Development of Stretch Solder Interconnections for Wafer Level Packaging

A wafer level packaging technique has been developed with an inherent advantage of good solder joint co-planarity suitable for wafer level testing. A suitable weak metallization scheme has also been established for the detachment process. During the fabrication process, the compliancy of the solder joint is enhanced through stretching to achieve a small shape factor. Thermal cycling reliability of these hourglass-shaped, stretch solder interconnections has been found to be considerably better than that of the conventional spherical-shaped solder bumps.     

Influence of the prebuckling stress-field on the critical loads of inhomogeneous composite laminates

The paper studies the uniaxial buckling behavior of composite laminates in which preselected variations of fiber spacing in the constituent laminae are adopted. Such laminates are referred to as inhomogeneous laminates because of the variable elastic stiffness along the coordinate axes. A non-uniform prebuckling stress state observed even under constant uniaxial compression has a pronounced influence on the buckling behaviour of an inhomogeneous laminate. A procedure is summarized for computing the critical load of a laminate using the Ritz method which exploits an analogy between the bending and stretching formulations and utilizes Gram-Schmidt orthogonal polynomials. The paper illustrates that the variation in fiber spacing is an innovative way of increasing the critical load for a prescribed amount of fiber and highlights its remarkable sensitivity to the nature of fiber spacing, in-plane and out-of-plane boundary conditions, fiber type and the aspect ratio of the laminate.     

Arthroplasty implant biomaterial particle associated macrophages differentiate into lacunar bone resorbing cells

OBJECTIVE: To study the pathogenesis of aseptic loosening: in particular, to determine whether macrophages responding to particles of biomaterials commonly used in arthroplasty surgery for arthritis are capable of differentiating into osteoclastic bone resorbing cells, and the cellular and hormonal conditions required for this to occur. METHODS: Biomaterial particles (polymethylmethacrylate, high density polyethylene, titanium, chromium-cobalt, stainless steel) were implanted subcutaneously into mice. Macrophages were isolated from the foreign body granulomas that resulted, cultured on bone slices and coverslips, and assessed for both cytochemical and functional evidence of osteoclast differentiation. RESULTS: Tartrate resistant acid phosphatase (TRAP) negative macrophages isolated from granulomas containing particles of all types of biomaterial composition were capable of differentiating into TRAP positive cells capable of extensive lacunar bone resorption (assessed by scanning electron microscopy). The presence of both UMR106 rat osteoblast-like cells and 1,25-dihydroxy vitamin D3 was necessary for this to occur. CONCLUSION: All implant materials produce wear particles that are the focus of a heavy foreign body macrophage response in the fibrous membrane between a loose implant component and the host bone undergoing resorption. These findings underline the importance of biomaterial wear particle generation and the macrophage response to different types of biomaterial wear particles in the pathogenesis of aseptic loosening.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

A unified method for optimal arbitrary pole placement

We consider the classic problem of pole placement by state feedback. We offer an eigenstructure assignment algorithm to obtain a novel parametric form for the pole-placing feedback matrix that can deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. This parametric formula is then exploited to introduce an unconstrained nonlinear optimisation algorithm to obtain a feedback matrix that delivers the desired pole placement with optimal robustness and minimum gain. Lastly we compare the performance of our method against several others from the recent literature.     

IEVCA: An efficient image encryption technique for IoT applications using 2-D Von-Neumann cellular automata

Multimedia Tools and Applications - Present era is marked by exponential growth in transfer of multimedia data through internet. Most of the Internet-of-Things(IoT) applications send images to...     

Entanglement sudden death and birth in qubit–qutrit systems under Dzyaloshinskii–Moriya interaction

We study entanglement dynamics of qubit–qutrit pair under Dzyaloshinskii–Moriya (DM) interaction. The qubit–qutrit pair acts as a closed system and one external qubit serve as the environment for the pair. The external qubit interact with qubit of closed system via DM interaction. This interaction frequently kills the entanglement between qubit–qutrit pair, which is also periodically recovered. On the other hand two parameter class of state of qubit–qutrit pair also affected by DM interaction and one parameter class of state remains unaffected. The frequency of occurrence of entanglement sudden death and entanglement sudden birth in two parameter class of state is half than qubit–qutrit pure state. We used our quantification of entanglement as negativity measure.     

Review of anthropometric considerations for tractor seat design

Tractor driving imposes a lot of physical and mental stress upon the operator. If the operator's seat is not comfortable, his work performance may be poor and there is also a possibility of accidents. The optimal design of tractor seat may be achieved by integrating anthropometric data with other technical features of the design. This paper reviews the existing information on the tractor seat design that considers anthropometry and biomechanical factors and gives an approach for seat design based on anthropometric data. The anthropometric dimensions, i.e. popliteal height sitting (5th percentile), hip breadth sitting (95th percentile), buttock popliteal length (5th percentile), interscye breadth (5th and 95th percentile) and sitting acromion height (5th percentile) of agricultural workers need to be taken into consideration for design of seat height, seat pan width, seat pan length, seat backrest width and seat backrest height, respectively, of a tractor. The seat dimensions recommended for tractor operator's comfort based on anthropometric data of 5434 Indian male agricultural workers were as follows: seat height of 380 mm, seat pan width of 420–450 mm, seat backrest width of 380–400 mm (bottom) and 270–290 mm (top), seat pan length of 370±10 mm, seat pan tilt of 5–7° backward and seat backrest height of 350 mm.

Relevance to industry

The approach presented in this paper for tractor seat design based on anthropometric considerations will help the tractor seat designers to develop and introduce seats suiting to the requirements of the user population. This will not only enhance the comfort of the tractor operators but may also help to reduce the occupational health problems of tractor operators.     

An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases

Intelligent computing system (ICS) and knowledge-based system (KBS) have been widely used in the detection and interpretation of EMG (electromyography) based diseases. Heuristic-based detection methods of EMG parameters for a particular disease have also been reported in the literature but little effort has been made by researchers to combine rule-based reasoning (RBR) and case-based reasoning of KBS, and ANN (artificial neural nets) of ICS. Integrating the methods in KBS and ICS improves the computational and reasoning efficiency of the problem-solving strategy. We have developed an integrated model of CBR and RBR for generating cases, and ANN for matching cases for the interpretation and diagnosis of neuromuscular diseases. We have hierarchically structured the neuromuscular diseases in terms of their physio-pyscho (muscular, cognitive and psychological) parameters and EMG based parameters (amplitude, duration, phase etc.). Cumulative confidence factor is computed at different node from lowest to highest level of hierarchal structure in the process of diagnosis of the neuromuscular diseases. The diseases considered are Duchenne muscular dystrophy, Polymyostits, Endocrine myopathy, Metabolic myopathy, Neuropathy, Poliomyletis and Myasthenia gravis. The basic objective of this work is to develop an integrated model of RBR, CBR and ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute cumulative confidence factor (CCF) of the diseases. CBR is used for diagnosing the neuromuscular diseases and to find the relative importance of sign and symptoms of a diseases to other diseases and ANN is used for matching process in CBR.