TinyML: Tools, Applications, Challenges, and Future Research Directions

In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimization, improved reliability, and maintenance of learning models' accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, various research challenges and future directions are identified.Comment: 12 pags, 3 tables, 4 figure

DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks

With the growing demand for vision applications and deployment across edge devices, the development of hardware-friendly architectures that maintain performance during device deployment becomes crucial. Neural architecture search (NAS) techniques explore various approaches to discover efficient architectures for diverse learning tasks in a computationally efficient manner. In this paper, we present the next-generation neural architecture design for computationally efficient neural architecture distillation - DONNAv2 . Conventional NAS algorithms rely on a computationally extensive stage where an accuracy predictor is learned to estimate model performance within search space. This building of accuracy predictors helps them predict the performance of models that are not being finetuned. Here, we have developed an elegant approach to eliminate building the accuracy predictor and extend DONNA to a computationally efficient setting. The loss metric of individual blocks forming the network serves as the surrogate performance measure for the sampled models in the NAS search stage. To validate the performance of DONNAv2 we have performed extensive experiments involving a range of diverse vision tasks including classification, object detection, image denoising, super-resolution, and panoptic perception network (YOLOP). The hardware-in-the-loop experiments were carried out using the Samsung Galaxy S10 mobile platform. Notably, DONNAv2 reduces the computational cost of DONNA by 10x for the larger datasets. Furthermore, to improve the quality of NAS search space, DONNAv2 leverages a block knowledge distillation filter to remove blocks with high inference costs.Comment: Accepted at ICCV-Workshop on Resource-Efficient Deep Learning, 202

Comparative study of quality of life in breast cancer patients receiving two different chemotherapy regimens using European Organization for Research and Treatment of Cancer Quality of Questionnaire-Core 30 questionnaire module; for tolerability and safety

Background: Breast cancer is one of the most frequent occurring cancers in women and burgeoning worldwide. It is the second most common malignancy in India after carcinoma of the uterine cervix. In clinical trials, quality of life (QOL) outcome measurements is an important as endpoints with improving subjects physical, emotional, and social well-being.Methods: In this study, we were evaluated the comparison of the QOL in breast cancer patients on anthracycline-based regimen (six cycles of 5-fluorouracil, adriamycin, and cyclophosphamide [FAC] for a period of 18 weeks) and taxane-containing regimen (four cycles of adriamycin and cyclophosphamide [AC] followed by four cycles of paclitaxel [PTX] for a period of 24 weeks) using European Organization for Research and Treatment of Cancer Quality of Questionnaire-Core 30.Results: During first 3 months of therapy, both treatment groups exhibited a reduction in health-related QOL (HRQOL) with no clinically significant difference between them. The effect on HRQOL was less evident 3 weeks after completing chemotherapy with HRQOL of both groups returning to near baseline scores.Conclusions: Both treatment regimens (FAC and AC → PTX [AC followed by PTX]) were equally tolerated in patients

Synthesis and Characterization of Novel Oxime Analogues

Novel oxime analogs have been synthesized from the tricyclic scaffolds. A series of iminoesters were synthesized by reacting oximes with anti-inflammatory drugs such as Naproxen, Ibuprofen, Aspirin, Etodolac, Aceclofenac, Flurbiprofen in the presence of the coupling agent N,N′-dicyclohexylcarbodiimide

Arterial spin labeled MRI detects clinically relevant increases in myocardial blood flow with vasodilatation

