Asian Journal of Electrical Sciences (AJES) is a quarterly international peer-reviewed journal of Electrical Sciences. One of the objectives of this journal is to disseminate knowledge on various research issues connected with the topics include, but are not limited to:

Telecommunications – transmission of information, coax cable, optical fiber or free space, amplitude modulation and frequency modulation, transmitters and receivers, transceiver.
Instrumentation – pressure, flow and temperature, electromagnetic theory, thermocouples, sensors of larger electrical systems, control engineering.
Computers – Computers and computer systems, hardware, software, software engineering.

Power engineering – generation, transmission and distribution of electricity, transformers, electric generators, electric motors, high voltage engineering and power electronics.
Control engineering – dynamic systems, design of controllers, electrical circuits, digital signal processors, microcontrollers and PLCs (Programmable Logic Controllers), industrial automation. Electronic Engineering -Electronic circuits, resistors, capacitors, inductors, diodes and transistors, radio engineering, radar, television, audio systems, computers and microprocessors.
Microelectronics – Electronic circuit, integrated circuit, semiconductor transistors, resistors, capacitors, inductors, Nanoelectronics.

Signal processing – signals, analog, digital, telecommunications, Digital Signal Processing, SDTV | HDTV sets, radios and mobile communication devices, Hi-Fi audio equipments, Dolby noise reduction algorithms, GSM mobile phones, mp3 multimedia players, camcorders and digital cameras, automobile control systems, noise cancelling headphones, digital spectrum analyzers, intelligent missile guidance, radar, GPS based cruise control systems, image processing, video processing, audio processing and speech processing systems.

Retinal image having very vital information. It plays important roles in finding of some diseases in early stages, such as diabetes, and cardiovascular disease. In this proposed system a new algorithm used to detect the blood vessels effectively from the retinal image. The initial image enhancement is carried out by using Adaptive Histogram Equalization, followed by the curvelet Transforms are applied to the equalized image and the curvelet coefficients are obtained. The modifications to the Curvelet transform coefficients are carried out by suppressing all the coefficients of one band. This combined effect of the equalization and the Curvelet Transforms provides a better enhancement to the image. This enhanced image is used for the extraction of blood vessels. Afterward, eliminate the ridges not belonging to the vessels tree by morphological operators by reconstruction while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multi-structure elements and local adaptive thresholding method along with connected components analysis (CCA) indicates the remained ridges belonging to vessels.