Browsing by Author "Ahmad, Shamim"

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A Low SAR In-Body Antenna for Wireless Monitoring Purpose of Pacemaker System
(Electrical Information and Communication Technology, 2019) Ahmad, Shamim; Hasan, Raja Rashidul; Hasan, Rakibul; Al Rakib, Md. Abdullah; Hasan, Md. Abid; Zubayar, Md
This research paper deals with a low SAR patch antenna that resonates at 2.415 GHz. This antenna can be employed to monitor the pacemaker system wirelessly, considering its performances. The main aim was to operate the antenna at ISM (Industrial, Scientific and Medical) band (2.4– 2.48 GHz) in the pacemaker system, where body granted materials were used to construct both the pacemaker and the antenna to ensure the biocompatibility. To investigate the antenna parameters’ changes between free space and in-side body condition, the designed antenna was examined and compared for both conditions. The key speciality of this design is that the SAR (Specific Absorption Rate), which is the most crucial parameter for any in-body antenna, was found in a considerable-safe region. Moreover, Computer Simulation Technology (CST)-based desired findings of this antenna for return loss, VSWR, and far-field radiation characteristics were found with compared to other recent body-implantable antenna related published works.
A Meandered Line Patch Antenna at Low Frequency Range for Early Stage Breast Cancer Detection
(Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2021) Al Rakib, Md Abdullah; Ahmad, Shamim; Kabir Khan, Md. Humayun; Haque, Mainul; Faruqi5, Tareq Mohammad; Jahan, Md Saroar; Mim, Jhuma Kabir
Every year a concerning number of women are affected by breast cancer which is one of the deadliest and common types of cancers. Breast cancer is curable at early stages. For detecting breast cancer, there are several methods such as MRI, Mammography, Tomography, Ultrasound, and biopsy are available in medical technology. Still, none of them are as easy and efficient as a microwave imaging technique, in this method, the antenna plays an important role. Therefore, this paper focuses on developing an antenna at a low-frequency range for microwave imaging techniques to detect cancerous tissue inside the breast. For this, the antenna parameters, i.e., return loss, VSWR, directivity, current density, and specific absorption rate were studied, by setting the antenna over without tumor and with tumor breast as upside-down, to ensure the compatibility of the antenna for the technique as well as for the patient’s body. A 5mm radius cancerous tumor was created inside the breast with dielectric conductivity of 4 and relative permittivity of 50. Cancerous cells were detected by reading the antenna parameters’ comparison between the healthy breast and the affected breast. The whole study was conducted by using CST MICROWAVE STUDIO SUITE 2020.
Design and Simulation-Based Parametric Studies of a Compact Ultra-Wide Band Antenna for Wireless Capsule Endoscopy System at Inside Body Environment
(International Journal of Electrical and Electronic Engineering & Telecommunications, 2020) Al Rakib, Md. Abdullah; Ahmad, Shamim; Faruqi, Tareq Mohammad; Haque, Mainul; Rukaia1, Sharifa Akter; Nazmi, Sumaiya
This paper focuses to design a compact (110mm³) Ultra-Wide Band (UWB) (3.1GHz to 10.6GHz) antenna, which covers almost the whole 10dB impedance matching bandwidth of the UWB range. Two of the main specialties of this article over other related articles are its antenna’s wider bandwidth (approx. 7.3GHz) and antenna’s simulation environment. No other papers consider such a realistic model to simulate their antenna, before. Due to its wider bandwidth, this antenna can be employed in the Wireless Capsule Endoscopy (WCE) system, which mainly requires a high-speed real-time data transfer-capable antenna. The antenna was examined inside simplified human Gastrointestinal (GI) tract phantoms (Colon, Esophagus, Small Intestine and Stomach) as well as the human Voxel GI tract model by maintaining proper tissue properties for the sake of accurate parametric results. Biocompatible material polyimide was used to construct the capsule wall to fulfill the system’s biocompatibility. In the result analysis part, the proposed antenna’s SAR (Specific Absorption Rate) or electromagnetic energy amount, consumed by near-side body tissue was considered and found in the acceptable region, according to Federal Communication Commission (FCC)’s regulation. Also, other crucial antenna parameters such as VSWR, reflection coefficient, radiation characteristics, efficiencies, directivity and surface current density were adoptable compare to other related articles. The Finite Integration Technique (FIT) of CST Microwave Studio Suite 2020 was used to investigate the antenna parameters.
