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Enhancing security is currently a paramount concern for society, as traditional surveillance methods necessitate constant vigilance and monitoring of cameras, which can be inadequate. To address this issue, developing an automated security system capable of real-time detection of abnormal human activities and taking appropriate actions is imperative. This paper introduces a novel approach for classifying human actions in controlled environments by combining a support vector machine (SVM) with the deep learning model You Only Look Once (YOLOv7). The YOLOv7 model calculates the boundaries of detected targets, which are then input into the SVM to enhance classification accuracy. The results demonstrate superior classification performance compared to alternative models. In practical terms, the proposed method achieves a testing accuracy 94.24% in classifying human activities based on real-world data. This approach offers promise for preemptively identifying abnormal actions before they occur, paving the way for further advancements in security methods. [ABSTRACT FROM AUTHOR]

Nowadays, research works into the dynamic and static human activities on Smart spaces abounds. Artificial Intelligence (AI) and low cost non-privacy invasive ambient sensors have made this ubiquitous. This review presents a state-of-the-art analysis, performance evaluation, and future research direction. One of the aims of activity recognition (especially that of humans) systems using thermal sensors and AI is the safety of persons in Smart spaces. In a Smart home, human activity detection systems are put in place to ensure the safety of persons in such an environment. This system should have the ability to monitor issues like fall detection, a common home-related accident. In this work, a review of trends in thermal sensor deployment, an appraisal of the popular datasets, AI algorithms, testbeds, and critical challenges of the recent works was provided to direct the research focus. In addition, a summary of AI models and their performance under various sensor resolutions was presented.

With the rapid pace of communication technology, the modern communication system still encounters challenges in meeting the dynamic requirements of users. Facilitating emergency services for patients without a caretaker side by is quite challenging. This work contributes a solution towards state-of-the-art research problems by introducing a novel architecture using collaboration, coordination and user activity detection using contextual information. A prototype is built and experiment is carried out to emphasize the importance of real-time activity-based context awareness in ambient intelligence (AmI) applications. The primary contributions of this work are introduction of novel architecture and usage of both static and dynamic activity-based contextual parameters. The secondary contribution of this model is to integrate ambient intelligence with context awareness to offer higher accuracy in determining the critical condition of a patient. Initially, analytical models are built using the context-based attributes that consider both clinical and non-clinical entities based on the minimal and essential vital information of patient. This paper further discusses the experimental model, which is highly cost-efficient both from an operational and usage viewpoint. Different assessment environments have been used for assessing the performance of the model. [ABSTRACT FROM AUTHOR]

The advancement of various computing technologies such as Artificial Intelligence (AI), cloud computing, mobile computing, and Internet of Things (IoT) is widely applied in several applications. The rapid evolvement of IoT is determined in advance with embedded systems, data analytics, and wireless connectivity. However, the Deep Learning (DL) method is highly applicable to evaluating human activity which is a crucial element to determine the surveillance of human behavior. But the common issue obtained in the detection of human activity is the time series problem and failure to detect accurately in heterogeneous sensors. So a novel Multi-Modal Deep-Learning (MMDL) is proposed to detect human activity accurately with reduced time consumption. However, in real-time applications, huge amounts of data are monitored and the activity of every individual is detected independently by integrating the proposed method with 2D Deep Canonical Correlation Analysis (DCCA). Furthermore, the detection of human activity is employed through IoT devices like multi-modal sensors. The multi-head CNN is used to decrease the cross channels and spatial interactions while training with high-dimensional datasets. The MMDL and binary Support Vector Machine (SVM) architectures overcome the drawbacks of the existing techniques such as the need for increased width/depth for improved performance and the rare usage of hierarchical representations. Thus the effectiveness of the proposed method is validated by STISEN and GAIT datasets. The experimentation results revealed that the proposed method attained a better accuracy of 97% with reduced time in various scenarios related to existing methods. [ABSTRACT FROM AUTHOR]

