Malaysian Multidisciplinary Journal

Abstract -- In the current scenario, there is an increased demand for antennas since it plays a vital role in communication systems. Multiband, Wideband and low profile antennas are in great demand for both commercial and military applications. Fractal antennas provide solution for fabrication of single small antenna operating over several frequencies. The term fractal means broken or irregular fragments which were originally coined by Mandelbrot. Fractals are geometric shapes that repeat itself over a variety of scale sizes, so the shape looks the same at different scales. It is also called as self-similar pattern. They have wild properties, like having a finite area but infinite perimeter. A self-similar object appears unchanged after increasing or shrinking its size. Similarity and scaling can be obtained using different algorithms In this work, Koch antenna is proposed. The parameters studied in this paper are return loss, gain. The targeted frequency of this antenna is 8.8 GHz for Radio location application and WPAN (Wireless Personal Area Application). It can be used for several applications in X-band (ranges from 8-12 GHz) and Ku-band (ranges from 12-18 GHz). The antenna resonates at 8.8 GHz, 10.8 GHz, 13.02 GHz and 15.23 GHz. The maximum gain observed is near about -32 dB. This proposed design is simulated using Ansoft High Frequency Simulated structure software.

Abstract -- Cancer is One of the important diseases that everyone should be aware of. Besides of many technologies that have been implemented for the treatment of cancer patients, it is necessary to create databases to store cancer patients therapy status. Such databases are implemented in some specialized hospitals using DBMS to store and retrieve the current therapy status of the patients. Usually DBMS is used to overcome the usage of hardcopy of cancer patients in hospitals, it is capable of storing the data over a regional space due to the lack of storage and data speed that is stored and retrieved. Here big data can store up to 1e+12 zeta byte of information in it, and is capable of restoring the data faster. Having many technologies for treatment of cancer patient, implementation of a database through big data can give a solution to access the therapy status of a group of patients through a good looking graphical format and can be used for analytical proposes in various platforms by various therapists.

Abstract – The most popular intravenous therapy plays a major role in the management of patients who are critically ill. Saline bottle level is very important because when the bottle is emptied and the needle is not removed from the vein then the blood flows outward into the bottle. In hospitals, the nurses or caretakers are responsible for monitoring the saline bottle level. To prevent the accident due to the ignorance of caretakers and to provide remote for telehealth service, we have proposed the cost-effective saline water monitoring device which including the combination of sensor and Internet of things (IoT). The system which can automatically monitor the saline flow rate. It can wirelessly send the data to nurse’s or doctor’s smart phone with the help of GSM Module and display the results in the form of saline weight in the LCD display unit. The system is reliable, cost effective and convenient for nurses. It can reuse for the next saline bottle. Nurses can easily can easily monitor the saline level from distance. It is mainly advantageous at night timing as there is no need for nurses to go to patient’s bed to check the weight of saline in the bottle.

Abstract -- A brain tumour is one of the most dangerous causes of death on the planet.as a result, it's critical to catch it as soon as feasible. any ways have been presented to forecast and segment the tumor. However, they have a number of issues, including the need for a specialist's assistance, the long run-time required, and the selection of an acceptable feature extractor. To overcome these challenges, we suggested a convolutional neural network-based technique for predicting and segmenting a brain tumour simultaneously. The plan was split into two halves. To begin, we employed a basic binary annotation to reflect the presence or absence of the tumour in order to avoid using a tagged image, which would imply a professional intervention. Second, we submitted the prepared image data into our deep learning model, which produced the final classification; if the classification showed the presence of the tumour, the brain tumour was segmented using the feature representations generated by the convolutional neural network designs.

Abstract -- A patient with mild COVID-19 infection may have the possibility of the infection becoming severe or critical in the future. The identification of the COVID-19 severity of a patient is important for early prevention and for the treatment planning. The aim of our project is to predict the risk of the COVID-19 affected person by using improved LSTM (Long Short Term Memory), which is an artificial recurrent neural network architecture used in the field of deep learning.

