Pattern recognition employing spatially variant unconstrained correlation filters

A spatial domain Optimal Trade-off Maximum Average Correlation Height (SPOT-MACH) filter is proposed in this thesis. The proposed technique uses a pre-defined fixed size kernel rather than using estimation techniques. The spatial domain implementation of OT-MACH offers the advantage that it does not have shift invariance imposed on it as the kernel can be modified depending upon its position within the input image. This allows normalization of the kernel and allows inclusion of a space domain non-linearity to improve performance. The proposed SPOT-MACH filter can be used to maximize the height of the correlation peak in the presence of distortions of the training object and provide resistance to background clutter. One of the major characteristics of the SPOT-MACH filter is that it can be tuned to maximize the height and sharpness of the correlation peak by using trade-offs between distortion tolerance, peak sharpness and the ability to suppress clutter noise. A number of non-parametric local regression techniques offer a simplified approach to pattern recognition problems which employ linear filtering using low pass filters designed using moving window local approximations. In most of these cases the algorithms search for a region of interest near the point of estimation for various prevailing conditions which fit the required criteria. These estimates are calculated for a defined window size which is determined as being the largest area within which the estimators do not widely vary from the criteria. The only drawback in this approach is that the window size is directly proportional to the required computational resources and would adversely affect the performance of the system if the moving window size is not proportionate to the resources. The proposed filter employs an optimization technique using low-pass filtering to highlight the potential region of interests in the image and then restricts the movement of the kernel to these regions to allow target identification and to use less computational resources. Also another optimization technique is also proposed which is based on an entropy filter which measures the degree of randomness between two changing scenes and would return the area where change has occurred i.e. the target object might be present. This approach gives a more accurate region of interest than the low-pass filtering approach. Apart from the software based optimization approaches two hardware based enhancement techniques have also been proposed in this thesis. One of the approaches employs Field Programmable Gate Array (FPGA) to perform correlation process employing the inbuilt multipliers and look up tables and the other one uses Graphical Processing Unit (GPU) to do parallel processing of the input scene. Also in this thesis a detailed analysis of SPOT-MACH has been carried out by comparing with popular feature based techniques like Scale Invariant Feature Transform (SIFT) and a comparison matrix has been created. The proposed filter uses a two-staged approach using speed optimizations and then detection of targets from input scenes. Both visible and Forward Looking Infrared (FLIR) imagery data sets have been used to test the performance of filter

Security and Privacy Issues in Medical Internet of Things: Overview, Countermeasures, Challenges and Future Directions

The rapid development and the expansion of Internet of Things (IoT)-powered technologies have strengthened the way we live and the quality of our lives in many ways by combining Internet and communication technologies through its ubiquitous nature. As a novel technological paradigm, this IoT is being served in many application domains including healthcare, surveillance, manufacturing, industrial automation, smart homes, the military, etc. Medical Internet of Things (MIoT), or the use of IoT in healthcare, is becoming a booming trend towards improving the health and wellbeing of billions of people by offering smooth and seamless medical facilities and by enhancing the services provided by medical practitioners, nurses, pharmaceutical companies, and other related government and non-government organizations. In recent times, this MIoT has gained higher attention for its potential to alleviate the massive burden on global healthcare, which has been caused by the rise of chronic diseases, the aging population, and emergency situations such as the recent COVID-19 global pandemic, where many government and non-government medical resources were challenged, owing to the rising demand for medical resources. It is evident that with this recent growing demand for MIoT, the associated technologies and its interconnected, heterogeneous nature adds new concerns as it becomes accessible to confidential patient data, often without patient or the medical staff consciousness, as the security and privacy of MIoT devices and technologies are often overlooked and undermined by relevant stakeholders. Hence, the growing security breaches that target the MIoT in healthcare are making the security and privacy of Medical IoT a crucial topic that is worth scrutinizing. In this study, we examined the current state of security and privacy of the MIoT, which has become of utmost concern among many security experts and researchers due to its rapid demand in recent times. Nevertheless, pertaining to the current state of security and privacy, we also examine and discuss a number of attack use cases, countermeasures and solutions, recent challenges, and anticipated future directions where further attention is required through this study

Assessing Spatial-Temporal Changes in Monetary Values of Urban Ecosystem Services through Remotely Sensed Data

Reckless urbanization in developing regions is leading to the deterioration of the urban environment. The ensuing impacts can place a burden on urban ecology, urban infrastructure, and residents. This scenario requires a combination of avoidance measures and a detailed assessment of the ecological sustainability of the city. While monetary assessments are certainly conceivable, in this study, the contributions of urban environmental infrastructure are weighed financially. Semi-planned (Jhang) and planned (Faisalabad) urban settlements provided the context for this survey. The study uses the Benefit Transfer Method (BTM) to assess changes in the monetary value of urban ecosystem services (UES) from remote sensing data. This finding suggests that urbanization in Pakistan is devouring productive ecological land in urban areas. The assessment shows that between 1989 and 2019, the agricultural area in Faisalabad shrank (−17.38%), and the built-up area increased (16.05%). Likewise, in Jhang City, the built-up area (4.44%) and wasteland (3.10%) swelled. However, during this period (1989–2019), the proportion of agricultural land in Jhang City decreased (−8.93%). As a result, prime areas of UES are falling back into low-return areas. It also found that provisioning ecosystem services (PES) accounted for a significant portion (68.12%) of the UES produced in Faisalabad and Jhang (69.72%), respectively. In contrast, Cultural Ecosystem Services (CES) contributed the smallest share of UES in Faisalabad (1.63%) and Jhang (1.65%). However, the remaining two services, regulatory and support services, made significant contributions. The assessment shows the role of incoherence, inconsistency, resource constraints, and neglect in compromising the urban environmental integrity of these cities. This situation requires a comprehensive assessment and coordinated effort. For this, it is feasible and useful to combine socioeconomic information with land cover data through computerized equipment