The recognition of head movements plays an important role in human–computer interface domains. The data collected with image sensors or inertial measurement unit (IMU) sensors are often used for identifying these types of actions. Compared with image processing methods, a recognition system using an IMU sensor has obvious advantages in terms of complexity, processing speed, and cost. In this paper, an IMU sensor is used to collect head movement data on the legs of glasses, and a new approach for recognizing head movements is proposed by combining activity detection and dynamic time warping (DTW). The activity detection of the time series of head movements is essentially based on the different characteristics exhibited by actions and noises. The DTW method estimates the warp path distances between the time series of the actions and the templates by warping under the time axis. Then, the types of head movements are determined by the minimum of these distances. The results show that a 100% accuracy was achieved in the task of classifying six types of head movements. This method provides a new option for head gesture recognition in current human–computer interfaces. [ABSTRACT FROM AUTHOR]

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains. [ABSTRACT FROM AUTHOR]

In health monitoring systems for the elderly, a crucial aspect is unobtrusively and continuously monitoring their activities to detect potentially hazardous incidents such as sudden falls as soon as they occur. However, the effectiveness of current non-contact sensor-based activity detection systems is limited by obstacles present in the environment. To overcome this limitation, a straightforward yet highly efficient approach involves utilizing multiple sensors that collaborate seamlessly. This paper proposes a method that leverages 2D Light Detection and Ranging (Lidar) technology for activity detection. Multiple 2D Lidars are positioned in an indoor environment with varying obstacles such as furniture, working cohesively to create a comprehensive representation of ongoing activities. The data from these Lidars is concatenated and transformed into a more interpretable format, resembling images. A convolutional Long Short-Term Memory (LSTM) Neural Network is then used to process these generated images to classify the activities. The proposed approach achieves high accuracy in three tasks: activity detection, fall detection, and unsteady gait detection. Specifically, it attains accuracies of 96.10%, 99.13%, and 93.13% for these tasks, respectively. This demonstrates the efficacy and promise of the method in effectively monitoring and identifying potentially hazardous events for the elderly through 2D Lidars, which are non-intrusive sensing technology. [ABSTRACT FROM AUTHOR]

Recognizing patient activity in real‐time from video or images collected by a CCTV camera available in the hospital during a Covid‐19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre‐trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre‐trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID‐19 patients as input and looks for matches inside the hold‐on images. [ABSTRACT FROM AUTHOR]

The recognition of head movements plays an important role in human–computer interface domains. The data collected with image sensors or inertial measurement unit (IMU) sensors are often used for identifying these types of actions. Compared with image processing methods, a recognition system using an IMU sensor has obvious advantages in terms of complexity, processing speed, and cost. In this paper, an IMU sensor is used to collect head movement data on the legs of glasses, and a new approach for recognizing head movements is proposed by combining activity detection and dynamic time warping (DTW). The activity detection of the time series of head movements is essentially based on the different characteristics exhibited by actions and noises. The DTW method estimates the warp path distances between the time series of the actions and the templates by warping under the time axis. Then, the types of head movements are determined by the minimum of these distances. The results show that a 100% accuracy was achieved in the task of classifying six types of head movements. This method provides a new option for head gesture recognition in current human–computer interfaces.

Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from 'hospital-centric' services to 'home-centric' services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient's health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited. [ABSTRACT FROM AUTHOR]

With the current market's growing need for electric vehicles and technologies in high-end vehicles, distracted driver detection requires the artificial intelligence's attention. In this paper, new strategies for improving the performance of the driver detection methodology are proposed. The proposed approach consists of two sub-systems namely driver activity detection and driver fatigue detection systems. The former one detects the activities of driver. The latter one is based on the facial feature recognition and determines the driver's fatigue level. The proposed model is evaluated on the activity detection and attained the classification accuracy of 99.69%, compared to the 94.32% accuracy in the state-of-the-art comparison. The KNN classifier had the best accuracy for detecting driver fatigue, with a 76.33% success rate. Experimental results reveal the superiority of proposed model over the existing models. The proposed model can be applied in the real-life environment. [ABSTRACT FROM AUTHOR]

