Diagnosis of Faulty Elements in Array Antenna using Nature Inspired Cuckoo Search Algorithm

Detection and correction of faulty elements in a linear array have great importance in radar, sonar, mobile communications and satellite. Due to single element failure, the whole radiation pattern damage in terms of side lobes level and nulls. Once we have detect the position of defective element, then correction method is applied to achieve the desired pattern. In this work, we introduce a nature inspired meta-heuristic cuckoo search algorithm to diagnose the position of defective elements in a linear array. The nature inspired cuckoo search algorithm is new to the optimization family and is used first time for fault detection in an array antenna. Cuckoo search algorithm is a global search optimization technique. The cost function is used as a fitness function which defines an error between the degraded far field power pattern and the estimated one. The proposed technique is used effectively for the diagnosis of complete, as well as, for partial faulty elements position. Different simulation results are evaluated for 40 elements Taylor pattern to validate and check the performance of the proposed technique

An efficient color LED driver based on self-configuration current mirror circuit

The string channel of Color LED driver with precise current balancing is proposed. It is noted that to drive a multiple LEDs string is by using a proper current source, due to the level of the brightness LED depends on the quantity of the current flows. In the production of LEDs, the variation in the forward voltage for each LED has been found significantly high. This variation causes different levels of brightness in LEDs. Then, controlling load current of LED by using a resistor to limit the LED current flowing is considered by associated with the forward voltage, instantly. Current sources have been designed to become immune to the above problem since it regulates the current, and not the voltage which flows through the LEDs. Hence, constant current source is the essential requirement to drive the LEDs. Besides, it is complex for color LEDs, dependent on the number of control nodes and dimming configuration. To arrange an accurate load current for the different sets of string color LEDs, the efficient LED driver is required, in which the current sharing is complement to each LED strings. Therefore, this paper suggests a color LED driver with self-configuration of enhanced current mirrors in multiple LED strings. The load currents have been efficiently balanced among the identical loads and unequal loads. The comparable efficiency of the string color LEDs losses has been shown thoroughly

Addressing Addressing Illicit Tobacco Growth in Pakistan: Leveraging AI and Satellite Technology for Precise Monitoring and Effective Solutions

The market share of illicit tobacco products in Pakistan has seen a significant surge in recent years. In 2022, it reached a staggering 42.5%. Since January 2023, there has been a sharp 32.5% increase in volumes of Duty Not Paid (DNP) products and a remarkable 67% surge in the quantities of smuggled cigarettes. This rise can be attributed to the unregistered and unlicensed tobacco cultivation in Pakistan. This sector has largely relied on conventional methods for data collection in the field, primarily managed by the country's crop statistical departments. The utilization of cutting-edge artificial intelligence techniques and satellite imagery for generating crop statistics has the potential to address this issue effectively. We established a synergy by combining images from two remote sensing satellites and collected field data to detect tobacco crops using Recurrent Neural Networks (RNN). The results affirm the effectiveness of these techniques in detecting and estimating the acreage of tobacco crops in the observed areas, particularly in a union council of the Swabi region. We conducted surveys to collect training and validation data through our proprietary smartphone application, GeoSurvey. The collected data was subsequently refined, preprocessed, and organized to prepare it for use with our deep learning algorithm. The model we developed for the detection and acreage estimation of tobacco crops is called Convolutional Long Short-Term Memory (ConvLSTM). We created two datasets from the acquired satellite images for comparison. Our experimentation results demonstrated that the use of ConvLSTM for the synergy of Sentinel-2 and Planet-Scope imagery yields higher training and validation accuracy, reaching 98.09% and 96.22%, respectively. In comparison, the use of time series Sentinel-2 images alone achieved training and testing accuracy of 97.78% and 95.56%

Addressing Addressing Illicit Tobacco Growth in Pakistan: Leveraging AI and Satellite Technology for Precise Monitoring and Effective Solutions

