problem for the WiMAX networks is how to provide a guaranteed quality of service for applications. A key aspect of this problem is how BSs should share Bandwidth capacity between different classes of traffic. This decision needs to be made for each incoming packet, and is known as the packet scheduling problem.A major challenge in packet scheduling is that the behavior of each traffic class may not be known in advance, and can vary dynamically.In this paper, we describe how we have modeled the packet scheduling problem as an application for reinforcement learning (RL). We demonstrate how our RL approach can learn scheduling policies that satisfy the quality of service requirements of multiple traffic classes under a variety of conditions. The proposed solution has been designed to have an ability to accommodate integrated traffic in the networks with effective scheduling schemes. A series of simulation experiments have been carried out to evaluate the performance of the proposed scheduling algorithm. The results reveal that the proposed solution performs effectively to the integrated traffic composed of messages with or without time constraints and achieves proportional fairness among different types of traffic.