This work presents the simulation of a Low density Parity Check (LDPC) coding scheme with
multiuserMulti-Carrier Code Division Multiple Access (MC-CDMA) system over Additive White
Gaussian Noise (AWGN) channel and multipath fading channels. The decoding technique used in
the simulation was iterative decoding since it gives maximum efficiency with ten iterations.
Modulation schemes that used are Phase Shift Keying (BPSK, QPSK and 16 PSK), along with the
Orthogonal Frequency Division Multiplexing (OFDM). A 12 pilot carrier were used in the estimator
to compensate channel effect. The channel model used is Long Term Evolution (LTE) channel with
Technical Specification TS 25.101v2.10 and 5 MHz bandwidth including the chan

In this paper a decoder of binary BCH code is implemented using a PIC microcontroller for code length n=127 bits with multiple error correction capability, the results are presented for correcting errors up to 13 errors. The Berkelam-Massey decoding algorithm was chosen for its efficiency. The microcontroller PIC18f45k22 was chosen for the implementation and programmed using assembly language to achieve highest performance. This makes the BCH decoder implementable as a low cost module that can be used as a part of larger systems. The performance evaluation is presented in terms of total number of instructions and the bit rate.

Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, t

Abstract

Hexapod robot is a flexible mechanical robot with six legs. It has the ability to walk over terrain. The hexapod robot look likes the insect so it has the same gaits. These gaits are tripod, wave and ripple gaits. Hexapod robot needs to stay statically stable at all the times during each gait in order not to fall with three or more legs continuously contacts with the ground. The safety static stability walking is called (the stability margin). In this paper, the forward and inverse kinematics are derived for each hexapod’s leg in order to simulate the hexapod robot model walking using MATLAB R2010a for all gaits and the geometry in order to derive the equations of the sub-constraint workspaces for each

Data centric techniques, like data aggregation via modified algorithm based on fuzzy clustering algorithm with voronoi diagram which is called modified Voronoi Fuzzy Clustering Algorithm (VFCA) is presented in this paper. In the modified algorithm, the sensed area divided into number of voronoi cells by applying voronoi diagram, these cells are clustered by a fuzzy C-means method (FCM) to reduce the transmission distance. Then an appropriate cluster head (CH) for each cluster is elected. Three parameters are used for this election process, the energy, distance between CH and its neighbor sensors and packet loss values. Furthermore, data aggregation is employed in each CH to reduce the amount of data transmission which le

Excessive skewness which occurs sometimes in the data is represented as an obstacle against normal distribution. So, recent studies have witnessed activity in studying the skew-normal distribution (SND) that matches the skewness data which is regarded as a special case of the normal distribution with additional skewness parameter (α), which gives more flexibility to the normal distribution. When estimating the parameters of (SND), we face the problem of the non-linear equation and by using the method of Maximum Likelihood estimation (ML) their solutions will be inaccurate and unreliable. To solve this problem, two methods can be used that are: the genetic algorithm (GA) and the iterative reweighting algorithm (IR) based on the M

An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

Diverting river flow during construction of a main dam involves the construction of cofferdams, and tunnels, channels or other temporary passages. Diversion channels are commonly used in wide valleys where the high flow makes tunnels or culverts uneconomic. The diversion works must form part of the overall project design since it will have a major impact on its cost, as well as on the design, construction program and overall cost of the permanent works. Construction costs contain of excavation, lining of the channel, and construction of upstream and downstream cofferdams. The optimization model was applied to obtain optimalchannel cross section, height of upstream cofferdam, and height of downstream cofferdamwith minimum construction cost

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c