The market share of illicit tobacco products in Pakistan has seen a significant surge in recent years. In 2022, it reached a staggering 42.5%. Since January 2023, there has been a sharp 32.5% increase in volumes of Duty Not Paid (DNP) products and a remarkable 67% surge in the quantities of smuggled cigarettes. This rise can be attributed to the unregistered and unlicensed tobacco cultivation in Pakistan. This sector has largely relied on conventional methods for data collection in the field, primarily managed by the country's crop statistical departments. The utilization of cutting-edge artificial intelligence techniques and satellite imagery for generating crop statistics has the potential to address this issue effectively. We established a synergy by combining images from two remote sensing satellites and collected field data to detect tobacco crops using Recurrent Neural Networks (RNN). The results affirm the effectiveness of these techniques in detecting and estimating the acreage of tobacco crops in the observed areas, particularly in a union council of the Swabi region. We conducted surveys to collect training and validation data through our proprietary smartphone application, GeoSurvey. The collected data was subsequently refined, preprocessed, and organized to prepare it for use with our deep learning algorithm. The model we developed for the detection and acreage estimation of tobacco crops is called Convolutional Long Short-Term Memory (ConvLSTM). We created two datasets from the acquired satellite images for comparison. Our experimentation results demonstrated that the use of ConvLSTM for the synergy of Sentinel-2 and Planet-Scope imagery yields higher training and validation accuracy, reaching 98.09% and 96.22%, respectively. In comparison, the use of time series Sentinel-2 images alone achieved training and testing accuracy of 97.78% and 95.56%

On the Performance of Self-Concatenated Coding for Wireless Mobile Video Transmission Using DSTS-SP-Assisted Smart Antenna System

In the current age of advanced technologies, there is an escalating demand for reliable wireless systems, catering to the high data rates of mobile multimedia applications. This article presents a novel approach to the concept of Self-Concatenated Convolutional Coding (SECCC) with Sphere Packing (SP) modulation via Differential Space-Time Spreading- (DSTS-) based smart antennas. The two transmitters provide transmit diversity which is capable of recuperating the signal from the effects of fading, even with a single receiving antenna. The proposed DSTS-SP SECCC scheme is probed for the Rayleigh fading channel. The SECCC structure is developed using the Recursive Systematic Convolutional (RSC) code with the aid of an interleaver. Interleaving generates randomness in exchange for extrinsic information between the constituent decoders. Iterative decoding is invoked at the receiving side to enhance the output performance by attaining fruitful convergence. The convergence behaviour of the proposed system is investigated using EXtrinsic Information Transfer (EXIT) curves. The performance of the proposed system is ascertained with the H.264 standard video codec. The perceived video quality of DSTS-SP SECCC is found to be significantly better than that of the DSTS-SP RSC. To be more precise, the proposed DSTS-SP SECCC system exhibits an Eb/N0 gain of 8 dB at the PSNR degradation point of 1 dB, relative to the equivalent rate DSTS-SP RSC. Similarly, an Eb/N0 gain of 10 dB exists for the DSTS-SP SECCC system at 1 dB degradation point when compared with the SECCC scheme dispensing with the DSTS-SP approach

On the Performance of Wireless Video Communication Using Iterative Joint Source Channel Decoding and Transmitter Diversity Gain Technique

In this research work, we have presented an iterative joint source channel decoding- (IJSCD-) based wireless video communication system. The anticipated transmission system is using the sphere packing (SP) modulation assisted differential space-time spreading (DSTS) multiple input-multiple output (MIMO) scheme. SP modulation-aided DSTS transmission mechanism results in achieving high diversity gain by keeping the maximum possible Euclidean distance between the modulated symbols. Furthermore, the proposed DSTS scheme results in a low-complexity MIMO scheme, due to nonemployment of any channel estimation mechanism. Various combinations of source bit coding- (SBC-) aided IJSCD error protection scheme has been used, while considering their identical overall bit rate budget. Artificial redundancy is incorporated in the source-coded stream for the proposed SBC scheme. The motive of adding artificial redundancy is to increase the iterative decoding performance. The performance of diverse SBC schemes is investigated for identical overall code rate. SBC schemes are employed with different combinations of inner recursive systematic convolutional (RSC) codes and outer SBC codes. Furthermore, the convergence behaviour of the employed error protection schemes is investigated using extrinsic information transfer (EXIT) charts. The results of experiments show that our proposed Rate−2/3 SBC-assisted error protection scheme with high redundancy incorporation and convergence capability gives better performance. The proposed Rate−2/3 SBC gives about 1.5 dB Eb/N0 gain at the PSNR degradation point of 1 dB as compared to Rate−6/7 SBC-assisted error protection scheme, while sustaining the overall bit rate budget. Furthermore, it is also concluded that the proposed Rate−2/3 SBC-assisted scheme results in Eb/N0 gain of 24 dB at the PSNR degradation point of 1 dB with reference to Rate−1 SBC benchmarker scheme