Abstract -- This Covid 19(Coronavirus Disease 2019) pandemic has really challenged the medical fraternity as well as scientists all over the world. All are trying to find a permanent solution for this dreaded RNA virus which is resistant to most of the drugs. Currently, all SARS-CoV-2 detection measures make use of RT-PCR test, and its results are generally reported as either positive or negative, which tells us if a person is infected or not. It’s important to know that the RT-PCR test does not provide the additional measure of the viral load in the sample. So, we felt that the diagnosis of COVID 19 must be made simple, painless and with less chance of cross infection of spread to the doctors, nurses, paramedics and all the frontline warriors. In this study we introduce the non-invasion technique to identify the COVID-19 using nano potential of the skin and the other blood parameters.

Abstract -- Smart farming is controlling Plant growth using RGB Lights. We can control plant growth using RGB lights in the photosynthesis process. Grow light is an electric light to help plants grow. Grow lights either attempt to provide a light spectrum similar to that of the sun, or to provide a spectrum that is more tailored to the needs of the plants being cultivated. Outdoor conditions are mimicked with varying colour, temperatures, and spectral outputs from the grow light, as well as varying the intensity of the lamps. Depending on the type of plant being cultivated, the stage of cultivation (e.g. the germination/vegetative phase or the flowering/fruiting phase), and the photoperiod required by the plants, specific ranges of the spectrum, luminous efficacy, and colour temperature are desirable for use with specific plants and time periods.

Abstract -- This process presents a very large-scale integration (VLSI) circuit design of a micro control unit (MCU) for wireless body sensor networks (WBSNs) in cost-intention. The proposed MCU design consists of an asynchronous interface, a multisensory controller, a register bank, a hardware-shared filter, a lossless compressor, an encryption encoder, an error correct coding (ECC) circuit, a universal asynchronous receiver/transmitter interface, a power management, and a QRS complex detector. A hardware-sharing technique was added to reduce the silicon area of a hardware-shared filter and provided functions in terms of high-pass, low-pass, and band-pass filters according to the uses of various body signals. The QRS complex detector was designed for calculating QRS information of the ECG signals. In addition, the QRS information is helpful to obtain the heart beats. The lossless compressor consists of an adaptive trending predictor and an extensible hybrid entropy encoder, which provides various methods to compress the different characteristics of body signals adaptively. Furthermore, an encryption encoder based on an asymmetric cryptography technique was designed to protect the private physical information during wireless transmission. The proposed MCU design in this paper contained 7.61k gate counts and consumed 1.33 mW when operating at 200 MHz by using a 90-nm CMOS process. Compared with previous designs, this paper has the benefits of increasing the average compression rate by over 12% in ECG Signal, providing body signal analysis and enhancing security of the WBSNs.

Abstract -- The blood leakage is a critical problem during Haemodialysis therapy. One of the preventive methods to stop the accident associated with the blood leakage is to install a blood leakage monitoring system. This monitoring system is intended for use by the haemodialysis patients with kidney disorder. The aim of this paper is to present such a design that can automatically detect the blood leakage occurrence on the atriovenous fistula and alert the patient about blood leakage by sound and warning light. This system consists of optical sensor, Microcontroller, Bluetooth Module, Alert Components (LED & buzzer) and Computer. To adsorb the blood due to venous needle dislodgement the absorbent material is used. The sensor detects the light intensity of the absorbent material and gives the output accordingly. In microcontroller, the simple algorithm that defining the sensing of Red color only is made to evaluate. Thus the system gives the result by sensing red color only. When the blood accumulates the absorbent material, the microcontroller enables the alert components and also this blood leakage occurrence is transmitted to the computer via Bluetooth module. Thus the healthcare workers can take essential action immediately to prevent the undue blood loss during Haemodialysis therapy.

