Anil Kumar Tiwari

Bio: Anil Kumar Tiwari is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topic(s): Lossless compression & Data compression. The author has an hindex of 14, co-authored 85 publication(s) receiving 560 citation(s). Previous affiliations of Anil Kumar Tiwari include Indian Institutes of Technology & LNM Institute of Information Technology.

Heart monitoring systems—A review

Abstract: To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.

A survey on lossless image compression methods

Abstract: In this paper we are describing some important state-of the-art algorithms used for lossless compression of images. These algorithms are broadly classified as prediction based methods and transform based methods. Motivation behind this work is to provide a detailed analysis of such algorithms and to give future research direction based on the analysis to the new researchers.

A novel approach for phonocardiographic signals processing to make possible fetal heart rate evaluations

Abstract: In this paper, a non-invasive, portable and inexpensive antenatal care system is developed using fetal phonocardiography. The fPCG technique has the potential to provide low-cost and long-term diagnostics to the under-served population. The fPCG signal contains valuable diagnostic information regarding fetal health during antenatal period. The fPCG signals are acquired from the maternal abdominal surface using a wireless data acquisition and recording system. The diagnostic parameters e.g., baseline, variability, acceleration and deceleration of the fetal heart rate are derived from the fPCG signal. A model based on adaptive neuro-fuzzy inference system is developed for the evaluation of fetal health status. To study the performance of the developed system, experiments were carried out with real fPCG signals under the supervision of medical experts. Its performance is found to be in close proximity with the widely accepted Doppler ultrasound based fetal monitor results. The overall performance shows that the developed system has a long-term monitoring capability with very high performance to cost ratio. The system can be used as first screening tool by the medical practitioners.

An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal

Abstract: Segmentation of the phonocardiography (PCG) signal into cardiac cycles is a primary task for the diagnosis of cardiovascular diseases. However, PCG is highly susceptible to noise, and extra sound called murmur may also be present in the PCG signal due to pathology. These components cause difficulties in the segmentation and therefore, segmentation is often preceded by the denoising of the PCG signal to emphasize the fundamental heart sounds S1 and S2, by removing these unwanted components. For the denoising of the PCG signal, discrete wavelet transform (DWT) based algorithms have shown good performance because such algorithms suppress in-band noise besides the out-of-band noise. Selection of threshold value and threshold function significantly affects the performance of these algorithms. In this paper, for threshold value estimation, an adaptive method based on statistical parameters of the given PCG signal is proposed. The statistical parameters are found to be highly effective for this purpose. We also propose a new threshold function, non-linear mid function, to address the issues of SNR and transients in the existing threshold functions, soft and hard. The proposed method is applied on a large number of PCG signals with additive white Gaussian noise, red noise, and pink noise. The Performance of the proposed method is also evaluated on the PCG signals recorded in real-life noisy scenarios and signals with murmur sound. The obtained results show that the proposed method is significantly superior to the competitive algorithms.

A review and comparative analysis of recent advancements in fetal monitoring techniques.

Abstract: Over the past few years, various devices and techniques have been developed for electronic fetal monitoring (EFM), which is performed during pregnancy or continuously during labor to ensure normal delivery of a healthy baby. We reviewed and analyzed the performance of currently used EFM techniques with the goal of determining a noninvasive, cost-effective alternative for use in the home environment. This review includes research papers, publications, web sources, product manuals, interviews, formal discussions, and other available literature with the goal of providing a comprehensive comparative analysis of all available EFM techniques. We relate some of the insights gained from reviewing a large number of resources.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Abstract: The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

Autonomic tone as a cardiovascular risk factor: The dangers of chronic fight or flight. Editorial

Abstract: Chronic imbalance of the autonomic nervous system is a prevalent and potent risk factor for adverse cardiovascular events, including mortality. Although not widely recognized by clinicians, this risk factor is easily assessed by measures such as resting and peak exercise heart rate, heart rate recovery after exercise, and heart rate variability. Any factor that leads to inappropriate activation of the sympathetic nervous system can be expected to have an adverse effect on these measures and thus on patient outcomes, while any factor that augments vagal tone tends to improve outcomes. Insulin resistance, sympathomimetic medications, and negative psychosocial factors all have the potential to affect autonomic function adversely and thus cardiovascular prognosis. Congestive heart failure and hypertension also provide important lessons about the adverse effects of sympathetic predominance, as well as illustrate the benefits of β-blockers and angiotensin-converting enzyme inhibitors, 2 classes of drugs that reduce adrenergic tone. Other interventions, such as exercise, improve cardiovascular outcomes partially by increasing vagal activity and attenuating sympathetic hyperactivity.

WiFi Sensing with Channel State Information: A Survey

Abstract: With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.

International journal of electronics and communication engineering & technology (ijecet)

Abstract: Compression methods are important in many medical applications to ensure fast interactivity through large sets of images (e.g. volumetric data sets, image databases), for searching context dependant images and for quantitative analysis of measured data. Medical data are increasingly represented in digital form. The limitations in transmission bandwidth and storage space on one side and the growing size of image datasets on the other side has necessitated the need for efficient methods and tools for implementation. Many techniques for achieving data compression have been introduced. In this study we propose context based adaptive lossless image codec.(CALIC)(12)