Realization of perfect reconstruction non-uniform filter banks via a tree structure

Obviously, a tree structure filter bank can be realized via a non-uniform filter bank, and perfect reconstruction is achieved if and only if each branch of the tree structure can provide perfect reconstruction. In this paper, the converse of this problem is studied. We show that a perfect reconstruction non-uniform filter bank with decimation ratio {2,4,4} can be realized via a tree structure and each branch of the tree structure achieves perfect reconstruction

Image lag optimisation in a 4T CMOS image sensor for the JANUS camera on ESA's JUICE mission to Jupiter

The CIS115, the imager selected for the JANUS camera on ESA’s JUICE mission to Jupiter, is a Four Transistor (4T) CMOS Image Sensor (CIS) fabricated in a 0.18 µm process. 4T CIS (like the CIS115) transfer photo generated charge collected in the pinned photodiode (PPD) to the sense node (SN) through the Transfer Gate (TG). These regions are held at different potentials and charge is transferred from the potential well under PPD to the potential well under the FD through a voltage pulse applied to the TG. Incomplete transfer of this charge can result in image lag, where signal in previous frames can manifest itself in subsequent frames, often appearing as ghosted images in successive readouts. This can seriously affect image quality in scientific instruments and must be minimised. This is important in the JANUS camera, where image quality is essential to help JUICE meet its scientific objectives. This paper presents two techniques to minimise image lag within the CIS115. An analysis of the optimal voltage for the transfer gate voltage is detailed where optimisation of this TG “ON” voltage has shown to minimise image lag in both an engineering model and gamma and proton irradiated devices. Secondly, a new readout method of the CIS115 is described, where following standard image integration, the PPD is biased to the reset voltage level (VRESET) through the transfer gate to empty charge on the PPD and has shown to reduce image lag in the CIS115

Efficient subband encoding of magnitude/phase spectra

For certain types of signals, perfect reconstruction is not essential. It is sufficient to have knowledge of either the magnitude or the phase spectrum only. Filter banks which introduce phase distortion are used for encoding speech signals. These schemes have an inherent redundancy; i.e., the samples are still transmitted at the sampling rate. It is shown how magnitude or phase information can be encoded at only half the sampling rate in a filter bank formalism

Explainable AI Framework for COVID-19 Prediction in Different Provinces of India

In 2020, covid-19 virus had reached more than 200 countries. Till December 20th 2021, 221 nations in the world had collectively reported 275M confirmed cases of covid-19 & total death toll of 5.37M. Many countries which include United States, India, Brazil, United Kingdom, Russia etc were badly affected by covid-19 pandemic due to the large population. The total confirmed cases reported in this country are 51.7M, 34.7M, 22.2M, 11.3M, 10.2M respectively till December 20, 2021. This pandemic can be controlled with the help of precautionary steps by government & civilians of the country. The early prediction of covid-19 cases helps to track the transmission dynamics & alert the government to take the necessary precautions. Recurrent Deep learning algorithms is a data driven model which plays a key role to capture the patterns present in time series data. In many literatures, the Recurrent Neural Network (RNN) based model are proposed for the efficient prediction of COVID-19 cases for different provinces. The study in the literature doesnt involve the interpretation of the model behavior & robustness. In this study, The LSTM model is proposed for the efficient prediction of active cases in each provinces of India. The active cases dataset for each province in India is taken from John Hopkins publicly available dataset for the duration from 10th June, 2020 to 4th August, 2021. The proposed LSTM model is trained on one state i.e., Maharashtra and tested for rest of the provinces in India. The concept of Explainable AI is involved in this study for the better interpretation & understanding of the model behavior. The proposed model is used to forecast the active cases in India from 16th December, 2021 to 5th March, 2022. It is notated that there will be a emergence of third wave on January, 2022 in India.Comment: 12 page

Effects of binders on stability and palatability of formulated dry compounded diets for spiny lobster Panulirus homarus (Linnaeus, 1758)

Experimental trials were conducted to formulate a palatable dry compounded diet for subadults of the spiny lobster Panulirus homarus in the size range 103-114 mm, using ingredients derived from natural sources in a desirable combination of 54.5% crude protein. Since the diet exhibited low stability, series of experiments were conducted further using different combinations of binders sourced from plant as well as synthetic origin to derive a stable and palatable pellet diet. Among 35 test diets formulated, diet with good palatability and maximum pellet stability (85.55 ± 5.94% for 8 h) were identified as the pellets made with binders in combination of sodium alginate (3%), ‘stick on’ a commercial phytochemical (1%) and agar agar (3%). This combination of binders was found suitable for artificial pelleted diet prepared for subadults of P. homarus

Soft X-ray radiation damage in EM-CCDs used for Resonant Inelastic X-ray Scattering