ObjectivesThis study sought to determine whether arterial spin labeled (ASL) cardiac magnetic resonance (CMR) is capable of detecting clinically relevant increases in regional myocardial blood flow (MBF) with vasodilator stress testing in human myocardium.BackgroundMeasurements of regional myocardial perfusion at rest and during vasodilatation are used to determine perfusion reserve, which indicates the presence and distribution of myocardial ischemia. ASL CMR is a perfusion imaging technique that does not require any contrast agents, and is therefore safe for use in patients with end-stage renal disease, and capable of repeated or continuous measurement.MethodsMyocardial ASL scans at rest and during adenosine infusion were incorporated into a routine CMR adenosine induced vasodilator stress protocol and was performed in 29 patients. Patients who were suspected of having ischemic heart disease based on first-pass imaging also underwent x-ray angiography. Myocardial ASL was performed using double-gated flow-sensitive alternating inversion recovery tagging and balanced steady-state free precession imaging at 3-T.ResultsSixteen patients were found to be normal and 13 patients were found to have visible perfusion defect based on first-pass CMR using intravenous gadolinium chelate. In the normal subjects, there was a statistically significant difference between MBF measured by ASL during adenosine infusion (3.67 ± 1.36 ml/g/min), compared to at rest (0.97 ± 0.64 ml/g/min), with p < 0.0001. There was also a statistically significant difference in perfusion reserve (MBFstress/MBFrest) between normal myocardial segments (3.18 ± 1.54) and the most ischemic segments in the patients with coronary artery disease identified by x-ray angiography (1.44 ± 0.97), with p = 0.0011.ConclusionsThis study indicates that myocardial ASL is capable of detecting clinically relevant increases in MBF with vasodilatation and has the potential to identify myocardial ischemia

A Survey on Semantic Communications for Intelligent Wireless Networks

With deployment of 6G technology, it is envisioned that competitive edge of wireless networks will be sustained and next decade's communication requirements will be stratified. Also 6G will aim to aid development of a human society which is ubiquitous and mobile, simultaneously providing solutions to key challenges such as, coverage, capacity, etc. In addition, 6G will focus on providing intelligent use-cases and applications using higher data-rates over mill-meter waves and Tera-Hertz frequency. However, at higher frequencies multiple non-desired phenomena such as atmospheric absorption, blocking, etc., occur which create a bottleneck owing to resource (spectrum and energy) scarcity. Hence, following same trend of making efforts towards reproducing at receiver, exact information which was sent by transmitter, will result in a never ending need for higher bandwidth. A possible solution to such a challenge lies in semantic communications which focuses on meaning (context) of received data as opposed to only reproducing correct transmitted data. This in turn will require less bandwidth, and will reduce bottleneck due to various undesired phenomenon. In this respect, current article presents a detailed survey on recent technological trends in regard to semantic communications for intelligent wireless networks. We focus on semantic communications architecture including model, and source and channel coding. Next, we detail cross-layer interaction, and various goal-oriented communication applications. We also present overall semantic communications trends in detail, and identify challenges which need timely solutions before practical implementation of semantic communications within 6G wireless technology. Our survey article is an attempt to significantly contribute towards initiating future research directions in area of semantic communications for intelligent 6G wireless networks

HPLC-LIF for early detection of oral cancer

At present, the diagnosis of many cancers relies on the subjective interpretation of morphological changes in biopsy samples. This usually provides only late diagnosis. Early detection, which can provide more successful therapy, is expected to be possible by identification of tumour markers in physiological samples. Immunoassay used at present for this purpose has several drawbacks. It is applicable only for known markers, can usually detect only one marker at a time, and may also fail to detect a marker when there exist conditions, which may mask or prevent the interaction between antigen and the antibody. We have developed a high performance liquid chromatography- laser induced fluorescence (HPLC-LIF) technique to detect and record simultaneously spectra and chromatograms of physiological samples, which will enable the detection of multiple 'markers' in a single physiological sample in a short time. Samples of saliva and serum from normal and oral cancer subjects have been studied with the set up. The present studies show that body fluids like saliva and serum of normal, premalignant and malignant subjects have substantially different protein profiles. By simultaneous recording of the chromatographic peaks and corresponding fluorescence spectra, it is possible to carry out unambiguous discrimination between normal, premalignant and malignant cases even when markers are present in femto/subfemtomole quantities, which should assist in early diagnosis of neoplasia

Optical pathology of oral tissue: a Raman spectroscopy diagnostic method

Raman and fluorescence spectroscopy methods are being considered as techniques which could be complementary or even alternative to biopsy, and pathology and clinical assays in many medical applications. The present paper discusses the results of Raman spectral studies on oral tissues for optical pathology. It is shown that Raman spectra of oral tissues can be classified into spectra of normal and malignant sets and a model based on such a classification can be used to analyse oral tissue for detection of oral malignancy. Sensitivity and specificity calculated from 90 test spectra are better than 85 and 90 per cent respectively