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings’ status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

In health monitoring systems for the elderly, a crucial aspect is unobtrusively and continuously monitoring their activities to detect potentially hazardous incidents such as sudden falls as soon as they occur. However, the effectiveness of current non-contact sensor-based activity detection systems is limited by obstacles present in the environment. To overcome this limitation, a straightforward yet highly efficient approach involves utilizing multiple sensors that collaborate seamlessly. This paper proposes a method that leverages 2D Light Detection and Ranging (Lidar) technology for activity detection. Multiple 2D Lidars are positioned in an indoor environment with varying obstacles such as furniture, working cohesively to create a comprehensive representation of ongoing activities. The data from these Lidars is concatenated and transformed into a more interpretable format, resembling images. A convolutional Long Short-Term Memory (LSTM) Neural Network is then used to process these generated images to classify the activities. The proposed approach achieves high accuracy in three tasks: activity detection, fall detection, and unsteady gait detection. Specifically, it attains accuracies of 96.10%, 99.13%, and 93.13% for these tasks, respectively. This demonstrates the efficacy and promise of the method in effectively monitoring and identifying potentially hazardous events for the elderly through 2D Lidars, which are non-intrusive sensing technology.

Food processing, e.g., freeze-drying, exerts strong pressure on bacteria in the food matrix, decreasing their viability/activity and even forcing them to become viable but unculturable (VBNC), which are often underestimated by traditional plate count. The strict standards of bacterial viability in probiotic products require accurate cell viability/activity enumeration. We developed a staining (5(6)-carboxyfluorescein diacetate succinimide ester, propidium iodide)-based flow cytometry rapid method for detecting the viability/activity of Lacticaseibacillus (Lb.) casei Zhang, a widely used probiotic in the dairy industry in China. We optimized the procedural and instrumental parameters for generating results comparable to that of standard plate counts. This method was also applied to freeze-dried Lb. casei Zhang, yielding 7.7 × 1011 CFU/g, which was non-significantly higher than the results obtained by plate count (6.4 × 1011 CFU/g), possibly due to the detection of VBNC cells in the freeze-dried powder. We anticipated that this method can be used for detecting lactic acid bacteria in other probiotic food/beverages. [ABSTRACT FROM AUTHOR]

Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, among others, as a way to identify such events. Typically, a 2D LIDAR is placed near the ground and collects measurements continuously, and a computational device classifies these measurements. However, in a realistic environment with home furniture, it is hard for such a device to operate as it requires a direct line of sight (LOS) with its target. Furniture will block the infrared (IR) rays from reaching the monitored person thus limiting the effectiveness of such sensors. Nonetheless, due to their fixed location, if a fall is not detected when it happens, it cannot be detected afterwards. In this context, cleaning robots present a much better alternative given their autonomy. In this paper, we propose to use a 2D LIDAR mounted on top of a cleaning robot. Through continuous movement, the robot is able to collect distance information continuously. Despite having the same drawback, by roaming in the room, the robot can identify if a person is laying on the ground after falling, even after a certain period from the fall event. To achieve such a goal, the measurements captured by the moving LIDAR are transformed, interpolated, and compared to a reference state of the surroundings. A convolutional long short-term memory (LSTM) neural network is trained to classify the processed measurements and identify if a fall event occurs or has occurred. Through simulations, we show that such a system can achieve an accuracy equal to 81.2% in fall detection and 99% in the detection of lying bodies. Compared to the conventional method, which uses a static LIDAR, the accuracy reaches for the same tasks 69.4% and 88.6%, respectively. [ABSTRACT FROM AUTHOR]

The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages. [ABSTRACT FROM AUTHOR]