Advancement in synchrotron and free electron laser facilities means that X-ray beams with higher intensity than ever before are being created. The high brilliance of the X-ray beam, as well as the ability to use a range of X-ray energies, means that they can be used in a wide range of applications. One such application is Resonant Inelastic X-ray Scattering (RIXS). RIXS uses the intense and tuneable X-ray beams in order to investigate the electronic structure of materials. The photons are focused onto a sample material and the scattered X-ray beam is diffracted off a high resolution grating to disperse the X-ray energies onto a position sensitive detector. Whilst several factors affect the total system energy resolution, the performance of RIXS experiments can be limited by the spatial resolution of the detector used. Electron-Multiplying CCDs (EM-CCDs) at high gain in combination with centroiding of the photon charge cloud across several detector pixels can lead to sub-pixel spatial resolution of 2–3 μm. X-ray radiation can cause damage to CCDs through ionisation damage resulting in increases in dark current and/or a shift in flat band voltage. Understanding the effect of radiation damage on EM-CCDs is important in order to predict lifetime as well as the change in performance over time. Two CCD-97s were taken to PTB at BESSY II and irradiated with large doses of soft X-rays in order to probe the front and back surfaces of the device. The dark current was shown to decay over time with two different exponential components to it. This paper will discuss the use of EM-CCDs for readout of RIXS spectrometers, and limitations on spatial resolution, together with any limitations on instrument use which may arise from X-ray-induced radiation damage

Non-linear responsivity characterisation of a CMOS Active Pixel Sensor for high resolution imaging of the Jovian system

The Jovian system is the subject of study for the Jupiter Icy Moon Explorer (JUICE), an ESA mission which is planned to launch in 2022. The scientific payload is designed for both characterisation of the magnetosphere and radiation environment local to the spacecraft, as well as remote characterisation of Jupiter and its satellites. A key instrument on JUICE is the high resolution and wide angle camera, JANUS, whose main science goals include detailed characterisation and study phases of three of the Galilean satellites, Ganymede, Callisto and Europa, as well as studies of other moons, the ring system, and irregular satellites. The CIS115 is a CMOS Active Pixel Sensor from e2v technologies selected for the JANUS camera. It is fabricated using 0.18 μm CMOS imaging sensor process, with an imaging area of 2000 × 1504 pixels, each 7 μm square. A 4T pixel architecture allows for efficient correlated double sampling, improving the readout noise to better than 8 electrons rms, whilst the sensor is operated in a rolling shutter mode, sampling at up to 10 Mpixel/s at each of the four parallel outputs.A primary parameter to characterise for an imaging device is the relationship that converts the sensor's voltage output back to the corresponding number of electrons that were detected in a pixel, known as the Charge to Voltage Factor (CVF). In modern CMOS sensors with small feature sizes, the CVF is known to be non-linear with signal level, therefore a signal-dependent measurement of the CIS115's CVF has been undertaken and is presented here. The CVF is well modelled as a quadratic function leading to a measurement of the maximum charge handling capacity of the CIS115 to be 3.4 × 104 electrons. If the CIS115's response is assumed linear, its CVF is 21.1 electrons per mV (1/47.5 μV per electron)

Proton irradiation of the CIS115 for the JUICE mission

The CIS115 is one of the latest CMOS Imaging Sensors designed by e2v technologies, with 1504x2000 pixels on a 7 μm pitch. Each pixel in the array is a pinned photodiode with a 4T architecture, achieving an average dark current of 22 electrons pixel-1 s-1 at 21°C measured in a front-faced device. The sensor aims for high optical sensitivity by utilising e2v’s back-thinning and processing capabilities, providing a sensitive silicon thickness approximately 9 μm to 12 μm thick with a tuned anti-reflective coating. The sensor operates in a rolling shutter mode incorporating reset level subtraction resulting in a mean pixel readout noise of 4.25 electrons rms. The full well has been measured to be 34000 electrons in a previous study, resulting in a dynamic range of up to 8000. These performance characteristics have led to the CIS115 being chosen for JANUS, the high-resolution and wide-angle optical camera on the JUpiter ICy moon Explorer (JUICE). The three year science phase of JUICE is in the harsh radiation environment of the Jovian magnetosphere, primarily studying Jupiter and its icy moons. Analysis of the expected radiation environment and shielding levels from the spacecraft and instrument design predict the End Of Life (EOL) displacement and ionising damage for the CIS115 to be equivalent to 1010 10 MeV protons cm-2 and 100 krad(Si) respectively. Dark current and image lag characterisation results following initial proton irradiations are presented, detailing the initial phase of space qualification of the CIS115. Results are compared to the pre-irradiation performance and the instrument specifications and further qualification plans are outlined

Deep Learning Approach for Intelligent Intrusion Detection System

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01–0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks