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This study presents a comprehensive analysis of power consumption in the context of AR applications on the HoloLens 2. As an ablation study, the research systematically dissects various aspects, shedding light on both hardware and software components. The power consumption was examined in diverse scenarios, ranging from the device at idle to running AR applications of increasing complexity. Overall, the study provides insights into the power management aspects associated with Hololens 2 AR devices and applications, offering cues for optimizing energy efficiency and enhancing the user experience. The findings confirm that display-related factors, such as the screen brightness and content complexity, significantly impact power consumption. However, the results reveal that power usage does not scale linearly with the number of objects in an AR scene due to overhead factors and object occlusion. Furthermore, usage scenarios such as lighting conditions and large/minor user motions are analyzed. Moreover, the influence of different development platforms, Unity and Unreal, on power consumption is discussed, highlighting their respective contributions to energy usage. These findings might be useful for researchers, developers, and users of AR technology.

Most of the current image processing methods used in the near-infrared imaging of finger vascular system concentrate on the extraction of internal structures (veins). In this paper, we propose a novel approach which allows to enhance both internal and external features of a finger. The method is based on the Distance Transformation and allows for selective extraction of physiological structures from an observed finger. We evaluate the impact of its parameters on the effectiveness of the already established processing pipeline used for biometric identification. The new method was compared with five state-of-the-art approaches to features extraction (position-gray-profile-curve—PGPGC, maximum curvature points in image profiles—MC, Niblack image adaptive thresholding—NAT, repeated dark line tracking—RDLT, and wide line detector—WD) on the GustoDB database of images obtained in a wide range of NIR wavelengths (730–950 nm). The results indicate a clear superiority of the proposed approach over the remaining alternatives. The method managed to reach over 90 % identification accuracy for all analyzed datasets.

There has recently been intensive development of methods for identification and personal verification using the human finger vascular system (FVS). The primary focus of these efforts has been the increasingly sophisticated methods of image processing, and frequently employing machine learning. In this article, we present a new concept of imaging in which the finger vasculature is illuminated using different wavelengths of light, generating multiple FVS images. We hypothesised that the analysis of these image sets, instead of individual images, could increase the effectiveness of identification. Analyses of data from over 100 volunteers, using five different deterministic methods for feature extraction, consistently demonstrated improved identification efficiency with the addition of data obtained from another wavelength. The best results were seen for combinations of diodes between 800 and 900 nm. Finger vascular system observations outside this range were of marginal utility. The knowledge gained from this experiment can be utilised by designers of biometric recognition devices leveraging FVS technology. Our results confirm that developments in this field are not restricted to image processing algorithms, and that hardware innovations remain relevant.

Most of the current image processing methods used in the near-infrared imaging of fingervascular system concentrate on the extraction of internal structures (veins). In this paper, we proposea novel approach which allows to enhance both internal and external features of a finger. The methodis based on the Distance Transformation and allows for selective extraction of physiological structuresfrom an observed finger. We evaluate the impact of its parameters on the effectiveness of the alreadyestablished processing pipeline used for biometric identification. The new method was comparedwith five state-of-the-art approaches to features extraction (position-gray-profile-curve-PGPGC,maximum curvature points in image profiles-MC, Niblack image adaptive thresholding-NAT,repeated dark line tracking-RDLT, and wide line detector-WD) on the GustoDB database of imagesobtained in a wide range of NIR wavelengths (730-950 nm). The results indicate a clear superiorityof the proposed approach over the remaining alternatives. The method managed to reach over 90%identification accuracy for all analyzed datasets.

SALT, a new dedicated readout Application Specific Integrated Circuit (ASIC) for the Upstream Tracker, a new silicon detector in the Large Hadron Collider beauty (LHCb) experiment, has been designed and developed. It is a 128-channel chip using an innovative architecture comprising a low-power analogue front-end with fast pulse shaping and a 40 MSps 6-bit Analog-to-Digital Converter (ADC) in each channel, followed by a Digital Signal Processing (DSP) block performing pedestal and Mean Common Mode (MCM) subtraction and zero suppression. The prototypes of SALT were fabricated and tested, confirming the full chip functionality and fulfilling the specifications. A signal-to-noise ratio of about 20 is achieved for a silicon sensor with a 12 pF input capacitance. In this paper, the SALT architecture and measurements of the chip performance are presented.

The HKROC ASIC was originally designed to read out the photomultiplier tubes (PMTs) for the Hyper-Kamiokande (HK) experiment. HKROC is a very innovative ASIC capable to read out a large number of channels satisfying stringent requirements in terms of noise, speed and dynamic range. Each HKROC channel features a low-noise preamplifier and shapers, a 10-bit successive approximation Analog-to-Digital Converter (SAR-ADC) for the charge measurement (up to 2500 pC) and a Time-to-Digital Converter (TDC) for the Time-of-Arrival (ToA) measurement with 25 ps binning. HKROC is auto-triggered and includes all necessary ancillary services as bandgap circuit, PLL (Phase-locked loop) and threshold DACs (Digital to Analog Converters). This presentation will describe the ASIC architecture and the experimental results of the last prototype received in January 2022.

The design and measurement results of ultra-low power, fast 10-bit Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) prototypes in 65 nm CMOS technology are presented. Eight prototype ADCs were designed using two different switching schemes of capacitive Digital-to-Analog Converters (DACs), based on MIM or MOM capacitors, and controlled by standard or low-power SAR logic. The layout of each ADC prototype is drawn in 60 um pitch to make it ready for multi-channel implementation. A series of measurements have been made confirming that all prototypes are fully functional, and six of them achieve very good quantitative performance. Five out of eight ADCs show both integral (INL) and differential (DNL) nonlinearity errors below 1 LSB. In dynamic measurements performed at 0.1 Nyquist input frequency, the effective number of bits (ENOB) between 8.9-9.3 was obtained for different ADC prototypes. Standard ADC versions work up to 80-90 MSps with ENOB between 8.9-9.2 bits at the highest sampling rate, while the low-power versions work up to above 50 MSps with ENOB around 9.3 bits at 40 MSps. The power consumption is linear with the sample rate and at 40 MSps it is around 400 uW for the low-power ADCs and just over 500 uW for the standard ADCs. At 80 MSps the standard ADCs consume about 1 mW.

This paper summarizes the physics potential of the CLIC high-energy e+e- linear collider. It provides input to the Snowmass 2013 process for the energy-frontier working groups on The Higgs Boson (HE1), Precision Study of Electroweak Interactions (HE2), Fully Understanding the Top Quark (HE3), as well as The Path Beyond the Standard Model -- New Particles, Forces, and Dimensions (HE4). It is accompanied by a paper describing the CLIC accelerator study, submitted to the Frontier Capabilities group of the Snowmass process.