A wide interest has been observed to find a low power and area efficient hardware design of discrete cosine transform (DCT) algorithm. This research work proposed a novel Common Subexpression Elimination (CSE) based pipelined architecture for DCT, aimed at reproducing the cost metrics of power and area while maintaining high speed and accuracy in DCT applications. The proposed design combines the techniques of Canonical Signed Digit (CSD) representation and CSE to implement the multiplier-less method for fixed constant multiplication of DCT coefficients. Furthermore, symmetry in the DCT coefficient matrix is used with CSE to further decrease the number of arithmetic operations. This architecture needs a single-port memory to feed the inputs instead of multiport memory, which leads to reduction of the hardware cost and area. From the analysis of experimental results and performance comparisons, it is observed that the proposed scheme uses minimum logic utilizing mere 340 slices and 22 adders. Moreover, this design meets the real time constraints of different video/image coders and peak-signal-to-noise-ratio (PSNR) requirements. Furthermore, the proposed technique has significant advantages over recent well-known methods along with accuracy in terms of power reduction, silicon area usage, and maximum operating frequency by 41%, 15%, and 15%, respectively.
A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.
Exploring novel biological anti-quorum sensing (QS) agents to control membrane biofouling is of great worth in order to allow sustainable performance of membrane bioreactors (MBRs) for wastewater treatment. In recent studies, QS inhibitors have provided evidence of alternative route to control membrane biofouling. This study investigated the role of Piper betle extract (PBE) as an anti-QS agent to mitigate membrane biofouling. Results demonstrated the occurrence of the N-acyl-homoserine-lactone (AHL) autoinducers (AIs), correlate QS activity and membrane biofouling mitigation. The AIs production in bioreactor was confirmed using an indicator strain Agrobacterium tumefaciens (NTL4) harboring plasmid pZLR4. Moreover, three different AHLs were found in biocake using thin layer chromatographic analysis. An increase in extracellular polymeric substances (EPS) and transmembrane pressure (TMP) was observed with AHL activity of the biocake during continuous MBR operation, which shows that membrane biofouling was in close relationship with QS activity. PBE was verified to mitigate membrane biofouling via inhibiting AIs production. SEM analysis further confirmed the effect of PBE on EPS and biofilm formation. These results exhibited that PBE could be a novel agent to target AIs for mitigation of membrane biofouling. Further work can be carried out to purify the active compound of Piper betle extract to target the QS to mitigate membrane biofouling.
Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.
Increasing heavy metal (HM) concentrations in the soil have become a significant problem in the modern industrialized world due to several anthropogenic activities. Heavy metals (HMs) are non-biodegradable and have long biological half lives; thus, once entered in food chain, their concentrations keep on increasing through biomagnification. The increased concentrations of heavy metals ultimately pose threat on human life also. The one captivating solution for this problem is to use green plants for HM removal from soil and render it harmless and reusable. Although this green technology called phytoremediation has many advantages over conventional methods of HM removal from soils, there are also many challenges that need to be addressed before making this technique practically feasible and useful on a large scale. In this review, we discuss the mechanisms of HM uptake, transport, and plant tolerance mechanisms to cope with increased HM concentrations. This review article also comprehensively discusses the advantages, major challenges, and future perspectives of phytoremediation of heavy metals from the soil.