Abstract -- Facial Expression conveys non-verbal cues, which plays an important role in interpersonal relations. The Cognitive Emotion AI system is the process of identifying the emotional state of a person. The main aim of our study is to  develop a robust system which can detect as well as recognize human emotion from live feed. There are some emotions which are universal to all human beings like angry, sad, happy, surprise, fear, disgust and neutral. The methodology of this system is based on two stages- facial detection is done by extraction of Haar Cascade features of a face using Viola Jones algorithm and then the emotion is verified and recognized using Artificial Intelligence Techniques. The system will take image or frame as an input and by providing the image to the model the model will perform the preprocessing and feature selection after that it will be predict the emotional state.

Abstract-- In today’s busy world time is a vital issue which can’t be managed by noticing each and every phenomenon with our tight schedule. So, now a day’s automatic systems are being preferred over manual system to make life simpler and easier in all aspects. In this world one of the most important aspects for developed countries is infrastructure. But in India, there is no awareness to make it a grand success of infrastructure achievements. The main aim of this work is to explore creativity in developing the SMART GARBAGE SYSTEM. In this module, it has placed automatic door opening system to segregate the waste in the area surroundings to achieve the overloading of garbages in the street. This work implemented the weighing machine to control the garbage system and intimate to the government for the clearance of garbages immediately. It fulfils the system called vacuum which can observe the garbages in the corner of the road and store it in public tank. The main aim is to perform automatic lifting for the tank to help the employees in solid waste management system. This work is a review of advance garbage system based on RFID. From this “Indian peoples should be the part of the solutions, not the pollutions”.

Abstract-- Eyes play a vital role in the life of human being as they have the ability to receive and process visual details to the brain. It is estimated that 83% of information from the environment is obtained through the eyes. There are various disabilities, in which blindness is one among them, in which a person needs to face several problems despite a variety of technological advancements. About 285 million people of visually impaired worldwide: 246 million have low vision (severe or moderate visual impairment) and 39 million are blind. Over 39 million people across the globe are visually impaired, among which one in three of them is an Indian, visually impaired people feel difficult to identify objects. The main aim of the project is to is to develop a low cost, rechargeable and efficient walking stick which help the visually impaired people to detect obstacles and recognize the name of the obstacle which would help them to do their daily work easier and smoother without any disturbances. The proposed walking stick can identify the name of the object and give the voice feedback as instruction. In case the visually impaired person feels uncomfortable the stick helps in generating buzzer notification to indicate the emergency condition to the surroundings which help them to lead a perfect life without the help of instructors, which help them to reach the destination safely.

Abstract-- Automatic cancer detection and segmentation is main topic for the computer-aided diagnosis of lung cancers in CT images. However, It is a complex work in low-contrast images as the low-level images are weak to detect. In this project, we propose a new technique. Finally we use shape constraint to reduce noise and identify focal cancers. Lung cancer is one of the most killer diseases developing countries and the detection of the cancer at the early stage is a challenge. Analysis and cure of lung malignancy have been one of the greatest difficulties faced by humans over the most recent couple of decades. Early identification of tumor would facilitate in sparing a huge number of lives over the globe consistently. This paper presents an approach which utilizes a Convolution Neural Network (CNN) to classify the tumors in lung as malignant or benign. The accuracy obtained by means of CNN is 96%, which is more efficient when compared to accuracy obtained by the traditional neural network systems.

Abstract-- Dental radiographs have been widely used by dentists in monitoring the progress of the periodontal defect treatment. In digital dental X-rays the regions of the cysts are of low contrast and the pixel intensity distribution is not homogenous so cyst segmentation is a complex problem in digital dental radiography. To efficiently analyse the cyst images we need an automatic segmentation, which can simplify the data analysis and could be used for further feature extraction and computerized diagnostic support system design, in particular to detect potentially precancerous lesions. In this paper the proposed algorithm (Dual Threshold Binary Decomposition) DTBD works well in the segmentation of multiple dental cyst in dental x-ray images. The proposed automated segmentation is most efficient and fast when compared to other parametric and non parametric thresholding based segmentation. After segmentation , the feature extraction algorithm is very easier to measure the circularity, gray level distribution and area can be calculated for diagnosis. This proposed algorithm will help the dentist to give treatment based on the severity measurement.

Abstract-- In this project we proposed the pre- excellence unsurpassed filter is denoised the speckle affected images. The denoising is achieved by understanding the performance of various speckle filters and selecting appropriate filter to remove speckle for a given arbitrary noisy image. The selection of excellence filter is achieved by estimating variance of the noisy image. We used 20 types of denoising filters to identify the pre-excellence unsurpassed speckle removal filters based on noise variance. The result shows that the proposed preexcellence unsurpassed filter chooses the best two filters to despeckled noisy image based on noise variance.

Abstract-- In this project, we develop a leaf Disease Detection using a Deep Convolutional neural network. in India, Agricultural productivity is something on which the economy highly depends. This is one of the reasons that disease detection in plants plays an important role in the agriculture field, as having the disease in plants is quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity, or productivity is affected.

Abstract-- The next generation Internet of Things (IoT) exhibits a unique feature that IoT devices have different energy profiles and quality of service (QoS) requirements. Here, the two energy and spectrally efficient transmission strategies, namely wireless power transfer assisted non-orthogonal multiple access (WPT - NOMA) and backscatter communication(Back-Com)assisted NOMA (BAC-NOMA), are proposed by utilizing this feature of IoT and employing spectrum and energy cooperation among the devices. In particular, the use of NOMA ensures that the devices with different QoS requirements can share the same spectrum, and WPT and Back-Com are employed to utilize the cooperation among the devices with different energy profiles. This concept enough for group of people at same location. For different location we need to improve the outage probability and frequency efficiency. Achieving this process by SWIPT-NOMA.

Abstract-- As a biometric trait, finger vein pattern-based technology is highly effective for personal identification with high security. In this project, we presented the design of a personal identification system based on near infrared (NIR) iris vein image. In this project, we introduced an observation model of iris vein imaging, upon which a self-adaptive illuminance control algorithm is proposed system simultaneously acquires the iris-vein using a novel score-level combination strategy. The proposed algorithm could automatically adjust the illuminance distribution of lighting: increase the illuminance of lighting, under which the thicker part of iris body is presented and decrease the illuminance of lighting, under which the thinner part of iris body is presented. We develop and investigate two new score-level combinations, i.e., holistic and nonlinear fusion, and comparatively evaluate them with more popular score-level fusion approaches to ascertain their effectiveness in the proposed system.

Abstract-- A patient with mild COVID-19 infection may have the possibility of the infection becoming severe or critical in the future. The identification of the COVID-19 severity of a patient is important for early prevention and for the treatment planning. The aim of our project is to predict the risk of the COVID-19 affected person by using improved LSTM (Long Short Term Memory), which is an artificial recurrent neural network architecture used in the field of deep learning.

Abstract-- This Covid 19(Coronavirus Disease 2019) pandemic has really challenged the medical fraternity as well as scientists all over the world. All are trying to find a permanent solution for this dreaded RNA virus which is resistant to most of the drugs. Currently, all SARS-CoV-2 detection measures make use of RT-PCR test, and its results are generally reported as either positive or negative, which tells us if a person is infected or not. It’s important to know that the RT-PCR test does not provide the additional measure of the viral load in the sample. So, we felt that the diagnosis of COVID 19 must be made simple, painless and with less chance of cross infection of spread to the doctors, nurses, paramedics and all the frontline warriors. In this study we introduce the non-invasion technique to identify the COVID-19 using nano potential of the skin and the other blood parameters.