Machine Learning and Bioinformatics Models to Identify the Genetic Link of Neurological Diseases Associated With the Causal Risk Factors
(University of Rajshahi, Rajshahi, 2021) Chowdhury, Utpala Nanda; Ahmad, Shamim; Islam, M. Babul; Moni, Mohammad Ali
Neurological diseases (NDs) are causing burgeoning burden to the patients, healthcare sector and the entire society. Hundreds of millions of people are currently a ected by various NDs worldwide and the number is increasing very rapidly. The most frequent categories include Alzheimer's disease (AD), Parkinson's disease (PD), epilepsy, Multiple sclerosis (MS), stroke and other cerebrovascular disorders, migraine and other headache, malignant brain tumors such as Glioblastoma multiforme (GBM) etc. Despite numerous research initiatives, preventive and therapeutic options for most of these NDs still remain very limited. Taking AD as an example, fully e ective preventive strategies are unavailable till now. Preventive measures usually comprise primary prevention based on risk reducing by identifying in uential factors and secondary prevention through early detection and abatement of the disease at initial stage. But the inadequate epidemiological knowledge of AD risk factors and absence of early premortem accurate diagnosis has foiled AD prevention. However, better insight about the co-occurrence of other neurological complications with AD can yield preventive and therapeutic advancement. On the other hand, enhanced understanding about the factors that impacts the response to the treatment could prolong the survival period. For instance, GBM is such an ND with shorten survival period provided the rst line treatment include brain surgery followed by chemotherapy and radiotherapy. In this context, research initiatives to mitigate the information gap regarding how the causative factors a ect the cell pathways altered in NDs and their comorbidities can alleviate the disease burden. Availability of high throughput technologies including microarray and next-generation sequencing (NGS) of tissue mRNA to analyse large-scale transcriptomic data have excelled various bioinformatics methodologies as promising tools in biomedical research eld. These approaches include di erential gene expression analysis, protein-protein interactions (PPIs), gene ontology (GO), metabolic pathway and regulatory factor analysis. Genetic inspection into the transcriptomic data through these tools yields better insight into the molecular pathogenesis of any health condition in junction with its causative factors and related complications. In addition to this, the exponentially increasing amount of accessible biological data has made machine learning techniques as promising means of discovering hidden genetic knowledge. In this thesis, we presented bioinformatics and computational frameworks based on transcriptomic data and machine learning based survival prediction models.
Multiple Kernel Learning And Its Application In Bioinformatics
(University of Rajshahi, 2017) Hasan, Md. Al Mehedi; Ahmad, Shamim; Molla, Md. Khademul Islam
During the last decades, the support vector machine (SVM) has been applied broadly within the field of computational biology or bioinformatics to answer biological questions and to reach valid biological conclusions. However, a successful application of SVM depends heavily on the determination of the right type and suitable parameter settings of kernel functions. The selection of the appropriate kernel and kernel parameters are both considered as the choice of kernel problem. Therefore, kernel learning becomes a crucial problem for all kernel-based methods like the SVM. Recently, the multiple kernel learning (MKL) has been developed to tackle the kernel learning problem efficiently and gives some scopes to improve the performance of a system. On the other hand, sometimes it is desirable to handle multiple data sources for pattern recognition in the field of bioinformatics. In this context, if these data sources are combined appropriately as one data source, it is then possible to provide a more "complete" representation of an entity which in turns, enhances the performance of a pattern recognition system. In this case, MKL also provides a way to combine features from various data sources, where each kernel will be dedicated to a particular type of data source. In order to use the above two advantages of MKL, we have applied MKL in two challenging problems in bioinformatics: protein subcellular localization prediction and protein post-translational modifications (PTMs) prediction. The knowledge of the subcellular localization and PTMs of proteins are important for both basic research and drug development. Recently various types of computational tools have been developed to predict the subcellular localization and PTMs or PTMs site of a protein through different types of machine learning algorithms. However, in order to meet the current demand of drug development and basic research, both of the above prediction systems require additional effort to produce efficient high-throughput tools. In our thesis work, we have applied MKL in order to give potential solution for the choice of kernel problem in one of the two mentioned applications of bioinformatics. In this case, the set of radial basis function (RBF) kernels (different values of sigma create different kernels) has been considered as the search space of the choice of kernel problem. Moreover, since both applications can be solved from various data sources, features from various sources are fused using multiple kernel learning with the expectation of better improvements. The experimental results show that the prediction systems using MKL based SVM provide better performance than other top existing systems in both applications. We have completed nine experiments throughout this thesis work. Where, four of those show the capability of single kernel based SVM, one shows the effects of the choice of kernel problem, one provides potential solution to the choice of kernel problem using MKL, finally, rest three show the application of MKL in handling multiple data sources. In addition to it, we have developed six user-friendly web servers for six specific prediction purposes as a product of these experiments.