An analyte monitor includes a sensor, a sensor control unit, and a display unit. The sensor has, for example, a substrate, a recessed channel formed in the substrate, and conductive material disposed in the recessed channel to form a working electrode. The sensor control unit typically has a housing adapted for placement on skin and is adapted to receive a portion of an electrochemical sensor. The sensor control unit also includes two or more conductive contacts disposed on the housing and configured for coupling to two or more contact pads on the sensor. A transmitter is disposed in the housing and coupled to the plurality of conductive contacts for transmitting data obtained using the sensor. The display unit has a receiver for receiving data transmitted by the transmitter of the sensor control unit and a display coupled to the receiver for displaying an indication of a level of an analyte. The analyte monitor may also be part of a drug delivery system to alter the level of the analyte based on the data obtained using the sensor.

Analyte Monitoring Device And Methods Of Use

An endoscopic bipolar forceps includes an elongated shaft having opposing jaw members at a distal end thereof. The jaw members are movable relative to one another from a first position wherein the jaw members are disposed in spaced relation relative to one another to a second position wherein the jaw members cooperate to grasp tissue therebetween. The forceps also includes a source of electrical energy connected to each jaw member such that the jaw members are capable of conducting energy through tissue held therebetween to effect a seal. A generally tube-like cutter is included which is slidably engaged about the elongated shaft and which is selectively movable about the elongated shaft to engage and cut tissue on at least one side of the jaw members while the tissue is engaged between jaw members.

Vessel sealer and divider

A method and an apparatus to analyze two measured signals that are modeled as containing desired and undesired portions such as noise, FM and AM modulation. Coefficients relate the two signals according to a model defined in accordance with the present invention. In one embodiment, a transformation is used to evaluate a ratio of the two measured signals in order to find appropriate coefficients. The measured signals are then fed into a signal scrubber which uses the coefficients to remove the unwanted portions. The signal scrubbing is performed in either the time domain or in the frequency domain. The method and apparatus are particularly advantageous to blood oximetry and pulserate measurements. In another embodiment, an estimate of the pulserate is obtained by applying a set of rules to a spectral transform of the scrubbed signal. In another embodiment, an estimate of the pulserate is obtained by transforming the scrubbed signal from a first spectral domain into a second spectral domain. The pulserate is found by identifying the largest spectral peak in the second spectral domain.

Signal processing apparatus and method

An electrosurgical system is disclosed. The electrosurgical system includes an electrosurgical generator adapted to supply electrosurgical energy to tissue. The electrosurgical generator includes impedance sensing circuitry which measures impedance of tissue, a microprocessor configured to determine whether a tissue reaction has occurred as a function of a minimum impedance value and a predetermined rise in impedance, wherein tissue reaction corresponds to a boiling point of tissue fluid, and an electrosurgical instrument including at least one active electrode adapted to apply electrosurgical energy to tissue.

Vessel sealing system

The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for transcutaneous measurement of glucose in a host.

Transcutaneous analyte sensor

The present invention involves method and apparatus for analyzing two measured signals that are modeled as containing primary and secondary portions. Coefficients relate the two signals according to a model defined in accordance with the present invention. In one embodiment, the present invention involves utilizing a transformation which evaluates a plurality of possible signal coefficients in order to find appropriate coefficients. Alternatively, the present invention involves using statistical functions or Fourier transform and windowing techniques to determine the coefficients relating to two measured signals. Use of this invention is described in particular detail with respect to blood oximetry measurements.

Signal processing apparatus.

The use of containers in cloud computing has been steadily increasing. With the emergence of Kubernetes, the management of applications inside containers (or pods) is simplified. Kubernetes allows automated actions like self-healing, scaling, rolling back, and updates for the application management. At the same time, security threats have also evolved with attacks on pods to perform malicious actions. Out of several recent malware types, cryptomining has emerged as one of the most serious threats with its hijacking of server resources for cryptocurrency mining. During application deployment and execution in the pod, a cryptomining process, started by a hidden malware executable can be run in the background, and a method to detect malicious cryptomining software running inside Kubernetes pods is needed. One feasible strategy is to use machine learning (ML) to identify and classify pods based on whether or not they contain a running process of cryptomining. In addition to such detection, the system administrator will need an explanation as to the reason(s) of the ML's classification outcome. The explanation will justify and support disruptive administrative decisions such as pod removal or its restart with a new image. In this article, we describe the design and implementation of an ML-based detection system of anomalous pods in a Kubernetes cluster by monitoring Linux-kernel system calls (syscalls). Several types of cryptominers images are used as containers within an anomalous pod, and several ML models are built to detect such pods in the presence of numerous healthy cloud workloads. Explainability is provided using SHAP , LIME , and a novel auto-encoding-based scheme for LSTM models. Seven evaluation metrics are used to compare and contrast the explainable models of the proposed ML cryptomining detection engine.

/pdf/cryptomining-detection-in-container-clouds-using-system-3kzc4famfu.pdf

Cryptomining Detection in Container Clouds Using System Calls and Explainable Machine Learning

EEG-based emotion classifiers have the potential of significantly improving the social integration of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis or the acute stages of Alzheimer's disease. Emotion classifiers have historically used software on general-purpose computers and operating under off-line conditions. Yet the wearability of such classifiers is a must if they are to enable the socialization of critical-care patients. Such wearability requires the use of low-power hardware accelerators that would enable near real-time classification and extended periods of operations. In this article, we architect, design, implement, and test a handcrafted, hardware Convolutional Neural Network, named BioCNN, optimized for EEG-based emotion detection and other bio-medical applications. The EEG signals are generated using a low-cost, off-the-shelf device, namely, Emotiv Epoc+, and then denoised and pre-processed ahead of their use by BioCNN. For training and testing, BioCNN uses three repositories of emotion classification datasets, including the publicly available DEAP and DREAMER datasets, along with an original dataset collected in-house from 5 healthy subjects using standard visual stimuli. A subject-specific training approach is used under TensorFlow to train BioCNN, which is implemented using the Digilent Atlys Board with a low-cost Spartan-6 FPGA. The experimental results show a competitive energy efficiency of 11 GOps/W, a throughput of 1.65 GOps that is in line with the real-time specification of a wearable device, and a latency of less than 1 ms, which is smaller than the 150 ms required for human interaction times. Its emotion inference accuracy is competitive with the top software-based emotion detectors.

/pdf/biocnn-a-hardware-inference-engine-for-eeg-based-emotion-2f5ju8585p.pdf

BioCNN: A Hardware Inference Engine for EEG-Based Emotion Detection

Design, modelling and characterization of comb drive MEMS gap-changeable differential capacitive accelerometer

This paper presents the design of a novel multi-ring vibratory gyroscope. The design incorporates two sets of rings, inner and outer, separated by a set of perforated inertial masses and connected to the outermost anchors by the means of springs. The structure’s performance is enhanced by utilizing springs with bends that play a significant role in reducing the device’s sensitivity to external shocks. The amalgamation of multiple rings with the C-shaped springs provides higher sensitivity with high shock resistance that makes this multi-vibrating ring gyroscope an ideal inertial sensor for space applications. Such a structure yields in-plane flexural modes that are utilized to sense rotation along the axis normal to the structure’s plane.

Ibrahim M. Elfadel

Papers

Signal processing apparatus.

Cryptomining Detection in Container Clouds Using System Calls and Explainable Machine Learning

BioCNN: A Hardware Inference Engine for EEG-Based Emotion Detection

Design, modelling and characterization of comb drive MEMS gap-changeable differential capacitive accelerometer

MEMS multi-vibrating ring gyroscope for space applications