Abstract-- Hydroponics refers to the art of growing plants in water without soil. Nutrients for the plants are supplied to the roots in the form of solution. The limitation in green house environment is to maintain the temperature, nutrition value at a particular level. The automation of nutrition level and temperature maintenance is done. IOT is used to transfer the retrieved data to the internet. The purpose of this project is to make it easier to grow plants domestically all year round. The objective is to construct a remotely controllable and environmentally independent automated hydroponic system. This would minimize the efforts required by the user to sustain plants in non-native climates. A hydroponic gardening system uses water as a growth medium instead of soil. The system is climate conscious and has benefits compared to conventional agriculture. Hydroponic systems are for regulating the nutrient concentration through EC. The system uses a microcontroller for analysis. The results are promising, showing that the system works. However, the limitations in time led to a short test period, therefore the data gathered is limited. The discussion based on the results conclude that the system cannot be considered completely automatic but reduces the need of manual labour.

Abstract-- Cognitive Radio (CR) is an intelligence system able to switch between radio access methods as well as transmitting in different portions of the radio spectrum. The reconfigurability of the CR passes through cognition tasks which are: sensing the spectrum, analyzing the spectrum, and making joint decisions on spectrum selections. Spectrum sensing (SS) is the first task of the CR life cycle that gains significance since the spectrum holes can be detected during this task. Spectrum sensing task operates in a Non-Cooperative spectrum sensing (Non-CSS) or a Cooperative spectrum sensing (CSS) modes, whereby the Secondary users (SUs) cooperate to determine the channel state. There are a plethora of works of spectrum sensing techniques in Cognitive radio networks (CRNs). Most of these types are classified as energy detection-based , cyclostation-Machinary matrix-based, and covariance-based techniques. Machine learning based techniques are another modern type of innovative spectrum sensing technique. In such methods, the sensing process in detecting the primary user’s activities passes through two phases which are: the feature extraction phase and the decision-making phase.

Abstract-- A reconfigurable intelligent surface (RIS) is a promising solution to build a programmable wireless environment via steering the incident signal in fully customizable ways with reconfigurable passive elements. In this work, a RIS aided multiuser multiple-input single-output (MISO) downlink communication system is considered. The objective is to maximize the sum-rate (SR) of all users by joint designing the beam forming at the access point (AP) and the phase vector of the RIS elements. A low-complexity algorithm is proposed to obtain the stationary solution for the joint design problem by utilizing the fractional programming technique. The main difference is that, the RIS can only control and optimize the behavior of the wireless environment, and has no capability to suppress inter-user interference. Due to that, the beam forming design at the AP and phase optimization at the RIS are deeply coupled, and the convergence speed of the alternating optimization approach is slow. Therefore, the computational complexity in each iteration step should be low and scalable to the number of RIS elements.

Abstract-- Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalization of existing interfaces. To this end, we aim to create a BCI that decodes text directly from neural signals. We implement a framework that initially isolates frequency bands in the input signal encapsulating differential information regarding production of various phonemic classes. We provide empirical evidence that our interface achieves an average accuracy of 32% calculated against a full corpus, i.e. one encompassing all feasible English words that can be formulated using the entire set of phonemes uttered by a patient, These bands form a feature set that feeds into an LSTM which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities incorporating prior knowledge of the English language to output text corresponding to the decoded word.

Abstract-- Melanoma is a serious type of skin cancer disease that increases over the past decades in the world and as a sequel to curing strategy in the medical field, an automatic detection of skin lesions using dermoscopic images has been still a challenging and complicated task. This kind of difficulty occurs in the diagnosis of lesion on owing to the following factors such as: indistinct lesion borders, poor color contrast, location dependent, shape variations and complex structures of the lesions. The progressing public health burden issues have to be detected early and treated in proper ways to prevent further spreading to other organs of the body through which medical professionals and researchers can save several lives. When there is an abnormal change in the appearance of skin, then there is a chance for the subject that may be affected by melanoma. To obtain better solutions, the knowledge of dermatology has to be combined with computer vision techniques for efficient melanoma detection. Hence, it is important to develop various detection techniques to assist clinicians to diagnose melanoma at early stages.

