Quad-Band 1 × 4 Linear MIMO Antenna for Millimeter-Wave, Wearable and Biomedical Telemetry Applications.

In this paper, we present the design of a millimeter-wave 1 × 4 linear MIMO array antenna that operates across multiple resonance frequency bands: 26.28-27.36 GHz, 27.94-28.62 GHz, 32.33-33.08 GHz, and 37.59-39.47 GHz, for mm-wave wearable biomedical telemetry application. The antenna is printed on a flexible substrate with dimensions of 11.0 × 44.0 mm2. Each MIMO antenna element features a modified slot-loaded triangular patch, incorporating 'cross'-shaped slots in the ground plane to improve impedance matching. The MIMO antenna demonstrates peak gains of 6.12, 8.06, 5.58, and 8.58 dBi at the four resonance frequencies, along with a total radiation efficiency exceeding 75%. The proposed antenna demonstrates excellent diversity metrics, with an ECC < 0.02, DG > 9.97 dB, and CCL below 0.31 bits/sec/Hz, indicating high performance for mm-wave applications. To verify its properties under flexible conditions, a bending analysis was conducted, showing stable S-parameter results with deformation radii of 40 mm (Rx) and 25 mm (Ry). SAR values for the MIMO antenna are calculated at 28.0/38.0 GHz. The average SAR values for 1 gm/10 gm of tissues at 28.0 GHz are found to be 0.0125/0.0079 W/Kg, whereas, at 38.0 GHz, average SAR values are 0.0189/0.0094 W/Kg, respectively. Additionally, to demonstrate the telemetry range of biomedical applications, a link budget analysis at both 28.0 GHz and 38.0 GHz frequencies indicated strong signal strength of 33.69 dB up to 70 m. The fabricated linear MIMO antenna effectively covers the mm-wave 5G spectrum and is suitable for wearable and biomedical applications due to its flexible characteristics.

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis.

Problem: With the rapid advancement of remote sensing technology is that the need for efficient and accurate crop classification methods has become increasingly important. This is due to the ever-growing demand for food security and environmental monitoring. Traditional crop classification methods have limitations in terms of accuracy and scalability, especially when dealing with large datasets of high-resolution remote sensing images. This study aims to develop a novel crop classification technique, named Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC) for analyzing remote sensing images. The objective is to achieve high classification accuracy for various food crops. Methods: The proposed DTODCNN-CC approach consists of the following key components. Deep convolutional neural network (DCNN) a GoogleNet architecture is employed to extract robust feature vectors from the remote sensing images. The Dipper throated optimization (DTO) optimizer is used for hyper parameter tuning of the GoogleNet model to achieve optimal feature extraction performance. Extreme Learning Machine (ELM): This machine learning algorithm is utilized for the classification of different food crops based on the extracted features. The modified sine cosine algorithm (MSCA) optimization technique is used to fine-tune the parameters of ELM for improved classification accuracy. Results: Extensive experimental analyses are conducted to evaluate the performance of the proposed DTODCNN-CC approach. The results demonstrate that DTODCNN-CC can achieve significantly higher crop classification accuracy compared to other state-of-the-art deep learning methods. Conclusion: The proposed DTODCNN-CC technique provides a promising solution for efficient and accurate crop classification using remote sensing images. This approach has the potential to be a valuable tool for various applications in agriculture, food security, and environmental monitoring.

Heuristic Classifier for Observe Accuracy of Cancer Polyp Using Video Capsule Endoscopy

Methods: Colonoscopy is a technique for examine colon cancer, polyps. In endoscopy, video capsule is universally used mechanism for finding gastrointestinal stages. But both the mechanisms are used to find the colon cancer or colorectal polyp. The Automatic Polyp Detection sub-challenge conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org). Method: Colonoscopy may be primary way of improve the ability of colon cancer detection especially flat lesions. Which otherwise may be difficult to detect. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics, detecting polyps automatically in colonoscopy is a hard problem. So the proposed video capsule cam supports to diagnose the polyps accurate and easy to identify its pattern. Existing methodology mainly concentrated on high accuracy and less time consumption and it uses many different types of data mining techniques. To analyse these high resolution video scale image we have to take segmentation of image in pixel level binary pattern with the help of a mid-pass filter and relative gray level of neighbours. This work consists of three major steps to improve the accuracy of video capsule endoscopy such as missing data imputation, high dimensionality reduction or feature selection and classification. The above steps are performed using a dataset called endoscopy polyp disease dataset with 500 patients. Our binary classification algorithm relieves human analyses using the video frames. SVM has given major contribution to process the dataset. Results: In this paper the key aspect of proposed results provide segmentation, binary pattern approach with Genetic Fuzzy based Improved Kernel Support Vector machine (GF-IKSVM) classifier. The segmented images all are mostly round shape. The result is refined via smooth filtering, computer vision methods and thresholding steps. Conclusion: Our experimental result produces 94.4% accuracy in that the proposed fuzzy system and genetic Fuzzy, which is higher than the methods, used in the literature. The GF-IKSVM classifier is well-organized and provides good accuracy results for patched VCE polyp disease diagnosis.

Determination of genotoxic impurity in atazanavir sulphate drug substance by LC-MS.

A sensitive LC-MS method was developed for the determination of tert-butyl 2-[4-(pyridine-2-yl) benzyl] hydrazine carboxylate (GTI-A), a genotoxic impurity in Atazanavir sulphate drug substance. The method was validated as per International Council for Harmonization guidelines, for QL, DL, linearity and accuracy. The QL and DL values obtained were 1.1ppm and 0.3ppm respectively. The Correlation coefficient found for the linearity study was 0.999. The % recovery of the added impurity in the range of 96.4-100.4 ensured the accuracy of the method.

Automatic Colorectal Polyp Detection in Colonoscopy Video Frames

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

Immunogenicity of human and bovine insulin in type 1 diabetes mellitus patients.

BACKGROUND: The advantages of synthetic insulin (human insulin) over bovine insulin in the treatment of type 1 diabetes mellitus (DM) are much debated in terms of potency and purity. Immunogenicity is one of several factors that determine potency and safety. This study was designed to investigate and study the difference in immunogenicity of human and bovine insulin. We investigated anti-insulin antibody (AIAB) status in 69 type 1 DM patients receiving insulin therapy. Group 1 had 33 patients treated with bovine insulin, and group 2 had 32 patients treated with human insulin. All patients had received their respective insulin therapy for a minimum period of 1 year and had no history of change in insulin type. Forty-three subjects from the normal population were the control group. METHODS: AIABs were assayed in serum samples of all subjects using a semiquantitative radioimmunoassay kit. The Kruskal-Wallis non-parametric and Mann-Whitney U tests were used to study the difference in immunogenicity of human and bovine insulins. RESULTS: The Kruskal-Wallis test showed that antibody titers in the three groups significantly differed (P<0.001). The Mann-Whitney U test showed no significant difference in AIAB titer between the treatment groups. AIAB titers in the two treatment groups differed significantly from that of the control group, independently (P<0.001). High titers of AIABs are present in patients receiving bovine and human insulin compared to that of the normal population. CONCLUSIONS: Bovine and human insulins are antigenic, and there is no significant difference in AIAB titer. Prospective studies are required to determine the long-term clinical significance of these antibodies.