Transmitting binary data across a network should generally avoid transmitting raw binary data over the medium for several reasons, one would be that the medium may be a textual one and may not accept or correctly handle raw bitstream, another would be that some protocols may misinterpret the meaning of the bits and causes a problem or even loss of the data. To make the data more readable and would avoid misinterpretation by different systems and environments, this paper introduces encoding two of the most broadly used data interchange formats, XML and JSON, into the Base64 which is an encoding scheme that converts binary data to an ASCII string format by using a radix-64 representation. This process, will, make the data more readable and would avoid misinterpretation by different systems and environments. The results reflect that encoding data in Base64 before the transmission will present many advantages including readability and integrity, it will also enable us to transmit binary data over textual mediums, 7 Bit protocols such as SMTP, and different network hardware without risking misinterpretation.
In this paper ternary logic is encoded into binary and certain processes were conducted on binary logic after which the binary is decoded to ternary. General purpose digital devices were used and the circuit is designed back to front starting from ternary logic provided by transistor pairs at output side back to front end. This provided easier design technique in this particular paper. Practical and simulation results are recorded. K eyw ords: Logic, ternary- binary conversion, coding, decoding. تشفير المنطق الثالثي رونق علي حبيب جامعة الب صرة / كلية الهندسة / العراق الخالصة في هذا البحث يتم تشفير المنطق الثالثي الى ثنائي ويتم اجراء بعض العمليات على المنطق الثنائي التي يتم بعدها االعادة الى الشفرة الثالثية . استخدمت النبائط الرقمية لالغراض العامة (general purpose devices) في تصميم الدائرة الذ ي يتم من مرحلة االخراج (output) الى مرحلة االدخال (input) بدءا من المنطق الثالثي باستخدام ازواج الترانزستور في جهة االخراج والعودة الى جهة االدخال . هذا يجعل تقنية التصميم اسهل ( في هذا البحث خاصة ). ولقد تم تسجيل النتائج العملية ونتائج المحاكاة للدائرة العمل . ية اﻟﻤﺠﻠﺔ اﻟﻌﺮاﻗﻴﺔ ﻟﻠﻬﻨﺪﺳﺔ اﻟﻜﻬﺮﺑﺎﺋﻴﺔ واﻻﻟﻜﺘﺮوﻧﻴﺔ Iraq J. Electrical and Electronic Engineering ﻡﺠﻠﺪ 10 ، اﻟﻌﺪد 1 ، 2014 Vol.10 No.1 , 2014 24
This paper proposes a low-cost Light Emitting Diodes (LED) system with a novel arrangement that allows an indoor multi- robot localization. The proposed system uses only a matrix of low-cost LED installed uniformly on the ground of an environment and low-cost Light Dependent Resistor (LDR), each equipped on bottom of the robot for detection. The matrix of LEDs which are driven by a modified binary search algorithm are used as active beacons. The robot localizes itself based on the signals it receives from a group of neighbor LEDs. The minimum bounded circle algorithm is used to draw a virtual circle from the information collected from the neighbor LEDs and the center of this circle represents the robot’s location. The propose system is practically implemented on an environment with (16*16) matrix of LEDs. The experimental results show good performance in the localization process.
This paper presents a simple method for the design of Chaotic Linear Feedback Shift Register (CLFSR) system. The proposed method is based on a combination of two known systems. The first is called Linear Feedback Shift Register (LFSR) system, and the other is called Chaotic Map system. The main principle of the proposed system is that, the output of the LFSR is modified by exclusive-or (XOR) it with the stream bit that is generated by using the chaotic map system to eliminate the linearity and the repeating in the output of the LFSR system. The proposed system is built under Matlab environment and the quality of sequence generation tested by using standard tests which shows that the proposed system is a good random number generator that overcome the linearity and repeating disadvantages.
This study proposes a blind speech separation algorithm that employs a single-channel technique. The algorithm’s input signal is a segment of a mixture of speech for two speakers. At first, filter bank analysis transforms the input from time to time-frequency domain (spectrogram). Number of sub-bands for the filter is 257. Non-Negative Matrix Factorization (NNMF) factorizes each sub-band output into 28 sub-signals. A binary mask separates each sub-signal into two groups; one group belongs to the first speaker and the other to the second speaker. The binary mask separates each sub-signal of the (257×28) 7196 sub-speech signals. That separation cannot identify the speaker. Identification of the sub-signal speaker for each sub-signal is achieved by speaker clustering algorithms. Since speaker clustering cannot process without speaker segmentation, the standard windowed-overlap frames have been used to partition the speech. The speaker clustering process fetches the extracted phase angle from the spectrogram (of the mixture speech) and merges it into the spectrogram (of the recovered speech). Filter bank synthesizes these signals to produce a full-band speech signal for each speaker. Subjective tests denote that the algorithm results are accepted. Objectively, the researchers experimented with 66 mixture chats (6 females and 6 males) to test the algorithm. The average of the SIR test is 11.1 dB, SDR is 1.7 dB, and SAR is 2.8 dB.
This paper presents the design of a path planning system in an environment that contains a set of static and dynamic polygon obstacles localized randomly. In this paper, an algorithm so-called (Polygon shape tangents algorithm) is proposed to move a mobile robot from a source point to a destination point with no collision with surrounding obstacles using the visibility binary tree algorithm. The methodology of this algorithm is based on predicting the steps of a robot trajectory from the source to the destination point. The polygon shapes tangent algorithm is compared with the virtual circles' tangents algorithm for different numbers of static and dynamic polygon obstacles for the time of arrival and the length of the path to the target. The obtained result shows that the used algorithm has better performance than the other algorithms and gets less time of arrival and shortest path with free collision.