Abstract-- This paper describes the gain improvement of a rectangular microstrip patch antenna by using double E-shaped patch. The antenna has been designed using Felt substrate because of its low dielectric constant at 1.45 and the permittivity of 0.02.The proposed antennas were analysed and simulated at the frequency of 5.64 GHz for Wireless Local Area Networks (WLAN) application using the Computer Simulation Technology (CST) software. The result shows that there is a gain enhancement (7.687dBi) in the final structure designed.

Abstract-- Face-recognition technologies are becoming increasingly popular in today's society. Facial recognition technology is widely used in security systems. It's crucial to consider the face-recognition system's ability to survive an unauthorized person's attack, though. The use of fake images and videos can compromise face recognition systems. The aim of this paper was to develop Face liveness detection. Face liveness detection's primary goal is to differentiate between a real and a fake face and also to detect whether it’s live or not. Face anti-spoofing is essential to prevent face recognition systems from a security breach. Although face anti-spoofing detection methods have been proposed so far, the problem is still unsolved due to the difficulty on the design of features and methods for spoof attacks. According to a 2018 report by the Center for Applied Internet Data Analysis (CAIDA), there are close to 30,000 spoofing attacks per day. Face liveness detection is a preprocessing step in face recognition for avoiding face spoofing attacks. To overcome this problem Convolutional Neural Networks (CNN) based Face liveness detection method is introduced. This proposed system classifies the real or fake face Anti-spoofing technologies come in help in these situations to avoid these assaults. Real and fake faces are distinguished using the obtained face embedding’s, which are then concatenated and delivered via Softmax layer for classification (Softmax layer as a Classifier). The proposed method for detecting spoofs has a test accuracy of 89% and a specificity of 90%.

Abstract-- A copy-move forgery in digital image is a type of passive technique it will contain a part of the copied image and pasted to another parts in the same image. This may be occurring by a forger to cover part of object or validity or to enhance the visual effect in the image. Nowadays, there are many advance editing software in digital image are used to tampering, the forger can easily tamper the image, as a result, the image truth or validity is lost. In this study we will introduce three scheme for copy-move forgery detection(CMFD) based on segmentation and comparing between them, we will discuss the Segmentation-Based Image CMFD, then, adaptive Over segmentation and Feature Point Matching, Finally, Multi-scale feature extraction and adaptive matching for CMFD.

Abstract-- To design of a Full Adder using Pass Transistors, Transmission Gates and Conventional Complementary Metal Oxide Semiconductor logic is presented. Performance analysis of the circuit has been conducted using Tanner software. For comparative analysis, the performance parameters have been compared with twenty existing FA circuits. The proposed FA has also been extended up to a word length of 64 bits in order to test its scalability. Only the proposed FA and five of the existing designs have the ability to operate without utilizing buffer in intermediate stages while extended to 64 bits. According to simulation results the proposed design demonstrates notable performance in power consumption and delay which accounted for low power delay product. Based on the simulation results it can be stated that the proposed FA circuit is an attractive alternative in the data path design of modern high-speed Central Processing Units.

Abstract-- Modern cryptography is heavily based on mathematical theory and computer science practice and the cryptographic algorithms are designed around computational hardness assumptions, making such algorithms hard to break in practice by any adversary. The hardware process to develop the cryptography function and to modification process to mainly based upon the internal gate architecture. The modified carry look-ahead adder based DCT architecture used to the cryptography applications. A fast algorithm for discrete cosine transforms (DCT)-domain image resizing is presented. To reduce computations, fast Wino grad DCTs are applied to a recently reported image resizing scheme that uses DCT low-pass truncated approximation. Our fast algorithm yields significant improvement in computational complexity over the fast algorithm of the reported method. Video processing systems such as HEVC requiring low energy consumption needed for the multimedia market has lead to extensive development in fast algorithms for the efficient approximation of 2-D DCT transforms. The DCT is employed in a multitude of compression standards due to its remarkable energy compaction properties. The proposed DCT approximation is a candidate for reconfigurable video standards such as HEVC. The proposed transform and several other DCT approximations are mapped to systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45 nm CMOS technology.