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Planning and resource allocation of a hybrid IoT network using artificial intelligence

A Strengths-Based Human Resource Management Approach to Neurodiversity: A Multi-Actor Qualitative Study

Although the attention for neurodiversity in human resource management (HRM) is growing, neurodivergent individuals are still primarily supported from a deficit-oriented paradigm, which points towards individuals' deviation from neurotypical norms. Following the HRM process model, our study explored to what extent a strengths-based HRM approach to the identification, use, and development of strengths of neurodivergent groups is intended, implemented, and perceived in organizations. Thirty participants were interviewed, including HRM professionals (n=15), supervisors of neurodivergent employees (n=4), and neurodivergent employees (n=11). Our findings show that there is significant potential in embracing the strengths-based approach to promote neurodiversity-inclusion, for instance with the use of job crafting practices or (awareness) training to promote strengths use. Still, the acknowledgement of neurodivergent individuals' strengths in the workplace depends on the integration of the strengths-based approach into a supportive framework of HR practices related to strengths identification, use, and development. Here, particular attention should be dedicated to strengths development for neurodivergent employees (e.g., optimally balancing strengths use). By adopting the strengths-based HRM approach to neurodiversity as a means of challenging the ableist norms of organizations, we add to the HRM literature by contributing to the discussion on how both research and organizations can optimally support an increasingly diverse workforce by focusing on individual strengths

Resource-Efficient Image Buffer Architecture for Neighborhood Processors

Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.