This paper presents the designing of path planning system in an environment contains a set of static polygon obstacles localized and distributed randomly by using differential drive mobile robot. In this paper the designed algorithm (two dimensional path planning algorithm) is proposed in order of investigate the path planning of mobile robot with free collision using the visibility binary tree algorithm. The suggested algorithm is compared with the virtual circles tangents algorithm in the time of arrival and the longest of the path to the target. The aim of this paper is to get an algorithm has better performance than the other algorithms and get less time of arrival and shortest path with free collision.
A robot is a smart machine that can help people in their daily lives and keep everyone safe. the three general sequences to accomplish any robot task is mapping the environment, the localization, and the navigation (path planning with obstacle avoidance). Since the goal of the robot is to reach its target without colliding, the most important and challenging task of the mobile robot is the navigation. In this paper, the robot navigation problem is solved by proposed two algorithms using low-cost IR receiver sensors arranged as an array, and a robot has been equipped with one IR transmitter. Firstly, the shortest orientation algorithm is proposed, the robot direction is corrected at each step of movement depending on the angle calculation. secondly, an Active orientation algorithm is presented to solve the weakness in the preceding algorithm. A chain of the active sensors in the environment within the sensing range of the virtual path is activated to be scan through the robot movement. In each algorithm, the initial position of the robot is detected using the modified binary search algorithm, various stages are used to avoid obstacles through suitable equations focusing on finding the shortest and the safer path of the robot. Simulation results with multi-resolution environment explained the efficiency of the algorithms, they are compatible with the designed environment, it provides safe movements (without hitting obstacles) and a good system control performance. A Comparison table is also provided.
Multiplication-accumulation (MAC) operation plays a crucial role in digital signal processing (DSP) applications, such as image convolution and filters, especially when performed on floating-point numbers to achieve high-level of accuracy. The performance of MAC module highly relies upon the performance of the multiplier utilized. This work offers a distinctive and efficient floating-point Vedic multiplier (VM) called adjusted-VM (AVM) to be utilized in MAC module to meet modern DSP demands. The proposed AVM is based on Urdhva-Tiryakbhyam-Sutra (UT-Sutra) approach and utilizes an enhanced design for the Brent-Kung carry-select adder (EBK-CSLA) to generate the final product. A (6*6)-bit AVM is designed first, then, it is extended to design (12*12)-bit AVM which in turns, utilized to design (24*24)-bit AVM. Moreover, the pipelining concept is used to optimize the speed of the offered (24*24)-bit AVM design. The proposed (24*24)-bit AVM can be used to achieve efficient multiplication for mantissa part in binary single-precision (BSP) floating-point MAC module. The proposed AVM architectures are modeled in VHDL, simulated, and synthesized by Xilinx-ISE14.7 tool using several FPGA families. The implementation results demonstrated a noticeable reduction in delay and area occupation by 33.16% and 42.42%, respectively compared with the most recent existing unpipelined design, and a reduction in delay of 44.78% compared with the existing pipelined design.
Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images. .
In the last couple decades, several successful steganography approaches have been proposed. Least Significant Bit (LSB) Insertion technique has been deployed due to its simplicity in implementation and reasonable payload capacity. The most important design parameter in LSB techniques is the embedding location selection criterion. In this work, LSB insertion technique is proposed which is based on selecting the embedding locations depending on the weights of coefficients in Cosine domain (2D DCT). The cover image is transformed to the Cosine domain (by 2D DCT) and predefined number of coefficients are selected to embed the secret message (which is in the binary form). Those weights are the outputs of an adaptive algorithm that analyses the cover image in two domains (Haar and Cosine). Coefficients, in the Cosine transform domain, with small weights are selected. The proposed approach is tested with samples from the BOSSbase, and a custom-built databases. Two metrics are utilized to show the effectiveness of the technique, namely, Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). In addition, human visual inspection of the result image is also considered. As shown in the results, the proposed approach performs better, in terms of (RMSE, and PSNR) than commonly employed truncation and energy based methods.
Novel Coronavirus (Covid-2019), which first appeared in December 2019 in the Chinese city of Wuhan. It is spreading rapidly in most parts of the world and becoming a global epidemic. It is devastating, affecting public health, daily life, and the global economy. According to the statistics of the World Health Organization on August 11, the number of cases of coronavirus (Covid-2019) reached nearly 17 million, and the number of infections globally distributed among most European countries and most countries of the Asian continent, and the number of deaths from the Corona virus reached 700 thousand people around the world. . It is necessary to detect positive cases as soon as possible in order to prevent the spread of this epidemic and quickly treat infected patients. In this paper, the current literature on the methods used to detect Covid is presented. In these studies, the research that used different techniques of artificial intelligence to detect COVID-19 was reviewed as the convolutionary neural network (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) were proposed for the identification of patients infected with coronavirus pneumonia using chest X-ray radiographs By using 5-fold cross validation, three separate binary classifications of four grades (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) were introduced. It has been shown that the pre-trained ResNet50 model offers the highest classification performance (96.1 percent accuracy for Dataset-1, 99.5 percent accuracy for Dataset-2 and 99.7 percent accuracy for Dataset-2) based on the performance results obtained.