A smartphone can provide a wide range of practical applications and services thanks to its advanced sensing capabilities. However, the sector of Hajj and Umrah, which are rituals performed by millions of pilgrims, still lacks intelligent solutions that can improve the pilgrim experience using these sophisticated capabilities. This research aims to bridge this gap by introducing a solution that applies a real-time monitoring process to different Umrah activities (i.e., Tawaf and Sa’i) using smartphone sensors. In the proposed solution, the smartphone first tracks the pilgrim’s path with the help of inertial sensors, commonly known as the inertial measurement unit (IMU). Then, an algorithm is developed to detect and process the different activities performed by the user and provide helpful instructions accordingly for a comfortable and successful experience. The proposed system was tested and validated using real data for Tawaf and Sa’i activities. The extracted paths were compared with the GPS data for validation. Results showed that the paths were extracted effectively and the algorithm monitored both Tawaf and Sa’i successfully. The deviation between the real path and the extracted path using the proposed algorithm can be enhanced with proper GPS assessment and step-length calibration.

Millimeter-wave (mmWave) radar sensors are a promising modality for gesture recognition as they can overcome several limitations of optic sensors typically used for gesture recognition. These limitations include cost, battery consumption, and privacy concerns. This work focuses on finger level (called micro) gesture recognition using mmWave radar. We propose a set of 6 micro-gestures that are not only intuitive and easy to perform for the user but are distinguishable based on Doppler and angle variation in time. For gesture recognition, we propose an end-to-end solution including an activity detection module (ADM) that automatically segments the data and the gesture classifier (GC) that takes the segmented data and predicts the gesture. Both the ADM and GC are based on machine learning (ML) tools. We evaluate the proposed solution using data collected from 11 users and our proposed solution achieves an end-to-end accuracy of 95%.

Image Incorporation concerns, including background confusion, uneven population distribution, and variations in scale and familiarity, can make group counting difficult. Pre-existing information and multi-level contextual representations are required to handle these problems effectively with deep neural networks and Mask-RCNN. Numerous studies on crowd counting use density maps without segmentation, which treat a group of individuals as a single entity. This article offers a hybrid method for crowd counting that combines Mask-RCNN (MRCNN) and a bidirectional convolutional long-term memory network (ConvLSTM), dubbed (CC: MRCNN-biCLSTM). The CC: MRCNN-biCLSTM is based on the Mask-RCN; it first segments instances and generates density maps, which are passed into adversarial learning during the training phase. Finally, the bidirectional convolutional LSTM is being used to return metrics and counts for individuals within a group of individuals. Following that, the suggested activity detection technique based on the Bayesian non-linear filter AD-BNF is used to identify a person's activity. Additionally, the suggested approach resolves human grouping and enhances metric performance. Extensive studies demonstrate that the suggested method outperforms more sophisticated techniques on four frequently used difficult criteria for density map precision and quality. [ABSTRACT FROM AUTHOR]

Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from ’hospital-centric’ services to ’home-centric’ services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient’s health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited.

The World Health Organization reported that face touching is a primary source of infection transmission of viral diseases, including COVID-19, seasonal Influenza, Swine flu, Ebola virus, etc. Thus, people have been advised to avoid such activity to break the viral transmission chain. However, empirical studies showed that it is either impossible or difficult to avoid as it is unconsciously a human habit. This gives rise to the need to develop means enabling the automatic prediction of the occurrence of such activity. In this paper, we propose SafeSense, a cross-subject face-touch prediction system that combines the sensing capability of smartwatches and smartphones. The system includes innovative modules for automatically labeling the smartwatches’ sensor measurements using smartphones’ proximity sensors during normal phone use. Additionally, SafeSense uses a multi-task learning approach based on autoencoders for learning a subject-invariant representation without any assumptions about the target subjects. SafeSense also improves the deep model’s generalization ability and incorporates different modules to boost the per-subject system’s accuracy and robustness at run-time. We evaluated the proposed system on ten subjects using three different smartwatches and their connected phones. Results show that SafeSense can obtain as high as 97.9% prediction accuracy with a F1-score of 0.98. This outperforms the state-of-the-art techniques in all the considered scenarios without extra data collection overhead. These results highlight the feasibility of the proposed system for boosting public safety. [ABSTRACT FROM AUTHOR]

