Anomaly Behaviour tracing of CHERI-RISC V using Hardware-Software Co-design

Capability Hardware Enhanced RISC Instructions (CHERI) is an extension of conventional ISAs with capabilities enabling fine-grained memory protection. Recently, RISC-V ISA has been extended to CHERI-RISC-V (aka Flute) with additional support for CHERI. In this paper, we have proposed a lightweight continuous monitoring system (CMS) based on hardware-software co-design that communicates with the RISC -V to identify any abnormalities in its operational behaviour. The digital hardware of the functionality of CMS and the CHERI Flute RISC-V has been prototyped in the FPGA. The CMS extracts the different features from RISC-V and transmits them to the processing system via an API. Further, an anomaly detection program is being executed by the ARM processor residing in the PS portion of the ZYNQ. This program enables continuous evaluation of the system operation to spot hardware failure or unusual system behaviour. Finally, the complete design has been prototyped and verified on Zynq FPGA ZC706

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A Novel Approach in WiFi CSI-Based Fall Detection

Falls are unforeseen events, that can potentially be injurious or even fatal, happen either because of health-related issues like weakness, faint ing, abnormal blood pressure, or external factors like slipping and tripping. According to the National Safety Council, falls are one of the leading causes of unintentional injuries accounting for 0.6 million fatal falls every year globally. This necessitates an efficient fall detection mechanism that can automatically detect falls and raise the alarm. Existing methodologies including wireless body-worn sensors do exist for the fall detection task, however at the cost of discomfort arising out of the multiple sensors for the patient wearing it. In this paper, we have explored the usage of the pattern of the data stream from channel state information (CSI) extracted from the Wi-Fi signals received from a single antenna domestic router, which can recognize a human fall activity in real-time and can raise an alarm, without the need of any body-worn sensor. This is done by employing Artificial Intelligence to build deep learning models to classify the extracted features from the CSI data stream. We have designed and implemented predictive deep neural network sequential models like Long Short-Term Memory (LSTM) and lightweight autoencoders for fall detection with extremely promising performance and accuracy ranging between 97 to 99% approximation in three different indoor scenarios with varied layout topology of transmitter–receiver links. This technology can prove to be a lifesaver in situations when a person is unable to raise any medical alert after being fallen. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd

A novel deep neural design and efficient Pipeline architecture for Person Re-Identification in high resolution Video

The primary objective of person re-identification (Re-ID) is to retrieve a person of interest across different nonintersecting cameras for managing in distributed surveillance systems. This has added to its increasing popularity on account of its widespread use, applications and research significance. In this study, we have proposed a novel pipelined deep learning architecture which acts as a robust feature extractor and also helps in reducing down the search space by generating feature embeddings followed by executing a distance metric measurement for finding the similar neighbourhood embeddings and subsequently sorting the cluster centroids of the matching embeddings for finding a set of the nearest match before passing down to a siamese network for similarity checking in the reidentification process. Our experiments reported to achieve a performance accuracy of 85 ∼ 90% with a model size of 288 MB executing at 30 fps in real-tim

Enterprise Class Deep Neural Network Architecture for recognizing objects and faces for surveillance systems

Building security systems is improving at a mammoth rate since the past decade, to cope with the threats of unauthorized access and fraudulent intentions. In high security public places like airports, embassies, corporate offices etc. only facial recognition for verification does not suffice full proof security. To predict the intention of an individual we must capture the objects around the subject/individual and his interactions with them in real time environment with good accuracy besides recognizing them. In this paper we designed an Enterprise Class Deep Neural Network (EcDNN) architecture built on the base architecture of YOLO network. Our proposed multitask learning network architecture recognizes the faces of registered individual as well as objects in the person's vicinity at one shot which achieves significant improvement in performance in terms of speed and model size without loss of precision, if it would have done separately in a cascaded model architecture. Our proposed single network architecture employing multitask learning is achieving state of the art recognition accuracy of 79 mAP at 40 fps with 33 % reduction in model size and an approximately 4x speedup with respect to the benchmark state of the art architectures, validated on standard dataset of PASCAL VOC 2012, FDDB and custom office dataset

Nanomagnetic Logic Design Approach for Area and Speed Efficient Adder using Ferromagnetically Coupled Fixed-Input Majority Gate

In this paper, we introduce the Magnetic Quantum-dot Cellular Automata (MQCA) based area and speed efficient design approach for nanomagnetic full adder implementation. We exploited the physical properties of three input MQCA majority gate, where the fixed input of the majority gate is coupled ferromagnetically to one of the primary input operands. Subsequently, we propose a design methodology, mapping logic and micromagnetic software implementation, validation of the binary full adder architecture built using two-three inputs MQCA majority gates. In addition, we also analyzed our proposed design for switching errors to ensure bit stability and reliability. Our proposed design leads to 36% - 69% reduction in the number of nanomagnets, 50% - 75% reduction in the number of clock cycles and 33% - 50% reduction in the number of majority gate operations required for the binary full adder implementation compared to the state of art designs

Architecture for complex network measures of brain connectivity

Cognitive and motor disorders are growing socio-economic concerns where drug treatments although being the first line of action, are not always effective in restoring cognitive and motor functionality. Research has shown that functional brain connectivity, signifying information exchange among different brain regions, is correlated with efficient execution of cognitive and motor tasks. Hence, to analyze the connectivity parameters in real-time for automated disease prognosis and control, an optimized accelerator/hardware design is required which can be integrated within the sensing device. Here we have designed and implemented an optimized hardware architecture of the graph theoretic parameters (computed concurrently) for the clinically significant functional connectivity measure (Phase Lag Index) of human brain network. To the best of our knowledge, this is a first study on the implementation of the complex network topology parameters of brain connectivity measure which has been synthesized at 25 Mhz, using STMicroelectronics 130-nm technology library and having a dynamic power consumption of 10 nW, making it amenable for real-time high speed operations