In uplink (UL) grant-free sparse code multiple access (SCMA) systems, unlike the conventional contention-based transmission, users' activities should be known before data decoding due to sporadic transmission in massive machine-type communication (mMTC). Since compressed sensing (CS) is the theory of sparse signal reconstruction with fewer samples, this theory is a good solution to detect active users. In this paper, we propose the dynamic and sparsity adaptive compressed sensing (DSACS) based active user detection (AUD) and channel estimation (CE) of UL grant-free SCMA. Unlike most of the CS-based methods, sparsity knowledge or potential active user list is not needed in the proposed algorithm, which is already not known in the practical systems. The proposed algorithm adopts a stagewise approach to expand the set of accurate active users for adaptively achieve the sparsity level. It uses the temporal correlation of users' activity to improve performance and reduce complexity. Then, false detected users are eliminated with joint message passing algorithm (JMPA), and channel gains of the accurate active users are estimated again in CE with feedback. The simulation results show that the proposed method without sparsity knowledge is capable of achieving detection in various scenarios in case of sporadic transmission in mMTC.

The use of fixed or scheduled setpoints combined with varying occupancy patterns in buildings could lead to spaces being over or under-conditioned, which may lead to significant waste in energy consumption. The present study aims to develop a vision-based deep learning method for real-time occupancy activity detection and recognition. The method enables predicting and generating real-time heat gain data, which can inform building energy management systems and heating, ventilation, and air-conditioning (HVAC) controls. A faster region-based convolutional neural network was developed, trained and deployed to an artificial intelligence-powered camera. For the initial analysis, an experimental test was performed within a selected case study building's office space. Average detection accuracy of 92.2% was achieved for all activities. Using building energy simulation, the case study building was simulated with both ‘static’ and deep learning influenced profiles to assess the potential energy savings that can be achieved. The work has shown that the proposed approach can better estimate the occupancy internal heat gains for optimising the operations of building HVAC systems.

Bio-nanomicelles based on biomaterials such as nucleic acids, peptides, glycans, and lipids have developed rapidly in the field of bioanalysis. Although DNA and peptides have unique advantages, unfortunately, there are few bio-nanomicelles integrating DNA with peptides. Here, we designed a peptide-DNA hybrid bio-nanomicelle for the activity detection of caspase-3. The detection mechanism is based on caspase-3 specific recognition and cleavage of peptide substrates, which owns high sensitivity and selectivity. Under optimal conditions, the detection of caspase-3 activity can be achieved using our designed bio-nanomicelles and the detection limit is 0.72 nM. Furthermore, the proposed method was also successfully applied for the detection of caspase-3 in cell lysate samples after apoptosis-inducing.

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced $ K $ -means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmW)/Terahertz (THz) wideband communication employing a large-scale antenna array is a promising technique of the sixth-generation (6G) wireless network for realizing massive machine-type communications (mMTC). To reduce the access latency and the signaling overhead, we design a grant-free random access scheme based on joint active device detection and channel estimation (JADCE) for mmW/THz wideband massive access. In particular, by exploiting the simultaneously sparse and low-rank structure of mmW/THz channels with spreads in the delay-angular domain, we propose two multi-rank aware JADCE algorithms via applying the quotient geometry of product of complex rank- $L$ matrices with the number of clusters $L$. It is proved that the proposed algorithms require a smaller number of measurements than the currently known bounds on measurements of conventional simultaneously sparse and low-rank recovery algorithms. Statistical analysis also shows that the proposed algorithms can linearly converge to the ground truth with low computational complexity. Finally, extensive simulation results confirm the superiority of the proposed algorithms in terms of the accuracy of both activity detection and channel estimation. [ABSTRACT FROM AUTHOR]